diff --git a/.gitignore b/.gitignore
index 70c44cda..14367eda 100644
--- a/.gitignore
+++ b/.gitignore
@@ -80,4 +80,4 @@ dist/
 *.blend[1-9]
 
 ## pytest
-.pytest_cache
\ No newline at end of file
+.pytest_cacheflycheck_*
diff --git a/doc/bibliography/tyssue.bib b/doc/bibliography/tyssue.bib
index 8c6de15a..c6be73b2 100644
--- a/doc/bibliography/tyssue.bib
+++ b/doc/bibliography/tyssue.bib
@@ -21,15 +21,15 @@ @article{aidukasLowcostSubmicronResolution2019
   date = {2019-05-15},
   journaltitle = {Scientific Reports},
   volume = {9},
+  number = {1},
   pages = {7457},
   issn = {2045-2322},
   doi = {10.1038/s41598-019-43845-9},
   url = {https://www.nature.com/articles/s41598-019-43845-9},
   urldate = {2019-05-18},
   abstract = {The revolution in low-cost consumer photography and computation provides fertile opportunity for a disruptive reduction in the cost of biomedical imaging. Conventional approaches to low-cost microscopy are fundamentally restricted, however, to modest field of view (FOV) and/or resolution. We report a low-cost microscopy technique, implemented with a Raspberry Pi single-board computer and color camera combined with Fourier ptychography (FP), to computationally construct 25-megapixel images with sub-micron resolution. New image-construction techniques were developed to enable the use of the low-cost Bayer color sensor, to compensate for the highly aberrated re-used camera lens and to compensate for misalignments associated with the 3D-printed microscope structure. This high ratio of performance to cost is of particular interest to high-throughput microscopy applications, ranging from drug discovery and digital pathology to health screening in low-income countries. 3D models and assembly instructions of our microscope are made available for open source use.},
-  file = {/home/guillaume/Zotero/storage/FCGG7I6P/Aidukas et al. - 2019 - Low-cost, sub-micron resolution, wide-field comput.pdf;/home/guillaume/Zotero/storage/66S87H5C/s41598-019-43845-9.html},
   langid = {english},
-  number = {1}
+  file = {/home/guillaume/Zotero/storage/FCGG7I6P/Aidukas et al. - 2019 - Low-cost, sub-micron resolution, wide-field comput.pdf;/home/guillaume/Zotero/storage/66S87H5C/s41598-019-43845-9.html}
 }
 
 @article{aigouy_suppplemental_2007,
@@ -47,16 +47,16 @@ @article{aigouyCellFlowReorients2010
   date = {2010-09-03},
   journaltitle = {Cell},
   volume = {142},
+  number = {5},
   pages = {773--786},
   issn = {0092-8674},
   doi = {10.1016/j.cell.2010.07.042},
   url = {https://www.sciencedirect.com/science/article/pii/S0092867410008901},
   urldate = {2021-04-15},
   abstract = {Planar cell polarity (PCP) proteins form polarized cortical domains that govern polarity of external structures such as hairs and cilia in both vertebrate and invertebrate epithelia. The mechanisms that globally orient planar polarity are not understood, and are investigated here in the Drosophila wing using a combination of experiment and theory. Planar polarity arises during growth and PCP domains are initially oriented toward the well-characterized organizer regions that control growth and patterning. At pupal stages, the wing hinge contracts, subjecting wing-blade epithelial cells to anisotropic tension in the proximal-distal axis. This results in precise patterns of oriented cell elongation, cell rearrangement and cell division that elongate the blade proximo-distally and realign planar polarity with the proximal-distal axis. Mutation of the atypical Cadherin Dachsous perturbs the global polarity pattern by altering epithelial dynamics. This mechanism utilizes the cellular movements that sculpt tissues to align planar polarity with tissue shape.},
-  file = {/home/guillaume/Zotero/storage/4PT9UVG4/Aigouy et al. - 2010 - Cell Flow Reorients the Axis of Planar Polarity in.pdf;/home/guillaume/Zotero/storage/LW5PW2NQ/S0092867410008901.html},
-  keywords = {DEVBIO},
   langid = {english},
-  number = {5}
+  keywords = {DEVBIO},
+  file = {/home/guillaume/Zotero/storage/4PT9UVG4/Aigouy et al. - 2010 - Cell Flow Reorients the Axis of Planar Polarity in.pdf;/home/guillaume/Zotero/storage/LW5PW2NQ/S0092867410008901.html}
 }
 
 @article{albeckCollectingOrganizingSystematic2006,
@@ -65,12 +65,12 @@ @article{albeckCollectingOrganizingSystematic2006
   date = {2006-11},
   journaltitle = {Nature Reviews Molecular Cell Biology},
   volume = {7},
+  number = {11},
   pages = {803--812},
   issn = {1471-0072, 1471-0080},
   doi = {10.1038/nrm2042},
   url = {http://www.nature.com/doifinder/10.1038/nrm2042},
-  urldate = {2016-11-02},
-  number = {11}
+  urldate = {2016-11-02}
 }
 
 @article{aldridgePhysicochemicalModellingCell2006,
@@ -79,15 +79,15 @@ @article{aldridgePhysicochemicalModellingCell2006
   date = {2006-11},
   journaltitle = {Nat Cell Biol},
   volume = {8},
+  number = {11},
   pages = {1195--1203},
   issn = {1465-7392},
   doi = {10.1038/ncb1497},
   url = {http://www.nature.com/ncb/journal/v8/n11/abs/ncb1497.html},
   urldate = {2016-11-02},
   abstract = {Physicochemical modelling of signal transduction links fundamental chemical and physical principles, prior knowledge about regulatory pathways, and experimental data of various types to create powerful tools for formalizing and extending traditional molecular and cellular biology.},
-  file = {/home/guillaume/Zotero/storage/AG3NTATQ/ncb1497.html},
   langid = {english},
-  number = {11}
+  file = {/home/guillaume/Zotero/storage/AG3NTATQ/ncb1497.html}
 }
 
 @article{aleciNovelCheapMethod2018,
@@ -96,15 +96,15 @@ @article{aleciNovelCheapMethod2018
   date = {2018-10-01},
   journaltitle = {Int Ophthalmol},
   volume = {38},
+  number = {5},
   pages = {2101--2115},
   issn = {1573-2630},
   doi = {10.1007/s10792-017-0709-x},
   url = {https://doi.org/10.1007/s10792-017-0709-x},
   urldate = {2019-03-13},
   abstract = {PurposeTo describe a novel optokinetic visual acuity estimator (Oktotype) and to report the preliminary results obtained in poorly and non-collaborative subjects.MethodsEleven series of symbols arranged horizontally and moving from left to right at a constant rate were displayed. In each sequence, the size of the stimuli was reduced logarithmically. By using this paradigm, the objective visual acuity was computed in 26 normal subjects as the minimum size of the symbols able to evoke the optokinetic response. In the preliminary phase, three contrast levels were tested, with white noise added to the first five sequences so as to normalize the overestimate found at the lower-half range of the acuity scale. Subsequently, the correspondence between subjective and objective visual acuity was compared in 10 poorly collaborative subjects, and the agreement between optokinetic and Teller visual acuity was measured in six non-collaborative subjects.ResultsThe best agreement is provided by the minimum contrast level (20\%) (R 2 = 0.74). The correspondence between the two techniques is satisfying both in the normal and in the poorly collaborative sample (concordance correlation coefficient: 0.85 and 0.83, respectively). In the non-collaborative group, the concordance correlation coefficient between Teller acuity and OKVA ranged between 0.79 (test) and 0.85 (retest). Test–retest reliability was very good for the Oktotype (K: 0.82), and better than the Teller test (K = 0.71), even if it was lower compared to Snellen acuity (K = 0.95).ConclusionThe Oktotype seems promising to predict Snellen visual acuity in normal and poorly collaborative subjects.},
-  keywords = {Agreement,Non-collaborative patients,Oktotype,Optokinetic nystagmus,Teller cards,Test–retest reliability,Visual acuity},
   langid = {english},
-  number = {5}
+  keywords = {Agreement,Non-collaborative patients,Oktotype,Optokinetic nystagmus,Teller cards,Test–retest reliability,Visual acuity}
 }
 
 @article{aleciOptokineticResponseEffective2018,
@@ -117,8 +117,8 @@ @article{aleciOptokineticResponseEffective2018
   url = {https://doi.org/10.1007/s10792-018-1001-4},
   urldate = {2019-03-13},
   abstract = {PurposeTo estimate objective visual acuity in subjects suffering from cataract and age-related macular degeneration via the optokinetic response evoked by a non-conventional induction method (oktotype); in addition, to compare such objective outcome with the subjective acuity based on the ETDRS charts.MethodsPatients were presented with 13 sequences of symbols arranged horizontally to form a serial pattern, moving from left to right at a constant rate. In each sequence, the size of the stimuli was reduced progressively, while the operator checked for the disappearance of the optokinetic response via a small video camera mounted on the test lens frame. The minimum angular size of the serial pattern able to evoke the optokinetic response (MAER) was referred to as the objective visual acuity of the subject.ResultsCorrelation between logMAER and logMAR was significant in the cataract and macular degeneration group (𝑅2catRcat2R\_\{\textbackslash text\{cat\}\}\^\{2\} = 0.70, p {$<$} .0001; 𝑅2AMDRAMD2R\_\{\textbackslash text\{AMD\}\}\^\{2\} = 0.63, p {$<$} .0007). In the two samples, the correspondence between subjective and objective visual acuity (as, respectively, decimal units and arbitrary decimal units) was satisfactory (concordance correlation coefficient: cataract group = 0.91 and AMD group = 0.93). Test–retest reliability of the oktotype was good for the cataract group and moderate for the AMD sample (Κ 0.81 and 0.59, respectively).ConclusionThe oktotype seems a promising tool to objectively assess visual acuity in noncooperating subjects with cataract or macular degeneration. Further research on other clinical conditions is needed to clarify the suitability of the procedure in the clinical setting.},
-  keywords = {Age-related macular degeneration,Cataract,Oktotype,Optokinetic nystagmus,Test–retest reliability,Visual acuity},
-  langid = {english}
+  langid = {english},
+  keywords = {Age-related macular degeneration,Cataract,Oktotype,Optokinetic nystagmus,Test–retest reliability,Visual acuity}
 }
 
 @article{alegotJakStatPathwayInduces2018,
@@ -134,8 +134,8 @@ @article{alegotJakStatPathwayInduces2018
   url = {https://doi.org/10.7554/eLife.32943},
   urldate = {2020-01-15},
   abstract = {Tissue elongation and its control by spatiotemporal signals is a major developmental question. Currently, it is thought that Drosophila ovarian follicular epithelium elongation requires the planar polarization of the basal domain cytoskeleton and of the extra-cellular matrix, associated with a dynamic process of rotation around the anteroposterior axis. Here we show, by careful kinetic analysis of fat2 mutants, that neither basal planar polarization nor rotation is required during a first phase of follicle elongation. Conversely, a JAK-STAT signaling gradient from each follicle pole orients early elongation. JAK-STAT controls apical pulsatile contractions, and Myosin II activity inhibition affects both pulses and early elongation. Early elongation is associated with apical constriction at the poles and with oriented cell rearrangements, but without any visible planar cell polarization of the apical domain. Thus, a morphogen gradient can trigger tissue elongation through a control of cell pulsing and without a planar cell polarity requirement.},
-  file = {/home/guillaume/Zotero/storage/V2DDIQ7R/Alégot et al. - 2018 - Jak-Stat pathway induces Drosophila follicle elong.pdf},
-  keywords = {dynamics,morphogen,morphogenesis}
+  keywords = {dynamics,morphogen,morphogenesis},
+  file = {/home/guillaume/Zotero/storage/V2DDIQ7R/Alégot et al. - 2018 - Jak-Stat pathway induces Drosophila follicle elong.pdf}
 }
 
 @article{aliee_physical_2012,
@@ -144,14 +144,14 @@ @article{aliee_physical_2012
   date = {2012},
   journaltitle = {Current Biology},
   volume = {22},
+  number = {11},
+  eprint = {22560616},
+  eprinttype = {pmid},
   pages = {967--976},
   issn = {09609822},
   doi = {10.1016/j.cub.2012.03.070},
   abstract = {Background: Separating cells with distinct identities and fates by straight and sharp compartment boundaries is important for growth and pattern formation during animal development. The physical mechanisms shaping compartment boundaries, however, are not fully understood. Results: We combine theory and quantitative experiments to investigate the roles of different mechanisms to shape compartment boundaries. Our theoretical work shows that cell elongation created by anisotropic stress, cell proliferation rate, orientation of cell division, and cell bond tension all have distinct effects on the morphology of compartment boundaries during tissue growth. Our experiments using the developing Drosophila wing reveal that the roughness of the dorsoventral compartment boundary is dynamic and that it decreases during development. By measuring tissue relaxation in response to laser ablation of cell bonds at different developmental times, we demonstrate that decreased boundary roughness correlates with increased cell bond tension along the compartment boundary. Finally, by using experimentally determined values for cell bond tension, cell elongation and bias in orientation of cell division in simulations of tissue growth, we can reproduce the main features of the time evolution of the dorsoventral compartment boundary shape. Conclusions: Local increase of cell bond tension along the boundary as well as global anisotropies in the tissue contribute to shaping boundaries in cell networks. We propose a simple scenario that combines time-dependent cell bond tension at the boundary, oriented cell division, and cell elongation in the tissue that can account for the main features of the dynamics of the shape of the dorsoventral compartment boundary. © 2012 Elsevier Ltd.},
-  eprint = {22560616},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/8NGIH63C/Aliee et al. - 2012 - Physical mechanisms shaping the Drosophila dorsoventral compartment boundary.pdf},
-  number = {11}
+  file = {/home/guillaume/Zotero/storage/8NGIH63C/Aliee et al. - 2012 - Physical mechanisms shaping the Drosophila dorsoventral compartment boundary.pdf}
 }
 
 @article{aliee_supplemental_????,
@@ -167,17 +167,17 @@ @article{allenaDiffusionreactionModelDrosophila2013
   date = {2013-03-01},
   journaltitle = {Computer Methods in Biomechanics and Biomedical Engineering},
   volume = {16},
+  number = {3},
+  eprint = {21970322},
+  eprinttype = {pmid},
   pages = {235--248},
   issn = {1025-5842},
   doi = {10.1080/10255842.2011.616944},
   url = {http://dx.doi.org/10.1080/10255842.2011.616944},
   urldate = {2017-03-06},
   abstract = {During the early stages of gastrulation in Drosophila embryo, the epithelial cells composing the single tissue layer of the egg undergo large strains and displacements. These movements have been usually modelled by decomposing the total deformation gradient in an (imposed or strain/stress dependent) active part and a passive response. Although the influence of the chemical and genetic activity in the mechanical response of the cell has been experimentally observed, the effects of the mechanical deformation on the latter have been far less studied, and much less modelled. Here, we propose a model that couples morphogen transport and the cell mechanics during embryogenesis. A diffusion-reaction equation is introduced as an additional mechanical regulator of morphogenesis. Consequently, the active deformations are not directly imposed in the analytical formulation, but they rather depend on the morphogen concentration, which is introduced as a new variable. In this study, we show that strain patterns similar to those observed during biological experiments can be reproduced by properly combining the two phenomena. In addition, we use a novel technique to parameterise the embryo geometry by solving two Laplace problems with specific boundary conditions. We apply the method to two morphogenetic movements: ventral furrow invagination and germ band extension. The matching between our results and the observed experimental deformations confirms that diffusion-reaction of morphogens can actually be controlling large morphogenetic movements.},
-  eprint = {21970322},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/ARWFR5IX/allena2013.pdf;/home/guillaume/Zotero/storage/QC4W7AA4/10255842.2011.html},
   keywords = {cell mechanics,diffusion-reaction,finite element model,Morphogenesis},
-  number = {3}
+  file = {/home/guillaume/Zotero/storage/ARWFR5IX/allena2013.pdf;/home/guillaume/Zotero/storage/QC4W7AA4/10255842.2011.html}
 }
 
 @article{allenaDiffusionreactionModelDrosophila2013a,
@@ -186,17 +186,17 @@ @article{allenaDiffusionreactionModelDrosophila2013a
   date = {2013-03-01},
   journaltitle = {Computer Methods in Biomechanics and Biomedical Engineering},
   volume = {16},
+  number = {3},
+  eprint = {21970322},
+  eprinttype = {pmid},
   pages = {235--248},
   issn = {1025-5842},
   doi = {10.1080/10255842.2011.616944},
   url = {http://dx.doi.org/10.1080/10255842.2011.616944},
   urldate = {2017-03-06},
   abstract = {During the early stages of gastrulation in Drosophila embryo, the epithelial cells composing the single tissue layer of the egg undergo large strains and displacements. These movements have been usually modelled by decomposing the total deformation gradient in an (imposed or strain/stress dependent) active part and a passive response. Although the influence of the chemical and genetic activity in the mechanical response of the cell has been experimentally observed, the effects of the mechanical deformation on the latter have been far less studied, and much less modelled. Here, we propose a model that couples morphogen transport and the cell mechanics during embryogenesis. A diffusion-reaction equation is introduced as an additional mechanical regulator of morphogenesis. Consequently, the active deformations are not directly imposed in the analytical formulation, but they rather depend on the morphogen concentration, which is introduced as a new variable. In this study, we show that strain patterns similar to those observed during biological experiments can be reproduced by properly combining the two phenomena. In addition, we use a novel technique to parameterise the embryo geometry by solving two Laplace problems with specific boundary conditions. We apply the method to two morphogenetic movements: ventral furrow invagination and germ band extension. The matching between our results and the observed experimental deformations confirms that diffusion-reaction of morphogens can actually be controlling large morphogenetic movements.},
-  eprint = {21970322},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/R6WWJWF7/10255842.2011.html},
   keywords = {cell mechanics,diffusion-reaction,finite element model,Morphogenesis},
-  number = {3}
+  file = {/home/guillaume/Zotero/storage/R6WWJWF7/10255842.2011.html}
 }
 
 @article{allenaSimulationMultipleMorphogenetic2010,
@@ -205,14 +205,14 @@ @article{allenaSimulationMultipleMorphogenetic2010
   date = {2010-05-01},
   journaltitle = {Journal of the Mechanical Behavior of Biomedical Materials},
   volume = {3},
+  number = {4},
   pages = {313--323},
   issn = {1751-6161},
   doi = {10.1016/j.jmbbm.2010.01.001},
   url = {http://www.sciencedirect.com/science/article/pii/S1751616110000032},
   urldate = {2017-07-12},
   abstract = {The present work describes a 3D finite element model of the Drosophila embryo designed to simulate three morphogenetic movements during early gastrulation: ventral furrow invagination, cephalic furrow formation and germ band extension. The embryo is represented by a regular ellipsoid and only the mesoderm is modeled. Additionally, the parametric description of the biological structure in a special curvilinear system provides mesh-independent endogenous strains. A deformation gradient decomposition is used to couple an active deformation, specific for each morphogenetic movement, together with a passive deformation, which is due to the response of the continuous mesoderm. Boundary conditions such as the rigid contact with the external vitelline membrane and the yolk pressure are also taken into account. The results suggest that the number of active strains responsible for the morphogenetic events can be less than that deduced from direct experimental observations. Finally, the estimation of the non-local pressures induced during morphogenetic movements is in good agreement with the experimental data.},
-  file = {/home/guillaume/Zotero/storage/CT9QSMXT/S1751616110000032.html},
-  number = {4}
+  file = {/home/guillaume/Zotero/storage/CT9QSMXT/S1751616110000032.html}
 }
 
 @article{altrockMathematicsCancerIntegrating2015,
@@ -222,16 +222,16 @@ @article{altrockMathematicsCancerIntegrating2015
   date = {2015-12},
   journaltitle = {Nat. Rev. Cancer},
   volume = {15},
+  number = {12},
+  eprint = {26597528},
+  eprinttype = {pmid},
   pages = {730--745},
   issn = {1474-1768},
   doi = {10.1038/nrc4029},
   abstract = {Mathematical modelling approaches have become increasingly abundant in cancer research. The complexity of cancer is well suited to quantitative approaches as it provides challenges and opportunities for new developments. In turn, mathematical modelling contributes to cancer research by helping to elucidate mechanisms and by providing quantitative predictions that can be validated. The recent expansion of quantitative models addresses many questions regarding tumour initiation, progression and metastases as well as intra-tumour heterogeneity, treatment responses and resistance. Mathematical models can complement experimental and clinical studies, but also challenge current paradigms, redefine our understanding of mechanisms driving tumorigenesis and shape future research in cancer biology.},
-  eprint = {26597528},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/RP4P73BS/nrc4029.html},
-  keywords = {Animals,Cancer genetics,Cancer microenvironment,Cancer models,Cancer therapy,Computational biology and bioinformatics,Disease Progression,Humans,Metastasis,Models; Biological,Models; Theoretical,Neoplasms,Prognosis},
   langid = {english},
-  number = {12}
+  keywords = {Animals,Cancer genetics,Cancer microenvironment,Cancer models,Cancer therapy,Computational biology and bioinformatics,Disease Progression,Humans,Metastasis,Models; Biological,Models; Theoretical,Neoplasms,Prognosis},
+  file = {/home/guillaume/Zotero/storage/RP4P73BS/nrc4029.html}
 }
 
 @article{anApicalConstrictionDriven2017,
@@ -240,17 +240,17 @@ @article{anApicalConstrictionDriven2017
   date = {2017-06-15},
   journaltitle = {Development},
   volume = {144},
+  number = {12},
+  eprint = {28506995},
+  eprinttype = {pmid},
   pages = {2153--2164},
   issn = {0950-1991, 1477-9129},
   doi = {10.1242/dev.150763},
   url = {http://dev.biologists.org/content/144/12/2153},
   urldate = {2017-06-21},
   abstract = {Skip to Next Section Cell delamination is a conserved morphogenetic process important for the generation of cell diversity and maintenance of tissue homeostasis. Here, we used Drosophila embryonic neuroblasts as a model to study the apical constriction process during cell delamination. We observe dynamic myosin signals both around the cell adherens junctions and underneath the cell apical surface in the neuroectoderm. On the cell apical cortex, the nonjunctional myosin forms flows and pulses, which are termed medial myosin pulses. Quantitative differences in medial myosin pulse intensity and frequency are crucial to distinguish delaminating neuroblasts from their neighbors. Inhibition of medial myosin pulses blocks delamination. The fate of a neuroblast is set apart from that of its neighbors by Notch signaling-mediated lateral inhibition. When we inhibit Notch signaling activity in the embryo, we observe that small clusters of cells undergo apical constriction and display an abnormal apical myosin pattern. Together, these results demonstrate that a contractile actomyosin network across the apical cell surface is organized to drive apical constriction in delaminating neuroblasts.},
-  eprint = {28506995},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/AGA5N4XH/2153.html},
   langid = {english},
-  number = {12}
+  file = {/home/guillaume/Zotero/storage/AGA5N4XH/2153.html}
 }
 
 @article{andreevPracticalGuideStorage2020,
@@ -259,6 +259,7 @@ @article{andreevPracticalGuideStorage2020
   date = {2020-07},
   journaltitle = {Microscopy Today},
   volume = {28},
+  number = {4},
   pages = {42--45},
   publisher = {{Cambridge University Press}},
   issn = {1551-9295, 2150-3583},
@@ -266,10 +267,9 @@ @article{andreevPracticalGuideStorage2020
   url = {https://www.cambridge.org/core/journals/microscopy-today/article/practical-guide-to-storage-of-large-amounts-of-microscopy-data/D3CE39447BFF5BBF9B3ED8A0C35C6F36},
   urldate = {2020-07-29},
   abstract = {, Biological imaging tools continue to increase in speed, scale, and resolution, often resulting in the collection of gigabytes or even terabytes of data in a single experiment. In comparison, the ability of research laboratories to store and manage this data is lagging greatly. This leads to limits on the collection of valuable data and slows data analysis and research progress. Here we review common ways researchers store data and outline the drawbacks and benefits of each method. We also offer a blueprint and budget estimation for a currently deployed data server used to store large datasets from zebrafish brain activity experiments using light-sheet microscopy. Data storage strategy should be carefully considered and different options compared when designing imaging experiments.},
-  file = {/home/guillaume/Zotero/storage/4G4DQA6Q/Andreev et Koo - 2020 - Practical Guide to Storage of Large Amounts of Mic.pdf;/home/guillaume/Zotero/storage/PZMIUU8S/D3CE39447BFF5BBF9B3ED8A0C35C6F36.html},
-  keywords = {big data,data management infrastructure,data workflow,light-sheet microscopy,zebrafish brains},
   langid = {english},
-  number = {4}
+  keywords = {big data,data management infrastructure,data workflow,light-sheet microscopy,zebrafish brains},
+  file = {/home/guillaume/Zotero/storage/4G4DQA6Q/Andreev et Koo - 2020 - Practical Guide to Storage of Large Amounts of Mic.pdf;/home/guillaume/Zotero/storage/PZMIUU8S/D3CE39447BFF5BBF9B3ED8A0C35C6F36.html}
 }
 
 @article{atiaGeometricConstraintsEpithelial2018,
@@ -283,8 +283,8 @@ @article{atiaGeometricConstraintsEpithelial2018
   url = {https://www.nature.com/articles/s41567-018-0089-9},
   urldate = {2018-04-05},
   abstract = {Epithelial cells are shown to scale via a shape distribution that is common to a number of different systems, suggesting that cell shape and shape variability are constrained through a relationship that is purely geometrical.},
-  file = {/home/guillaume/Zotero/storage/NEFCQ5US/s41567-018-0089-9.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/NEFCQ5US/s41567-018-0089-9.html}
 }
 
 @article{baillesGeneticInductionMechanochemical2019,
@@ -298,8 +298,8 @@ @article{baillesGeneticInductionMechanochemical2019
   url = {https://www.nature.com/articles/s41586-019-1492-9},
   urldate = {2019-08-19},
   abstract = {Tissue shape changes in the posterior endoderm of the\&nbsp;early Drosophila embryo are driven by actomyosin contractions emerging from a transcriptional induction followed by a mechanically-driven propagation of RhoI–myosin II activation.},
-  file = {/home/guillaume/Zotero/storage/EUQ4CIWG/s41586-019-1492-9.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/EUQ4CIWG/s41586-019-1492-9.html}
 }
 
 @article{bambardekar_direct_2014,
@@ -307,10 +307,10 @@ @article{bambardekar_direct_2014
   author = {Bambardekar, Kapil and Clément, Raphaël and Blanc, Olivier and Chardès, Claire and Lenne, Pierre-françois},
   date = {2014},
   volume = {112},
+  number = {5},
   pages = {1416--1421},
   doi = {10.1073/pnas.1418732112},
-  file = {/home/guillaume/Zotero/storage/IX2N552I/Bambardekar et al. - 2014 - Direct laser manipulation reveals the mechanics of cell contacts in vivo.pdf},
-  number = {5}
+  file = {/home/guillaume/Zotero/storage/IX2N552I/Bambardekar et al. - 2014 - Direct laser manipulation reveals the mechanics of cell contacts in vivo.pdf}
 }
 
 @article{bartonActiveVertexModel2016,
@@ -323,8 +323,8 @@ @article{bartonActiveVertexModel2016
   url = {http://biorxiv.org/content/early/2016/12/18/095133},
   urldate = {2016-12-20},
   abstract = {We introduce an Active Vertex Model (AVM) for cell-resolution studies of the mechanics of confluent epithelial tissues consisting of tens of thousands of cells, with a level of detail inaccessible to similar methods. The AVM combines the Vertex Model for confluent epithelial tissues with active matter dynamics. This introduces a natural description of the cell motion and accounts for motion patterns observed on multiple scales. Furthermore, cell contacts are generated dynamically from positions of cell centres. This not only enables efficient numerical implementation, but provides a natural description of the T1 transition events responsible for local tissue rearrangements. The AVM also includes cell alignment, cell-specific mechanical properties, cell growth, division and apoptosis. In addition, the AVM introduces a flexible, dynamically changing boundary of the epithelial sheet allowing for studies of phenomena such as the fingering instability or wound healing. We illustrate these capabilities with a number of case studies.},
-  file = {/home/guillaume/Zotero/storage/BZIXCB9F/Barton et al. - 2016 - Active Vertex Model for Cell-Resolution Descriptio.pdf;/home/guillaume/Zotero/storage/84KDUWF9/095133.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/BZIXCB9F/Barton et al. - 2016 - Active Vertex Model for Cell-Resolution Descriptio.pdf;/home/guillaume/Zotero/storage/84KDUWF9/095133.html}
 }
 
 @article{belmonteVirtualtissueComputerSimulations2016,
@@ -333,6 +333,7 @@ @article{belmonteVirtualtissueComputerSimulations2016
   date = {2016-05-18},
   journaltitle = {MBoC},
   volume = {27},
+  number = {22},
   pages = {3673--3685},
   publisher = {{American Society for Cell Biology (mboc)}},
   issn = {1059-1524},
@@ -340,8 +341,7 @@ @article{belmonteVirtualtissueComputerSimulations2016
   url = {https://www.molbiolcell.org/doi/full/10.1091/mbc.E16-01-0059},
   urldate = {2021-01-29},
   abstract = {In autosomal dominant polycystic kidney disease (ADPKD), cysts accumulate and progressively impair renal function. Mutations in PKD1 and PKD2 genes are causally linked to ADPKD, but how these mutations drive cell behaviors that underlie ADPKD pathogenesis is unknown. Human ADPKD cysts frequently express cadherin-8 (cad8), and expression of cad8 ectopically in vitro suffices to initiate cystogenesis. To explore cell behavioral mechanisms of cad8-driven cyst initiation, we developed a virtual-tissue computer model. Our simulations predicted that either reduced cell–cell adhesion or reduced contact inhibition of proliferation triggers cyst induction. To reproduce the full range of cyst morphologies observed in vivo, changes in both cell adhesion and proliferation are required. However, only loss-of-adhesion simulations produced morphologies matching in vitro cad8-induced cysts. Conversely, the saccular cysts described by others arise predominantly by decreased contact inhibition, that is, increased proliferation. In vitro experiments confirmed that cell–cell adhesion was reduced and proliferation was increased by ectopic cad8 expression. We conclude that adhesion loss due to cadherin type switching in ADPKD suffices to drive cystogenesis. Thus, control of cadherin type switching provides a new target for therapeutic intervention.},
-  file = {/home/guillaume/Zotero/storage/CFLMGGB9/Belmonte et al. - 2016 - Virtual-tissue computer simulations define the rol.pdf;/home/guillaume/Zotero/storage/GVT262R7/mbc.html},
-  number = {22}
+  file = {/home/guillaume/Zotero/storage/CFLMGGB9/Belmonte et al. - 2016 - Virtual-tissue computer simulations define the rol.pdf;/home/guillaume/Zotero/storage/GVT262R7/mbc.html}
 }
 
 @article{benazerafMultiscaleQuantificationTissue2017,
@@ -354,8 +354,8 @@ @article{benazerafMultiscaleQuantificationTissue2017
   url = {http://biorxiv.org/content/early/2017/02/10/053124},
   urldate = {2017-03-08},
   abstract = {Embryonic axis extension is a complex multi-tissue morphogenetic process responsible for the formation of the posterior part of the amniote body. Cells located in the caudal part of the embryo divide and rearrange to participate in the elongation of the different embryonic tissues (e.g. neural tube, axial and paraxial mesoderm, lateral plate, ectoderm, endoderm). We previously identified the paraxial mesoderm as a crucial player of axis elongation, but how movements and growth are coordinated between the different posterior tissues to drive morphogenesis remain largely unknown. Here we use the quail embryo as a model system to quantify cell behavior and movements in the various tissues of the elongating embryo. We first quantify the tissue-specific contribution to axis elongation by using 3D volumetric techniques, then quantify tissue-specific parameters such as cell density and proliferation at different embryonic stages. To be able to study cell behavior at a multi-tissue scale we used high-resolution 4D imaging of transgenic quail embryos expressing constitutively expressed fluorescent proteins. We developed specific tracking and image analysis techniques to analyze cell motion and compute tissue deformations in 4D. This analysis reveals extensive sliding between tissues during axis extension. Further quantification of tissue tectonics showed patterns of rotations, contractions and expansions, which are coherent with the multi-tissue behavior observed previously. Our results confirm the central role of the PSM in axis extension; we propose that the PSM specific cell proliferation and migration programs control the coordination of elongation between tissues during axis extension.},
-  file = {/home/guillaume/Zotero/storage/92HXK7IJ/Benazeraf et al. - 2017 - Multiscale quantification of tissue behavior durin.pdf;/home/guillaume/Zotero/storage/STSZTEPX/053124.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/92HXK7IJ/Benazeraf et al. - 2017 - Multiscale quantification of tissue behavior durin.pdf;/home/guillaume/Zotero/storage/STSZTEPX/053124.html}
 }
 
 @article{bergstralh_lateral_2015,
@@ -364,15 +364,15 @@ @article{bergstralh_lateral_2015
   date = {2015-09},
   journaltitle = {Nature cell biology},
   volume = {17},
+  number = {11},
+  eprint = {26414404},
+  eprinttype = {pmid},
   pages = {1497--1503},
   issn = {1476-4679},
   doi = {10.1038/ncb3248},
   url = {http://www.nature.com.gate1.inist.fr/ncb/journal/v17/n11/full/ncb3248.html#affil-auth},
   abstract = {Cells in simple epithelia orient their mitotic spindles in the plane of the epithelium so that both daughter cells are born within the epithelial sheet. This is assumed to be important to maintain epithelial integrity and prevent hyperplasia, because misaligned divisions give rise to cells outside the epithelium. Here we test this assumption in three types of Drosophila epithelium; the cuboidal follicle epithelium, the columnar early embryonic ectoderm, and the pseudostratified neuroepithelium. Ectopic expression of Inscuteable in these tissues reorients mitotic spindles, resulting in one daughter cell being born outside the epithelial layer. Live imaging reveals that these misplaced cells reintegrate into the tissue. Reducing the levels of the lateral homophilic adhesion molecules Neuroglian or Fasciclin 2 disrupts reintegration, giving rise to extra-epithelial cells, whereas disruption of adherens junctions has no effect. Thus, the reinsertion of misplaced cells seems to be driven by lateral adhesion, which pulls cells born outside the epithelial layer back into it. Our findings reveal a robust mechanism that protects epithelia against the consequences of misoriented divisions.},
-  eprint = {26414404},
-  eprinttype = {pmid},
-  langid = {english},
-  number = {11}
+  langid = {english}
 }
 
 @article{biDensityindependentRigidityTransition2015,
@@ -393,8 +393,8 @@ @article{bielmeier_interface_2016
   volume = {26},
   pages = {563--574},
   doi = {10.1016/j.cub.2015.12.063},
-  file = {/home/guillaume/Zotero/storage/V9C8QWU8/Bielmeier et al. - 2016 - Interface Contractility between Differently Fated Cells Drives Cell Elimination and Cyst Formation.pdf},
-  keywords = {actomyosin contractility,apoptosis,cell elimination,continuum mechanics,epithelial cyst,epithelium,physical modeling,tissue patterning,vertex model}
+  keywords = {actomyosin contractility,apoptosis,cell elimination,continuum mechanics,epithelial cyst,epithelium,physical modeling,tissue patterning,vertex model},
+  file = {/home/guillaume/Zotero/storage/V9C8QWU8/Bielmeier et al. - 2016 - Interface Contractility between Differently Fated Cells Drives Cell Elimination and Cyst Formation.pdf}
 }
 
 @article{blanchard_tissue_2009,
@@ -403,15 +403,15 @@ @article{blanchard_tissue_2009
   date = {2009},
   journaltitle = {Nature methods},
   volume = {6},
+  number = {6},
+  eprint = {19412170},
+  eprinttype = {pmid},
   pages = {458--464},
   issn = {1548-7091},
   doi = {10.1038/nmeth.1327},
   url = {http://dx.doi.org/10.1038/nmeth.1327},
   abstract = {The dynamic reshaping of tissues during morphogenesis results from a combination of individual cell behaviors and collective cell rearrangements. However, a comprehensive framework to unambiguously measure and link cell behavior to tissue morphogenesis is lacking. Here we introduce such a kinematic framework, bridging cell and tissue behaviors at an intermediate, mesoscopic, level of cell clusters or domains. By measuring domain deformation in terms of the relative motion of cell positions and the evolution of their shapes, we characterized the basic invariant quantities that measure fundamental classes of cell behavior, namely tensorial rates of cell shape change and cell intercalation. In doing so we introduce an explicit definition of cell intercalation as a continuous process. We mapped strain rates spatiotemporally in three models of tissue morphogenesis, gaining insight into morphogenetic mechanisms. Our quantitative approach has broad relevance for the precise characterization and comparison of morphogenetic phenotypes.},
-  eprint = {19412170},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/MN4SXG7V/Blanchard et al. - 2009 - Tissue tectonics morphogenetic strain rates, cell shape change and intercalation.pdf},
-  number = {6}
+  file = {/home/guillaume/Zotero/storage/MN4SXG7V/Blanchard et al. - 2009 - Tissue tectonics morphogenetic strain rates, cell shape change and intercalation.pdf}
 }
 
 @article{blanchardPulsatileApicomedialContractility2018,
@@ -419,6 +419,7 @@ @article{blanchardPulsatileApicomedialContractility2018
   author = {Blanchard, Guy B and Étienne, Jocelyn and Gorfinkiel, Nicole},
   date = {2018-08-01},
   journaltitle = {Current Opinion in Genetics \& Development},
+  series = {Developmental Mechanisms, Patterning and Evolution},
   volume = {51},
   pages = {78--87},
   issn = {0959-437X},
@@ -426,8 +427,7 @@ @article{blanchardPulsatileApicomedialContractility2018
   url = {http://www.sciencedirect.com/science/article/pii/S0959437X18300108},
   urldate = {2018-08-01},
   abstract = {We review recent developments in the understanding of the biomechanics of apicomedial actomyosin and how its contractility can tense and deform tissue. Myosin pulses are driven by a biochemical oscillator but how they are modulated by the mechanical context remains unclear. On the other hand, the emergence of tissue behaviour is highly dependent on the material properties of actin, on how strongly components are connected and on the influence of neighbouring tissues. We further review the use of constitutive equations in exploring the mechanics of epithelial apices dominated by apicomedial Myosin contractility.},
-  file = {/home/guillaume/Zotero/storage/UKTF49SK/Blanchard et al. - 2018 - From pulsatile apicomedial contractility to effect.pdf;/home/guillaume/Zotero/storage/XA28RNIE/S0959437X18300108.html},
-  series = {Developmental Mechanisms, Patterning and Evolution}
+  file = {/home/guillaume/Zotero/storage/UKTF49SK/Blanchard et al. - 2018 - From pulsatile apicomedial contractility to effect.pdf;/home/guillaume/Zotero/storage/XA28RNIE/S0959437X18300108.html}
 }
 
 @article{blanchardTakingStrainQuantifying2017,
@@ -437,17 +437,17 @@ @article{blanchardTakingStrainQuantifying2017
   date = {2017-05-19},
   journaltitle = {Phil. Trans. R. Soc. B},
   volume = {372},
+  number = {1720},
+  eprint = {28348250},
+  eprinttype = {pmid},
   pages = {20150513},
   issn = {0962-8436, 1471-2970},
   doi = {10.1098/rstb.2015.0513},
   url = {http://rstb.royalsocietypublishing.org/content/372/1720/20150513},
   urldate = {2017-04-04},
   abstract = {Computer-assisted tracking of the shapes of many cells over long periods of development has driven the exploration of novel ways to quantify the contributions of different cell behaviours to morphogenesis. A handful of similar methods have now been published that are used to calculate tissue deformations (strain rates) in epithelia. These methods are further used to quantify strain rates attributable to each of the cell behaviours in the tissue, such as cell shape change, cell rearrangement and cell division, that together sum to the tissue strain rates. In this review, aimed at developmental biologists, I will introduce the general approach, characterize differences in current approaches and highlight extensions of these methods that remain to be fully explored. The methods will make a major contribution to the emerging field of tissue mechanics. Precisely quantified strain rates are an essential first step towards exploring constitutive equations relating stress to strain via tissue mechanical properties. This article is part of the themed issue ‘Systems morphodynamics: understanding the development of tissue hardware’.},
-  eprint = {28348250},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/2SRHQUUP/20150513.html},
   langid = {english},
-  number = {1720}
+  file = {/home/guillaume/Zotero/storage/2SRHQUUP/20150513.html}
 }
 
 @article{bonfantiUnifiedRheologicalModel2019,
@@ -460,8 +460,8 @@ @article{bonfantiUnifiedRheologicalModel2019
   url = {https://www.biorxiv.org/content/10.1101/543330v1},
   urldate = {2019-02-13},
   abstract = {{$<$}p{$>$}The mechanical response of single cells and tissues exhibits a broad distribution of time scales that gives often rise to a distinctive power-law regime. Such complex behaviour cannot be easily captured by traditional rheological approaches, making material characterisation and predictive modelling very challenging. Here, we present a novel model combining conventional viscoelastic elements with fractional calculus that successfully captures the macroscopic relaxation response of epithelial monolayers. The parameters extracted from the fitting of the relaxation modulus allow prediction of the response of the same material to slow stretch and creep, indicating that the model captured intrinsic material properties. Two characteristic times can be derived from the model parameters, and together these explain different qualitative behaviours observed in creep after genetic and chemical treatments. We compared the response of tissues with the behaviour of single cells as well as intra-cellular and extra-cellular components, and linked the power-law behaviour of the epithelium to the dynamics of the cell cortex. Such a unified model for the mechanical response of biological materials provides a novel and robust mathematical approach for diagnostic methods based on mechanical traits as well as more accurate computational models of tissues mechanics.{$<$}/p{$>$}},
-  file = {/home/guillaume/Zotero/storage/SSYETMEB/543330v1.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/SSYETMEB/543330v1.html}
 }
 
 @article{bosveld_epithelial_2016,
@@ -481,17 +481,17 @@ @article{brezavscekModelEpithelialInvagination2012
   date = {2012-09-05},
   journaltitle = {Biophysical Journal},
   volume = {103},
+  number = {5},
+  eprint = {23009857},
+  eprinttype = {pmid},
   pages = {1069--1077},
   issn = {0006-3495},
   doi = {10.1016/j.bpj.2012.07.018},
   url = {http://www.cell.com/biophysj/abstract/S0006-3495(12)00793-X},
   urldate = {2018-03-12},
   abstract = {We propose a 2D mechanical model of a tubular epithelium resembling the early Drosophila embryo. The model consists of a single layer of identical cells with energy associated with the tension of cell cortex. Depending on the relative tension of the apical, basal, and lateral sides of the cells, tissue thickness, and the degree of external constraint, the minimal-energy states of the epithelial cross section include circular shapes as well as a range of inward-buckled shapes. Some of the solutions are characterized by a single deep groove, which shows that an epithelium consisting of cells of identical mechanical properties can infold. This is consistent with what is seen in embryos of certain Drosophila mutants. To ensure that the infolding occurs at a predetermined section of the epithelium, we extend the model by increasing the cross-sectional area of a subset of cells, which is consistent with observations in wild-type embryos. This variation of cell parameters across the epithelium is sufficient to make it fold at a specific site. The model explores previously untested minimal conditions for tissue invagination and is devoid of specificity needed to accurately describe an in~vivo situation in Drosophila.},
-  eprint = {23009857},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/5V8W2TEL/Brezavšček et al. - 2012 - A Model of Epithelial Invagination Driven by Colle.pdf;/home/guillaume/Zotero/storage/G4CDNLFB/S0006-3495(12)00793-X.html},
   langid = {english},
-  number = {5}
+  file = {/home/guillaume/Zotero/storage/5V8W2TEL/Brezavšček et al. - 2012 - A Model of Epithelial Invagination Driven by Colle.pdf;/home/guillaume/Zotero/storage/G4CDNLFB/S0006-3495(12)00793-X.html}
 }
 
 @online{Build246DamCB,
@@ -511,8 +511,8 @@ @article{carterPavementCellsTopology2017
   url = {http://www.biorxiv.org/content/early/2017/07/07/160762},
   urldate = {2017-07-10},
   abstract = {{$<$}p{$>$}D9Arcy Thompson emphasised the importance of surface tension as a potential driving force in establishing cell shape and topology within tissues. Leaf epidermal pavement cells grow into jigsaw-piece shapes, highly deviating from such classical forms. We investigate the topology of developing Arabidopsis leaves composed solely of pavement cells. Image analysis of around 50,000 cells reveals a clear and unique topological signature, deviating from previously studied epidermal tissues. This topological distribution is however established early during leaf development, already before the typical pavement cell shapes emerge, with topological homeostasis maintained throughout growth and unaltered between division and maturation zones. Simulating graph models, we identify a heuristic cellular division rule that reproduces the observed topology. Our parsimonious model predicts how and when cells effectively place their division plane with respect to their neighbours. We verify the predicted dynamics through in vivo tracking of 800 mitotic events, and conclude that the distinct topology is not a direct consequence of the jigsaw-like shape of the cells, but rather owes itself to a strongly life-history-driven process, with limited impact from cell surface mechanics.{$<$}/p{$>$}},
-  file = {/home/guillaume/Zotero/storage/F3FABDJW/Carter et al. - 2017 - Pavement cells and the topology puzzle.pdf;/home/guillaume/Zotero/storage/JPMVQN9R/160762.full.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/F3FABDJW/Carter et al. - 2017 - Pavement cells and the topology puzzle.pdf;/home/guillaume/Zotero/storage/JPMVQN9R/160762.full.html}
 }
 
 @online{centerforhistoryandnewmediaGuideRapidePour,
@@ -533,15 +533,16 @@ @article{chanPatternedCorticalTension2017
   url = {https://elifesciences.org/content/6/e22796v1},
   urldate = {2017-05-26},
   abstract = {N-cadherin at heterotypic contacts controls the level and asymmetric localisation of Myosin-II motor, thereby influencing cell shapes and cell packing.},
-  file = {/home/guillaume/Zotero/storage/RQZCM29J/Chan et al. - 2017 - Patterned cortical tension mediated by N-cadherin .pdf;/home/guillaume/Zotero/storage/KEF75PC5/e22796.html},
+  langid = {english},
   keywords = {<italic>D. melanogaster</italic>,cell adhesion,cell contractility,cell mechanics,cell shapes,modelling,Morphogenesis},
-  langid = {english}
+  file = {/home/guillaume/Zotero/storage/RQZCM29J/Chan et al. - 2017 - Patterned cortical tension mediated by N-cadherin .pdf;/home/guillaume/Zotero/storage/KEF75PC5/e22796.html}
 }
 
 @article{chaouiyaSBMLQualitativeModels2013,
   title = {{{SBML}} Qualitative Models: A Model Representation Format and Infrastructure to Foster Interactions between Qualitative Modelling Formalisms and Tools},
   shorttitle = {{{SBML}} Qualitative Models},
   author = {Chaouiya, Claudine and Bérenguier, Duncan and Keating, Sarah M. and Naldi, Aurélien and van Iersel, Martijn P. and Rodriguez, Nicolas and Dräger, Andreas and Büchel, Finja and Cokelaer, Thomas and Kowal, Bryan and Wicks, Benjamin and Gonçalves, Emanuel and Dorier, Julien and Page, Michel and Monteiro, Pedro T. and von Kamp, Axel and Xenarios, Ioannis and de Jong, Hidde and Hucka, Michael and Klamt, Steffen and Thieffry, Denis and Le Novère, Nicolas and Saez-Rodriguez, Julio and Helikar, Tomáš},
+  options = {useprefix=true},
   date = {2013},
   journaltitle = {BMC Systems Biology},
   volume = {7},
@@ -551,8 +552,7 @@ @article{chaouiyaSBMLQualitativeModels2013
   url = {http://dx.doi.org/10.1186/1752-0509-7-135},
   urldate = {2016-11-02},
   abstract = {Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing.},
-  file = {/home/guillaume/Zotero/storage/I6ACIJU5/Chaouiya et al. - 2013 - SBML qualitative models a model representation fo.pdf;/home/guillaume/Zotero/storage/SGSHK45P/1752-0509-7-135.html},
-  options = {useprefix=true}
+  file = {/home/guillaume/Zotero/storage/I6ACIJU5/Chaouiya et al. - 2013 - SBML qualitative models a model representation fo.pdf;/home/guillaume/Zotero/storage/SGSHK45P/1752-0509-7-135.html}
 }
 
 @article{cheddadi_mod_2010,
@@ -567,15 +567,15 @@ @article{chenExtracellularMatrixStiffness2019
   date = {2019-07-26},
   journaltitle = {Nat Commun},
   volume = {10},
+  number = {1},
   pages = {1--15},
   issn = {2041-1723},
   doi = {10.1038/s41467-019-10874-x},
   url = {https://www.nature.com/articles/s41467-019-10874-x},
   urldate = {2019-08-30},
   abstract = {The extracellular matrix can shape developing organs, but how external forces direct intercellular morphogenesis is unclear. Here, the authors use 3D imaging to show that elongation of the Drosophila egg chamber involves polarized cell reorientation signalled by changes in stiffness of the surrounding extracellular matrix.},
-  file = {/home/guillaume/Zotero/storage/7PIYA6CG/Chen et al. - 2019 - Extracellular matrix stiffness cues junctional rem.pdf;/home/guillaume/Zotero/storage/LPS26KHN/s41467-019-10874-x.html},
   langid = {english},
-  number = {1}
+  file = {/home/guillaume/Zotero/storage/7PIYA6CG/Chen et al. - 2019 - Extracellular matrix stiffness cues junctional rem.pdf;/home/guillaume/Zotero/storage/LPS26KHN/s41467-019-10874-x.html}
 }
 
 @article{chenMechanicalForcesCell2018,
@@ -584,15 +584,15 @@ @article{chenMechanicalForcesCell2018
   date = {2018-12-15},
   journaltitle = {J Cell Sci},
   volume = {131},
+  number = {24},
   pages = {jcs218156},
   issn = {0021-9533, 1477-9137},
   doi = {10.1242/jcs.218156},
   url = {http://jcs.biologists.org/content/131/24/jcs218156},
   urldate = {2018-12-20},
   abstract = {Skip to Next Section In various physiological processes, the cell collective is organized in a monolayer, such as seen in a simple epithelium. The advances in the understanding of mechanical behavior of the monolayer and its underlying cellular and molecular mechanisms will help to elucidate the properties of cell collectives. In this Review, we discuss recent in vitro studies on monolayer mechanics and their implications on collective dynamics, regulation of monolayer mechanics by physical confinement and geometrical cues and the effect of tissue mechanics on biological processes, such as cell division and extrusion. In particular, we focus on the active nematic property of cell monolayers and the emerging approach to view biological systems in the light of liquid crystal theory. We also highlight the mechanosensing and mechanotransduction mechanisms at the sub-cellular and molecular level that are mediated by the contractile actomyosin cytoskeleton and cell–cell adhesion proteins, such as E-cadherin and α-catenin. To conclude, we argue that, in order to have a holistic understanding of the cellular response to biophysical environments, interdisciplinary approaches and multiple techniques – from large-scale traction force measurements to molecular force protein sensors – must be employed.},
-  file = {/home/guillaume/Zotero/storage/QSQ8XEWU/10.1242@jcs.218156.pdf;/home/guillaume/Zotero/storage/RUJWGBJG/jcs218156.html},
   langid = {english},
-  number = {24}
+  file = {/home/guillaume/Zotero/storage/QSQ8XEWU/10.1242@jcs.218156.pdf;/home/guillaume/Zotero/storage/RUJWGBJG/jcs218156.html}
 }
 
 @article{christiansenSilicoLabelingPredicting2018,
@@ -602,18 +602,18 @@ @article{christiansenSilicoLabelingPredicting2018
   date = {2018-04-19},
   journaltitle = {Cell},
   volume = {173},
+  number = {3},
+  eprint = {29656897},
+  eprinttype = {pmid},
   pages = {792-803.e19},
   issn = {0092-8674, 1097-4172},
   doi = {10.1016/j.cell.2018.03.040},
   url = {https://www.cell.com/cell/abstract/S0092-8674(18)30364-7},
   urldate = {2018-04-26},
   abstract = {Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call “in silico labeling” (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire.},
-  eprint = {29656897},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/B994UQ82/S0092-8674(18)30364-7.html;/home/guillaume/Zotero/storage/ZJPN3QXA/S0092-8674(18)30364-7.html},
-  keywords = {cancer,computer vision,deep learning,machine learning,microscopy,neuroscience,stem cells},
   langid = {english},
-  number = {3}
+  keywords = {cancer,computer vision,deep learning,machine learning,microscopy,neuroscience,stem cells},
+  file = {/home/guillaume/Zotero/storage/B994UQ82/S0092-8674(18)30364-7.html;/home/guillaume/Zotero/storage/ZJPN3QXA/S0092-8674(18)30364-7.html}
 }
 
 @article{civelekoglu-scholeyModelChromosomeMotility2006,
@@ -623,31 +623,31 @@ @article{civelekoglu-scholeyModelChromosomeMotility2006
   date = {2006-06-01},
   journaltitle = {Biophysical Journal},
   volume = {90},
+  number = {11},
+  eprint = {16533843},
+  eprinttype = {pmid},
   pages = {3966--3982},
   issn = {0006-3495},
   doi = {10.1529/biophysj.105.078691},
   url = {https://www.cell.com/biophysj/abstract/S0006-3495(06)72579-6},
   urldate = {2019-09-02},
-  eprint = {16533843},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/B82U2YHW/Civelekoglu-Scholey et al. - 2006 - Model of Chromosome Motility in Drosophila Embryos.pdf;/home/guillaume/Zotero/storage/INBB7I74/S0006-3495(06)72579-6.html},
-  keywords = {electron microscopy,EM,fluorescence recovery after photobleaching,FRAP,interpolar microtubule,ipMT,kinetochore,kinetochore microtubule,kMT,kt,microtubule,MT},
   langid = {english},
-  number = {11}
+  keywords = {electron microscopy,EM,fluorescence recovery after photobleaching,FRAP,interpolar microtubule,ipMT,kinetochore,kinetochore microtubule,kMT,kt,microtubule,MT},
+  file = {/home/guillaume/Zotero/storage/B82U2YHW/Civelekoglu-Scholey et al. - 2006 - Model of Chromosome Motility in Drosophila Embryos.pdf;/home/guillaume/Zotero/storage/INBB7I74/S0006-3495(06)72579-6.html}
 }
 
 @article{clevers_modeling_2016,
   title = {Modeling {{Development}} and {{Disease}} with {{Organoids}}},
   author = {Clevers, Hans and Antonica, F. and Kasprzyk, D.F. and Opitz, R. and Iacovino, M. and Liao, X.H. and Dumitrescu, A.M. and Refetoff, S. and Peremans, K. and Manto, M. and Kyba, M. and Costagliola, S. and Barkauskas, C.E. and Cronce, M.J. and Rackley, C.R. and Bowie, E.J. and Keene, D.R. and Stripp, B.R. and Randell, S.H. and Noble, P.W. and Hogan, B.L. and Barker, N. and van Es, J.H. and Kuipers, J. and Kujala, P. and van den Born, M. and Cozijnsen, M. and Haegebarth, A. and Korving, J. and Begthel, H. and Peters, P.J. and Clevers, H. and Barker, N. and Huch, M. and Kujala, P. and van de Wetering, M. and Snippert, H.J. and van Es, J.H. and Sato, T. and Stange, D.E. and Begthel, H. and van den Born, M. and Al, et and Bartfeld, S. and Bayram, T. and van de Wetering, M. and Huch, M. and Begthel, H. and Kujala, P. and Vries, R. and Peters, P.J. and Clevers, H. and Boj, S.F. and Hwang, C.I. and Baker, L.A. and Chio, I.I. and Engle, D.D. and Corbo, V. and Jager, M. and Ponz-Sarvise, M. and Tiriac, H. and Spector, M.S. and Al, et and Camp, J.G. and Badsha, F. and Florio, M. and Kanton, S. and Gerber, T. and Wilsch-Bräuninger, M. and Lewitus, E. and Sykes, A. and Hevers, W. and Lancaster, M. and Al, et and Chen, K.G. and Mallon, B.S. and McKay, R.D. and Robey, P.G. and Cherry, A.B. and Daley, G.Q. and Chua, C.W. and Shibata, M. and Lei, M. and Toivanen, R. and Barlow, L.J. and Bergren, S.K. and Badani, K.K. and McKiernan, J.M. and Benson, M.C. and Hibshoosh, H. and Al, et and Ciancanelli, M.J. and Huang, S.X. and Luthra, P. and Garner, H. and Itan, Y. and Volpi, S. and Lafaille, F.G. and Trouillet, C. and Schmolke, M. and Albrecht, R.A. and Al, et and Clevers, H. and Clevers, H. and Clevers, H. and Loh, K.M. and Nusse, R. and Dekkers, F.e.a. and Dekkers, J.F. and Wiegerinck, C.L. and de Jonge, H.R. and Bronsveld, I. and Janssens, H.M. and Groot, K.M. de Winter-de and Brandsma, A.M. and de Jong, N.W. and Bijvelds, M.J. and Scholte, B.J. and Al, et and Desai, T.J. and Brownfield, D.G. and Krasnow, M.A. and DeWard, A.D. and Cramer, J. and Lagasse, E. and Dorrell, C. and Tarlow, B. and Wang, Y. and Canaday, P.S. and Haft, A. and Schug, J. and Streeter, P.R. and Finegold, M.J. and Shenje, L.T. and Kaestner, K.H. and Grompe, M. and Drost, J. and van Jaarsveld, R.H. and Ponsioen, B. and Zimberlin, C. and van Boxtel, R. and Buijs, A. and Sachs, N. and Overmeer, R.M. and Offerhaus, G.J. and Begthel, H. and Al, et and Dye, B.R. and Hill, D.R. and Ferguson, M.A. and Tsai, Y.H. and Nagy, M.S. and Dyal, R. and Wells, J.M. and Mayhew, C.N. and Nattiv, R. and Klein, O.D. and Al, et and Eiraku, M. and Sasai, Y. and Eiraku, M. and Watanabe, K. and Matsuo-Takasaki, M. and Kawada, M. and Yonemura, S. and Matsumura, M. and Wataya, T. and Nishiyama, A. and Muguruma, K. and Sasai, Y. and Eiraku, M. and Takata, N. and Ishibashi, H. and Kawada, M. and Sakakura, E. and Okuda, S. and Sekiguchi, K. and Adachi, T. and Sasai, Y. and Firth, A.L. and Menon, T. and Parker, G.S. and Qualls, S.J. and Lewis, B.M. and Ke, E. and Dargitz, C.T. and Wright, R. and Khanna, A. and Gage, F.H. and Verma, I.M. and Fordham, R.P. and Yui, S. and Hannan, N.R. and Soendergaard, C. and Madgwick, A. and Schweiger, P.J. and Nielsen, O.H. and Vallier, L. and Pedersen, R.A. and Nakamura, T. and Al, et and Fukuda, M. and Mizutani, T. and Mochizuki, W. and Matsumoto, T. and Nozaki, K. and Sakamaki, Y. and Ichinose, S. and Okada, Y. and Tanaka, T. and Watanabe, M. and Nakamura, T. and Gallico, G.G. and O'Connor, N.E. and Compton, C.C. and Kehinde, O. and Green, H. and Gao, D. and Vela, I. and Sboner, A. and Iaquinta, P.J. and Karthaus, W.R. and Gopalan, A. and Dowling, C. and Wanjala, J.N. and Undvall, E.A. and Arora, V.K. and Al, et and Grün, D. and Lyubimova, A. and Kester, L. and Wiebrands, K. and Basak, O. and Sasaki, N. and Clevers, H. and van Oudenaarden, A. and Huang, S.X. and Islam, M.N. and O'Neill, J. and Hu, Z. and Yang, Y.G. and Chen, Y.W. and Mumau, M. and Green, M.D. and Vunjak-Novakovic, G. and Bhattacharya, J. and Snoeck, H.W. and Huang, L. and Holtzinger, A. and Jagan, I. and BeGora, M. and Lohse, I. and Ngai, N. and Nostro, C. and Wang, R. and Muthuswamy, L.B. and Crawford, H.C. and Al, et and Huch, M. and Bonfanti, P. and Boj, S.F. and Sato, T. and Loomans, C.J. and van de Wetering, M. and Sojoodi, M. and Li, V.S. and Schuijers, J. and Gracanin, A. and Al, et and Huch, M. and Dorrell, C. and Boj, S.F. and van Es, J.H. and Li, V.S. and van de Wetering, M. and Sato, T. and Hamer, K. and Sasaki, N. and Finegold, M.J. and Al, et and Huch, M. and Gehart, H. and van Boxtel, R. and Hamer, K. and Blokzijl, F. and Verstegen, M.M. and Ellis, E. and van Wenum, M. and Fuchs, S.A. and de Ligt, J. and Al, et and Jain, R. and Barkauskas, C.E. and Takeda, N. and Bowie, E.J. and Aghajanian, H. and Wang, Q. and Padmanabhan, A. and Manderfield, L.J. and Gupta, M. and Li, D. and Al, et and Jung, P. and Sato, T. and Merlos-Suárez, A. and Barriga, F.M. and Iglesias, M. and Rossell, D. and Auer, H. and Gallardo, M. and Blasco, M.A. and Sancho, E. and Al, et and Karthaus, W.R. and Iaquinta, P.J. and Drost, J. and Gracanin, A. and van Boxtel, R. and Wongvipat, J. and Dowling, C.M. and Gao, D. and Begthel, H. and Sachs, N. and Al, et and Kessler, M. and Hoffmann, K. and Brinkmann, V. and Thieck, O. and Jackisch, S. and Toelle, B. and Berger, H. and Mollenkopf, H.J. and Mangler, M. and Sehouli, J. and Al, et and Korinek, V. and Barker, N. and Moerer, P. and van Donselaar, E. and Huls, G. and Peters, P.J. and Clevers, H. and Kurmann, A.A. and Serra, M. and Hawkins, F. and Rankin, S.A. and Mori, M. and Astapova, I. and Ullas, S. and Lin, S. and Bilodeau, M. and Rossant, J. and Al, et and Lancaster, M.A. and Knoblich, J.A. and Lancaster, M.A. and Renner, M. and Martin, C.A. and Wenzel, D. and Bicknell, L.S. and Hurles, M.E. and Homfray, T. and Penninger, J.M. and Jackson, A.P. and Knoblich, J.A. and Li, X. and Nadauld, L. and Ootani, A. 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De and Os, R. Van and Al, et and Matano, M. and Date, S. and Shimokawa, M. and Takano, A. and Fujii, M. and Ohta, Y. and Watanabe, T. and Kanai, T. and Sato, T. and McCracken, K.W. and Catá, E.M. and Crawford, C.M. and Sinagoga, K.L. and Schumacher, M. and Rockich, B.E. and Tsai, Y.H. and Mayhew, C.N. and Spence, J.R. and Zavros, Y. and Wells, J.M. and Muguruma, K. and Nishiyama, A. and Ono, Y. and Miyawaki, H. and Mizuhara, E. and Hori, S. and Kakizuka, A. and Obata, K. and Yanagawa, Y. and Hirano, T. and Sasai, Y. and Muguruma, K. and Nishiyama, A. and Kawakami, H. and Hashimoto, K. and Sasai, Y. and Nadauld, L.D. and Garcia, S. and Natsoulis, G. and Bell, J.M. and Miotke, L. and Hopmans, E.S. and Xu, H. and Pai, R.K. and Palm, C. and Regan, J.F. and Al, et and Nakano, T. and Ando, S. and Takata, N. and Kawada, M. and Muguruma, K. and Sekiguchi, K. and Saito, K. and Yonemura, S. and Eiraku, M. and Sasai, Y. and Nanduri, L.S. and Baanstra, M. and Faber, H. and Rocchi, C. and Zwart, E. and de Haan, G. and van Os, R. and Coppes, R.P. and Nantasanti, S. and Spee, B. and Kruitwagen, H.S. and Chen, C. and Geijsen, N. and Oosterhoff, L.A. and van Wolferen, M.E. and Pelaez, N. and Fieten, H. and Wubbolts, R.W. and Al, et and O'connor, N.E. and Mulliken, J.B. and Banks-Schlegel, S. and Kehinde, O. and Green, H. and Ootani, A. and Li, X. and Sangiorgi, E. and Ho, Q.T. and Ueno, H. and Toda, S. and Sugihara, H. and Fujimoto, K. and Weissman, I.L. and Capecchi, M.R. and Kuo, C.J. and Pellegrini, G. and Traverso, C.E. and Franzi, A.T. and Zingirian, M. and Cancedda, R. and Luca, M. 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Chuva de Sousa and Little, M.H. and Takebe, T. and Sekine, K. and Enomura, M. and Koike, H. and Kimura, M. and Ogaeri, T. and Zhang, R.R. and Ueno, Y. and Zheng, Y.W. and Koike, N. and Al, et and Treutlein, B. and Brownfield, D.G. and Wu, A.R. and Neff, N.F. and Mantalas, G.L. and Espinoza, F.H. and Desai, T.J. and Krasnow, M.A. and Quake, S.R. and van de Wetering, M. and Francies, H.E. and Francis, J.M. and Bounova, G. and Iorio, F. and Pronk, A. and van Houdt, W. and van Gorp, J. and Taylor-Weiner, A. and Kester, L. and Al, et and von Furstenberg, R.J. and Gulati, A.S. and Baxi, A. and Doherty, J.M. and Stappenbeck, T.S. and Gracz, A.D. and Magness, S.T. and Henning, S.J. and Wang, F. and Scoville, D. and He, X.C. and Mahe, M.M. and Box, A. and Perry, J.M. and Smith, N.R. and Lei, N.Y. and Davies, P.S. and Fuller, M.K. and Al, et and Wang, X. and Yamamoto, Y. and Wilson, L.H. and Zhang, T. and Howitt, B.E. and Farrow, M.A. and Kern, F. and Ning, G. and Hong, Y. and Khor, C.C. and Al, et and Watson, C.L. and Mahe, M.M. and Múnera, J. and Howell, J.C. and Sundaram, N. and Poling, H.M. and Schweitzer, J.I. and Vallance, J.E. and Mayhew, C.N. and Sun, Y. and Al, et and Wong, A.P. and Bear, C.E. and Chin, S. and Pasceri, P. and Thompson, T.O. and Huan, L.J. and Ratjen, F. and Ellis, J. and Rossant, J. and Xia, Y. and Nivet, E. and Sancho-Martinez, I. and Gallegos, T. and Suzuki, K. and Okamura, D. and Wu, M.Z. and Dubova, I. and Esteban, C.R. and Montserrat, N. and Al, et and Yin, X. and Farin, H.F. and van Es, J.H. and Clevers, H. and Langer, R. and Karp, J.M. and Yui, S. and Nakamura, T. and Sato, T. and Nemoto, Y. and Mizutani, T. and Zheng, X. and Ichinose, S. and Nagaishi, T. and Okamoto, R. and Tsuchiya, K. and Al, et},
+  options = {useprefix=true},
   date = {2016-06},
   journaltitle = {Cell},
   volume = {165},
+  number = {7},
   pages = {1586--1597},
   issn = {00928674},
   doi = {10.1016/j.cell.2016.05.082},
-  url = {http://linkinghub.elsevier.com/retrieve/pii/S0092867416307292},
-  number = {7},
-  options = {useprefix=true}
+  url = {http://linkinghub.elsevier.com/retrieve/pii/S0092867416307292}
 }
 
 @article{clewleyHybridModelsBiological2012,
@@ -656,16 +656,16 @@ @article{clewleyHybridModelsBiological2012
   date = {2012},
   journaltitle = {PLoS Comput. Biol.},
   volume = {8},
+  number = {8},
+  eprint = {22912566},
+  eprinttype = {pmid},
   pages = {e1002628},
   issn = {1553-7358},
   doi = {10.1371/journal.pcbi.1002628},
   abstract = {The PyDSTool software environment is designed to develop, simulate, and analyze dynamical systems models, particularly for biological applications. Unlike the engineering application focus and graphical specification environments of most general purpose simulation tools, PyDSTool provides a programmatic environment well suited to exploratory data- and hypothesis-driven biological modeling problems. In this work, we show how the environment facilitates the application of hybrid dynamical modeling to the reverse engineering of complex biophysical dynamics; in this case, of an excitable membrane. The example demonstrates how the software provides novel tools that support the inference and validation of mechanistic hypotheses and the inclusion of data constraints in both quantitative and qualitative ways. The biophysical application is broadly relevant to models in the biosciences. The open source and platform-independent PyDSTool package is freely available under the BSD license from http://sourceforge.net/projects/pydstool/. The hosting service provides links to documentation and online forums for user support.},
-  eprint = {22912566},
-  eprinttype = {pmid},
-  keywords = {Biophysics,Models; Biological},
   langid = {english},
-  number = {8},
-  pmcid = {PMC3415397}
+  pmcid = {PMC3415397},
+  keywords = {Biophysics,Models; Biological}
 }
 
 @article{coburnContactInhibitionLocomotion2016,
@@ -674,17 +674,17 @@ @article{coburnContactInhibitionLocomotion2016
   date = {2016-07-11},
   journaltitle = {Mol. Biol. Cell},
   volume = {27},
+  number = {22},
+  eprint = {27605701},
+  eprinttype = {pmid},
   pages = {3436--3448},
   issn = {1059-1524, 1939-4586},
   doi = {10.1091/mbc.E16-04-0226},
   url = {http://www.molbiolcell.org/content/27/22/3436},
   urldate = {2016-11-04},
   abstract = {We used a computational approach to analyze the biomechanics of epithelial cell aggregates—islands, stripes, or entire monolayers—that combines both vertex and contact-inhibition-of-locomotion models to include cell–cell and cell–substrate adhesion. Examination of the distribution of cell protrusions (adhesion to the substrate) in the model predicted high-order profiles of cell organization that agree with those previously seen experimentally. Cells acquired an asymmetric distribution of basal protrusions, traction forces, and apical aspect ratios that decreased when moving from the edge to the island center. Our in silico analysis also showed that tension on cell–cell junctions and apical stress is not homogeneous across the island. Instead, these parameters are higher at the island center and scale up with island size, which we confirmed experimentally using laser ablation assays and immunofluorescence. Without formally being a three-dimensional model, our approach has the minimal elements necessary to reproduce the distribution of cellular forces and mechanical cross-talk, as well as the distribution of principal stress in cells within epithelial cell aggregates. By making experimentally testable predictions, our approach can aid in mechanical analysis of epithelial tissues, especially when local changes in cell–cell and/or cell–substrate adhesion drive collective cell behavior.},
-  eprint = {27605701},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/NWVTPMM2/3436.html},
   langid = {english},
-  number = {22}
+  file = {/home/guillaume/Zotero/storage/NWVTPMM2/3436.html}
 }
 
 @article{combedazouMyosinIIGoverns2016,
@@ -692,16 +692,16 @@ @article{combedazouMyosinIIGoverns2016
   author = {Combedazou, Anne and Choesmel-Cadamuro, Valérie and Gay, Guillaume and Liu, Jiaying and Dupré, Loïc and Ramel, Damien and Wang, Xiaobo},
   date = {2016-01-01},
   journaltitle = {J Cell Sci},
+  eprint = {27034137},
+  eprinttype = {pmid},
   pages = {jcs.179952},
   issn = {0021-9533, 1477-9137},
   doi = {10.1242/jcs.179952},
   url = {http://jcs.biologists.org/content/early/2016/03/30/jcs.179952},
   urldate = {2017-06-29},
   abstract = {Skip to Next Section Border cell migration during Drosophila oogenesis is a potent model to study collective cell migration, a process involved in development and metastasis. Border cell clusters adopt two main types of behaviour during migration: linear and rotational. Still, the molecular mechanism controlling the switch from one to the other is unknown. Here, we demonstrate that non-muscle Myosin II activity controls the linear to rotational switch. Further, we show that the regulation of NMII takes place downstream of guidance receptor signalling and is critical to ensure efficient collective migration. This study thus provides new insight into the molecular mechanism coordinating the different cell behaviours in a migrating cluster.},
-  eprint = {27034137},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/D6AVGA84/Combedazou et al. - 2016 - Myosin II governs collective cell migration behavi.pdf;/home/guillaume/Zotero/storage/UTDHJNGX/jcs.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/D6AVGA84/Combedazou et al. - 2016 - Myosin II governs collective cell migration behavi.pdf;/home/guillaume/Zotero/storage/UTDHJNGX/jcs.html}
 }
 
 @article{combedazouMyosinIIGoverns2016a,
@@ -709,16 +709,16 @@ @article{combedazouMyosinIIGoverns2016a
   author = {Combedazou, A and Choesmel-Cadamuro, V and Gay, G and Liu, J and Dupré, L and Ramel, D and Wang, X},
   date = {2016-03-31},
   journaltitle = {J Cell Sci},
+  eprint = {27034137},
+  eprinttype = {pmid},
   pages = {jcs.179952},
   issn = {0021-9533, 1477-9137},
   doi = {10.1242/jcs.179952},
   url = {http://jcs.biologists.org/content/early/2016/04/13/jcs.179952},
   urldate = {2016-12-11},
   abstract = {Skip to Next Section Border cell migration during Drosophila oogenesis is a potent model to study collective cell migration, a process involved in development and metastasis. Border cell clusters adopt two main types of behaviour during migration: linear and rotational. However, the molecular mechanism controlling the switch from one to the other is unknown. Here, we demonstrate that non-muscle Myosin II (NMII, also known as Spaghetti squash) activity controls the linear-to-rotational switch. Furthermore, we show that the regulation of NMII takes place downstream of guidance receptor signalling and is critical to ensure efficient collective migration. This study thus provides new insight into the molecular mechanism coordinating the different cell behaviours in a migrating cluster.},
-  eprint = {27034137},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/ISDPHQFX/Combedazou et al. - 2016 - Myosin II governs collective cell migration behavi.pdf;/home/guillaume/Zotero/storage/KWZJ8BQP/jcs.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/ISDPHQFX/Combedazou et al. - 2016 - Myosin II governs collective cell migration behavi.pdf;/home/guillaume/Zotero/storage/KWZJ8BQP/jcs.html}
 }
 
 @online{ComparingIndividualbasedApproaches,
@@ -740,15 +740,15 @@ @article{conteBiomechanicalAnalysisVentral2012
   date = {2012},
   journaltitle = {PLoS ONE},
   volume = {7},
+  number = {4},
+  eprint = {22511944},
+  eprinttype = {pmid},
   doi = {10.1371/journal.pone.0034473},
   url = {/pmcc/articles/PMC3325263/?report=abstract},
   urldate = {2018-01-09},
   abstract = {PubMed Central Canada (PMC Canada) provides free access to a stable and permanent online digital archive of full-text, peer-reviewed health and life sciences research publications. It builds on PubMed Central (PMC), the U.S. National Institutes of Health (NIH) free digital archive of biomedical and life sciences journal literature and is a member of the broader PMC International (PMCI) network of e-repositories.},
-  eprint = {22511944},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/7TXKH2NN/PMC3325263.html},
   langid = {english},
-  number = {4}
+  file = {/home/guillaume/Zotero/storage/7TXKH2NN/PMC3325263.html}
 }
 
 @article{conteBiomechanicalAnalysisVentral2012a,
@@ -757,15 +757,15 @@ @article{conteBiomechanicalAnalysisVentral2012a
   date = {2012-04-12},
   journaltitle = {PLOS ONE},
   volume = {7},
+  number = {4},
   pages = {e34473},
   issn = {1932-6203},
   doi = {10.1371/journal.pone.0034473},
   url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0034473},
   urldate = {2017-03-04},
   abstract = {The article provides a biomechanical analysis of ventral furrow formation in the Drosophila melanogaster embryo. Ventral furrow formation is the first large-scale morphogenetic movement in the fly embryo. It involves deformation of a uniform cellular monolayer formed following cellularisation, and has therefore long been used as a simple system in which to explore the role of mechanics in force generation. Here we use a quantitative framework to carry out a systematic perturbation analysis to determine the role of each of the active forces observed. The analysis confirms that ventral furrow invagination arises from a combination of apical constriction and apical–basal shortening forces in the mesoderm, together with a combination of ectodermal forces. We show that the mesodermal forces are crucial for invagination: the loss of apical constriction leads to a loss of the furrow, while the mesodermal radial shortening forces are the primary cause of the internalisation of the future mesoderm as the furrow rises. Ectodermal forces play a minor but significant role in furrow formation: without ectodermal forces the furrow is slower to form, does not close properly and has an aberrant morphology. Nevertheless, despite changes in the active mesodermal and ectodermal forces lead to changes in the timing and extent of furrow, invagination is eventually achieved in most cases, implying that the system is robust to perturbation and therefore over-determined.},
-  file = {/home/guillaume/Zotero/storage/P2QUJSCV/Conte et al. - 2012 - A Biomechanical Analysis of Ventral Furrow Formati.pdf;/home/guillaume/Zotero/storage/4GT3W99T/article.html},
   keywords = {Deformation,Drosophila melanogaster,Ectoderm,Embryos,Epithelium,In vivo imaging,Mesoderm,Mesodermal cells},
-  number = {4}
+  file = {/home/guillaume/Zotero/storage/P2QUJSCV/Conte et al. - 2012 - A Biomechanical Analysis of Ventral Furrow Formati.pdf;/home/guillaume/Zotero/storage/4GT3W99T/article.html}
 }
 
 @article{coravosActomyosinPulsingTissue2017,
@@ -774,16 +774,16 @@ @article{coravosActomyosinPulsingTissue2017
   date = {2017-04-01},
   journaltitle = {Trends in Cell Biology},
   volume = {27},
+  number = {4},
+  eprint = {27989655},
+  eprinttype = {pmid},
   pages = {276--283},
   issn = {0962-8924, 1879-3088},
   doi = {10.1016/j.tcb.2016.11.008},
   url = {http://www.cell.com/trends/cell-biology/abstract/S0962-8924(16)30206-9},
   urldate = {2017-03-24},
-  eprint = {27989655},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/2BQNNHIP/coravos2016.pdf;/home/guillaume/Zotero/storage/W4TCU87V/S0962-8924(16)30206-9.html},
   langid = {english},
-  number = {4}
+  file = {/home/guillaume/Zotero/storage/2BQNNHIP/coravos2016.pdf;/home/guillaume/Zotero/storage/W4TCU87V/S0962-8924(16)30206-9.html}
 }
 
 @incollection{cordelieres3DQuantitativeColocalisation2020,
@@ -792,6 +792,7 @@ @incollection{cordelieres3DQuantitativeColocalisation2020
   author = {Cordelières, Fabrice P. and Zhang, Chong},
   editor = {Miura, Kota and Sladoje, Nataša},
   date = {2020},
+  series = {Learning {{Materials}} in {{Biosciences}}},
   pages = {33--66},
   publisher = {{Springer International Publishing}},
   location = {{Cham}},
@@ -800,8 +801,7 @@ @incollection{cordelieres3DQuantitativeColocalisation2020
   urldate = {2019-10-24},
   abstract = {In this module we will first build a 3D object based colocalisation macro step by step. Then we will practice to adapt and extend the current macro such that it can also work with intensity-based colocalisation methods.},
   isbn = {978-3-030-22386-1},
-  langid = {english},
-  series = {Learning {{Materials}} in {{Biosciences}}}
+  langid = {english}
 }
 
 @article{courtheoux_ase1/prc1-dependent_2009,
@@ -810,8 +810,8 @@ @article{courtheoux_ase1/prc1-dependent_2009
   date = {2009},
   journaltitle = {The Journal of cell biology},
   volume = {187},
-  pages = {399--412},
-  number = {3}
+  number = {3},
+  pages = {399--412}
 }
 
 @article{courtheoux_dynein_2007,
@@ -820,13 +820,14 @@ @article{courtheoux_dynein_2007
   date = {2007},
   journaltitle = {Biology of the Cell},
   volume = {99},
-  pages = {627--637},
-  number = {11}
+  number = {11},
+  pages = {627--637}
 }
 
 @article{curranMyosinIIControls2017,
   title = {Myosin {{II Controls Junction Fluctuations}} to {{Guide Epithelial Tissue Ordering}}},
   author = {Curran, Scott and Strandkvist, Charlotte and Bathmann, Jasper and de Gennes, Marc and Kabla, Alexandre and Salbreux, Guillaume and Baum, Buzz},
+  options = {useprefix=true},
   date = {2017-10-26},
   journaltitle = {Developmental Cell},
   issn = {1534-5807},
@@ -834,44 +835,43 @@ @article{curranMyosinIIControls2017
   url = {http://www.sciencedirect.com/science/article/pii/S1534580717307712},
   urldate = {2017-11-15},
   abstract = {Summary Under conditions of homeostasis, dynamic changes in the length of individual adherens junctions (AJs) provide epithelia with the fluidity required to maintain tissue integrity in the face of intrinsic and extrinsic forces. While the contribution of AJ remodeling to developmental morphogenesis has been intensively studied, less is known about AJ dynamics in other circumstances. Here, we study AJ dynamics in an epithelium that undergoes a gradual increase in packing order, without concomitant large-scale changes in tissue size or shape. We find that neighbor exchange events are driven by stochastic fluctuations in junction length, regulated in part by junctional actomyosin. In this context, the developmental increase of isotropic junctional actomyosin reduces the rate of neighbor exchange, contributing to tissue order. We propose a model in which the local variance in tension between junctions determines whether actomyosin-based forces will inhibit or drive the topological transitions that either refine or deform a tissue.},
-  file = {/home/guillaume/Zotero/storage/ENNMXRC2/Curran et al. - 2017 - Myosin II Controls Junction Fluctuations to Guide .pdf;/home/guillaume/Zotero/storage/YYMC7LTP/S1534580717307712.html},
   keywords = {cadherin,epithelia,junction fluctuations,Morphogenesis,Myosin,neighbor exchange,Tissue mechanics,tissue refinement,vertex model},
-  options = {useprefix=true}
+  file = {/home/guillaume/Zotero/storage/ENNMXRC2/Curran et al. - 2017 - Myosin II Controls Junction Fluctuations to Guide .pdf;/home/guillaume/Zotero/storage/YYMC7LTP/S1534580717307712.html}
 }
 
 @article{davidson_emergent_2010,
   title = {Emergent Morphogenesis: {{Elastic}} Mechanics of a Self-Deforming Tissue},
   author = {a. Davidson, Lance and Joshi, Sagar D. and Kim, Hye Young and von Dassow, Michelangelo and Zhang, Lin and Zhou, Jian},
+  options = {useprefix=true},
   date = {2010},
   journaltitle = {Journal of Biomechanics},
   volume = {43},
+  number = {1},
+  eprint = {19815213},
+  eprinttype = {pmid},
   pages = {63--70},
   issn = {00219290},
   doi = {10.1016/j.jbiomech.2009.09.010},
   url = {http://dx.doi.org/10.1016/j.jbiomech.2009.09.010},
   abstract = {Multicellular organisms are generated by coordinated cell movements during morphogenesis. Convergent extension is a key tissue movement that organizes mesoderm, ectoderm, and endoderm in vertebrate embryos. The goals of researchers studying convergent extension, and morphogenesis in general, include understanding the molecular pathways that control cell identity, establish fields of cell types, and regulate cell behaviors. Cell identity, the size and boundaries of tissues, and the behaviors exhibited by those cells shape the developing embryo; however, there is a fundamental gap between understanding the molecular pathways that control processes within single cells and understanding how cells work together to assemble multicellular structures. Theoretical and experimental biomechanics of embryonic tissues are increasingly being used to bridge that gap. The efforts to map molecular pathways and the mechanical processes underlying morphogenesis are crucial to understanding: (1) the source of birth defects, (2) the formation of tumors and progression of cancer, and (3) basic principles of tissue engineering. In this paper, we first review the process of tissue convergent extension of the vertebrate axis and then review models used to study the self-organizing movements from a mechanical perspective. We conclude by presenting a relatively simple "wedge-model" that exhibits key emergent properties of convergent extension such as the coupling between tissue stiffness, cell intercalation forces, and tissue elongation forces. ?? 2009 Elsevier Ltd. All rights reserved.},
-  eprint = {19815213},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/HJKSSG33/Davidson et al. - 2010 - Emergent morphogenesis Elastic mechanics of a self-deforming tissue.pdf},
   keywords = {Cell shape and tissue mechanics,Computer Simulation,Convergence and extension,Convergent extension,Frog,Gastrulation,In silico,Modeling},
-  number = {1},
-  options = {useprefix=true}
+  file = {/home/guillaume/Zotero/storage/HJKSSG33/Davidson et al. - 2010 - Emergent morphogenesis Elastic mechanics of a self-deforming tissue.pdf}
 }
 
 @online{debackStatisticalMathematicalModeling2018,
   title = {Statistical and Mathematical Modeling of Spatiotemporal Dynamics of Stem Cells},
   author = {de Back, Walter and Zerjatke, Thomas and Roeder, Ingo},
+  options = {useprefix=true},
   date = {2018-09-05},
+  eprint = {1809.01708},
+  eprinttype = {arxiv},
+  primaryclass = {q-bio},
   url = {http://arxiv.org/abs/1809.01708},
   urldate = {2018-09-13},
   abstract = {Statistical and mathematical modeling are crucial to describe, interpret, compare and predict the behavior of complex biological systems including the organization of hematopoietic stem and progenitor cells in the bone marrow environment. The current prominence of high-resolution and live-cell imaging data provides an unprecedented opportunity to study the spatiotemporal dynamics of these cells within their stem cell niche and learn more about aberrant, but also unperturbed, normal hematopoiesis. However, this requires careful quantitative statistical analysis of the spatial and temporal behavior of cells and the interaction with their microenvironment. Moreover, such quantification is a prerequisite for the construction of hypothesis-driven mathematical models that can provide mechanistic explanations by generating spatiotemporal dynamics that can be directly compared to experimental observations. Here, we provide a brief overview of statistical methods in analyzing spatial distribution of cells, cell motility, cell shapes and cellular genealogies. We also describe cell- based modeling formalisms that allow researchers to simulate emergent behavior in a multicellular system based on a set of hypothesized mechanisms. Together, these methods provide a quantitative workflow for the analytic and synthetic study of the spatiotemporal behavior of hematopoietic stem and progenitor cells.},
   archiveprefix = {arXiv},
-  eprint = {1809.01708},
-  eprinttype = {arxiv},
-  file = {/home/guillaume/Zotero/storage/PNYJNKYB/de Back et al. - 2018 - Statistical and mathematical modeling of spatiotem.pdf;/home/guillaume/Zotero/storage/87PNI822/1809.html},
   keywords = {Quantitative Biology - Quantitative Methods},
-  options = {useprefix=true},
-  primaryclass = {q-bio}
+  file = {/home/guillaume/Zotero/storage/PNYJNKYB/de Back et al. - 2018 - Statistical and mathematical modeling of spatiotem.pdf;/home/guillaume/Zotero/storage/87PNI822/1809.html}
 }
 
 @article{demongeotDiscreteMeshApproach2016,
@@ -881,15 +881,15 @@ @article{demongeotDiscreteMeshApproach2016
   date = {2016-12-01},
   journaltitle = {Acta Biotheor},
   volume = {64},
+  number = {4},
   pages = {427--446},
   issn = {0001-5342, 1572-8358},
   doi = {10.1007/s10441-016-9301-4},
   url = {https://link.springer.com/article/10.1007/s10441-016-9301-4},
   urldate = {2017-03-04},
   abstract = {Morphogenesis is a general concept in biology including all the processes which generate tissue shapes and cellular organizations in a living organism. Many hybrid formalizations (i.e., with both discrete and continuous parts) have been proposed for modelling morphogenesis in embryonic or adult animals, like gastrulation. We propose first to study the ventral furrow invagination as the initial step of gastrulation, early stage of embryogenesis. We focus on the study of the connection between the apical constriction of the ventral cells and the initiation of the invagination. For that, we have created a 3D biomechanical model of the embryo of the Drosophila melanogaster based on the finite element method. Each cell is modelled by an elastic hexahedron contour and is firmly attached to its neighbouring cells. A uniform initial distribution of elastic and contractile forces is applied to cells along the model. Numerical simulations show that invagination starts at ventral curved extremities of the embryo and then propagates to the ventral medial layer. Then, this observation already made in some experiments can be attributed uniquely to the specific shape of the embryo and we provide mechanical evidence to support it. Results of the simulations of the “pill-shaped” geometry of the Drosophila melanogaster embryo are compared with those of a spherical geometry corresponding to the Xenopus lævis embryo. Eventually, we propose to study the influence of cell proliferation on the end of the process of invagination represented by the closure of the ventral furrow.},
-  file = {/home/guillaume/Zotero/storage/BMEZQJRQ/demongeot2016.pdf;/home/guillaume/Zotero/storage/5S7VZWJI/s10441-016-9301-4.html},
   langid = {english},
-  number = {4}
+  file = {/home/guillaume/Zotero/storage/BMEZQJRQ/demongeot2016.pdf;/home/guillaume/Zotero/storage/5S7VZWJI/s10441-016-9301-4.html}
 }
 
 @article{desclouxParameterfreeImageResolution2019,
@@ -898,6 +898,7 @@ @article{desclouxParameterfreeImageResolution2019
   date = {2019-09},
   journaltitle = {Nat Methods},
   volume = {16},
+  number = {9},
   pages = {918--924},
   publisher = {{Nature Publishing Group}},
   issn = {1548-7105},
@@ -905,10 +906,9 @@ @article{desclouxParameterfreeImageResolution2019
   url = {https://www.nature.com/articles/s41592-019-0515-7},
   urldate = {2020-03-25},
   abstract = {Decorrelation analysis offers an improved method for assessing image resolution that works on a single image and is insensitive to common image artifacts. The method can be applied generally to any type of microscopy images.},
-  file = {/home/guillaume/Zotero/storage/8KQJIPBC/Descloux et al. - 2019 - Parameter-free image resolution estimation based o.pdf;/home/guillaume/Zotero/storage/LZXIXIAW/s41592-019-0515-7.html},
   issue = {9},
   langid = {english},
-  number = {9}
+  file = {/home/guillaume/Zotero/storage/8KQJIPBC/Descloux et al. - 2019 - Parameter-free image resolution estimation based o.pdf;/home/guillaume/Zotero/storage/LZXIXIAW/s41592-019-0515-7.html}
 }
 
 @article{diazdelalozaForcesShapingDrosophila,
@@ -930,6 +930,7 @@ @article{drasdoSinglecellbasedModelTumor2005
   date = {2005-07},
   journaltitle = {Phys. Biol.},
   volume = {2},
+  number = {3},
   pages = {133--147},
   publisher = {{IOP Publishing}},
   issn = {1478-3975},
@@ -937,9 +938,8 @@ @article{drasdoSinglecellbasedModelTumor2005
   url = {https://doi.org/10.1088/1478-3975/2/3/001},
   urldate = {2021-02-08},
   abstract = {To what extent the growth dynamics of tumors is controlled by nutrients, biomechanical forces and other factors at different stages and in different environments is still largely unknown. Here we present a biophysical model to study the spatio-temporal growth dynamics of two-dimensional tumor monolayers and three-dimensional tumor spheroids as a complementary tool to in vitro experiments. Within our model each cell is represented as an individual object and parametrized by cell-biophysical and cell-kinetic parameters that can all be experimentally determined. Hence our modeling strategy allows us to study which mechanisms on the microscopic level of individual cells may affect the macroscopic properties of a growing tumor. We find the qualitative growth kinetics and patterns at early growth stages to be remarkably robust. Quantitative comparisons between computer simulations using our model and published experimental observations on monolayer cultures suggest a biomechanically-mediated form of growth inhibition during the experimentally observed transition from exponential to sub-exponential growth at sufficiently large tumor sizes. Our simulations show that the same transition during the growth of avascular tumor spheroids can be explained largely by the same mechanism. Glucose (or oxygen) depletion seems to determine mainly the size of the necrotic core but not the size of the tumor. We explore the consequences of the suggested biomechanical form of contact inhibition, in order to permit an experimental test of our model. Based on our findings we propose a phenomenological growth law in early expansion phases in which specific biological small-scale processes are subsumed in a small number of effective parameters.},
-  file = {/home/guillaume/Zotero/storage/FDDW7MW6/Drasdo et Höhme - 2005 - A single-cell-based model of tumor growthin vitro.pdf},
   langid = {english},
-  number = {3}
+  file = {/home/guillaume/Zotero/storage/FDDW7MW6/Drasdo et Höhme - 2005 - A single-cell-based model of tumor growthin vitro.pdf}
 }
 
 @article{dufourDecipheringTissueMorphodynamics2017,
@@ -948,15 +948,15 @@ @article{dufourDecipheringTissueMorphodynamics2017
   date = {2017-05-19},
   journaltitle = {Philos. Trans. R. Soc. Lond., B, Biol. Sci.},
   volume = {372},
+  number = {1720},
+  eprint = {28348249},
+  eprinttype = {pmid},
   issn = {1471-2970},
   doi = {10.1098/rstb.2015.0512},
   abstract = {In recent years developmental biology has greatly benefited from the latest advances in fluorescence microscopy techniques. Consequently, quantitative and automated analysis of this data is becoming a vital first step in the quest for novel insights into the various aspects of development. Here we present an introductory overview of the various image analysis methods proposed for developmental biology images, with particular attention to openly available software packages. These tools, as well as others to come, are rapidly paving the way towards standardized and reproducible bioimaging studies at the whole-tissue level. Reflecting on these achievements, we discuss the remaining challenges and the future endeavours lying ahead in the post-image analysis era.This article is part of the themed issue 'Systems morphodynamics: understanding the development of tissue hardware'.},
-  eprint = {28348249},
-  eprinttype = {pmid},
-  keywords = {bioimage informatics,cell segmentation,cell tracking,Developmental Biology,Image Processing; Computer-Assisted,Microscopy; Fluorescence,Morphogenesis,Plant Development,reproducible research,software,Software},
   langid = {english},
-  number = {1720},
-  pmcid = {PMC5379021}
+  pmcid = {PMC5379021},
+  keywords = {bioimage informatics,cell segmentation,cell tracking,Developmental Biology,Image Processing; Computer-Assisted,Microscopy; Fluorescence,Morphogenesis,Plant Development,reproducible research,software,Software}
 }
 
 @article{egan_role_????,
@@ -965,13 +965,13 @@ @article{egan_role_????
   year = {January},
   journaltitle = {Nature communications},
   volume = {6},
+  eprint = {26145480},
+  eprinttype = {pmid},
   pages = {7418},
   issn = {2041-1723},
   doi = {10.1038/ncomms8418},
   url = {http://www.nature.com.gate1.inist.fr/ncomms/2015/150706/ncomms8418/full/ncomms8418.html},
   abstract = {Natural systems frequently exploit intricate multiscale and multiphasic structures to achieve functionalities beyond those of man-made systems. Although understanding the chemical make-up of these systems is essential, the passive and active mechanics within biological systems are crucial when considering the many natural systems that achieve advanced properties, such as high strength-to-weight ratios and stimuli-responsive adaptability. Discovering how and why biological systems attain these desirable mechanical functionalities often reveals principles that inform new synthetic designs based on biological systems. Such approaches have traditionally found success in medical applications, and are now informing breakthroughs in diverse frontiers of science and engineering.},
-  eprint = {26145480},
-  eprinttype = {pmid},
   langid = {english}
 }
 
@@ -981,16 +981,16 @@ @article{ellenbergCallPublicArchives2018
   date = {2018-11},
   journaltitle = {Nat. Methods},
   volume = {15},
+  number = {11},
+  eprint = {30377375},
+  eprinttype = {pmid},
   pages = {849--854},
   issn = {1548-7105},
   doi = {10.1038/s41592-018-0195-8},
-  eprint = {30377375},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/QUGBPJ2Y/Ellenberg et al. - 2018 - A call for public archives for biological image da.pdf},
-  keywords = {Archives,Databases as Topic,Diagnostic Imaging,Humans,Image Processing; Computer-Assisted,Information Dissemination,Information Storage and Retrieval,Public Sector},
   langid = {english},
-  number = {11},
-  pmcid = {PMC6884425}
+  pmcid = {PMC6884425},
+  keywords = {Archives,Databases as Topic,Diagnostic Imaging,Humans,Image Processing; Computer-Assisted,Information Dissemination,Information Storage and Retrieval,Public Sector},
+  file = {/home/guillaume/Zotero/storage/QUGBPJ2Y/Ellenberg et al. - 2018 - A call for public archives for biological image da.pdf}
 }
 
 @article{elosegui-artola_mechanical_2016,
@@ -998,11 +998,11 @@ @article{elosegui-artola_mechanical_2016
   author = {Elosegui-Artola, Alberto and Oria, Roger and Chen, Yunfeng and Kosmalska, Anita and Pérez-González, Carlos and Castro, Natalia and Zhu, Cheng and Trepat, Xavier and Roca-Cusachs, Pere},
   date = {2016-04},
   journaltitle = {Nature cell biology},
+  eprint = {27065098},
+  eprinttype = {pmid},
   issn = {1476-4679},
   doi = {10.1038/ncb3336},
-  abstract = {Cell function depends on tissue rigidity, which cells probe by applying and transmitting forces to their extracellular matrix, and then transducing them into biochemical signals. Here we show that in response to matrix rigidity and density, force transmission and transduction are explained by the mechanical properties of the actin-talin-integrin-fibronectin clutch. We demonstrate that force transmission is regulated by a dynamic clutch mechanism, which unveils its fundamental biphasic force/rigidity relationship on talin depletion. Force transduction is triggered by talin unfolding above a stiffness threshold. Below this threshold, integrins unbind and release force before talin can unfold. Above the threshold, talin unfolds and binds to vinculin, leading to adhesion growth and YAP nuclear translocation. Matrix density, myosin contractility, integrin ligation and talin mechanical stability differently and nonlinearly regulate both force transmission and the transduction threshold. In all cases, coupling of talin unfolding dynamics to a theoretical clutch model quantitatively predicts cell response.},
-  eprint = {27065098},
-  eprinttype = {pmid}
+  abstract = {Cell function depends on tissue rigidity, which cells probe by applying and transmitting forces to their extracellular matrix, and then transducing them into biochemical signals. Here we show that in response to matrix rigidity and density, force transmission and transduction are explained by the mechanical properties of the actin-talin-integrin-fibronectin clutch. We demonstrate that force transmission is regulated by a dynamic clutch mechanism, which unveils its fundamental biphasic force/rigidity relationship on talin depletion. Force transduction is triggered by talin unfolding above a stiffness threshold. Below this threshold, integrins unbind and release force before talin can unfold. Above the threshold, talin unfolds and binds to vinculin, leading to adhesion growth and YAP nuclear translocation. Matrix density, myosin contractility, integrin ligation and talin mechanical stability differently and nonlinearly regulate both force transmission and the transduction threshold. In all cases, coupling of talin unfolding dynamics to a theoretical clutch model quantitatively predicts cell response.}
 }
 
 @online{EmbryonicTissuesActive,
@@ -1022,8 +1022,8 @@ @article{escribanoBalanceMechanicalForces2018
   url = {https://www.biorxiv.org/content/early/2018/11/26/375931},
   urldate = {2019-01-03},
   abstract = {The formation of gaps in the endothelium is a crucial process underlying both cancer and immune cell extravasation, contributing to the functioning of the immune system during infection, the unfavorable development of chronic inflammation and tumor metastasis. Here, we present a stochastic-mechanical multiscale model of an endothelial cell monolayer and show that the dynamic nature of the endothelium leads to spontaneous gap formation, even without intervention from the transmigrating cells. These gaps preferentially appear at the vertices between three endothelial cells, as opposed to the border between two cells. We quantify the frequency and lifetime of these gaps, and validate our predictions experimentally. Interestingly, we find experimentally that cancer cells also preferentially extravasate at vertices, even when they first arrest on borders. This suggests that extravasating cells, rather than initially signaling to the endothelium, might exploit the autonomously forming gaps in the endothelium to initiate transmigration.},
-  file = {/home/guillaume/Zotero/storage/YVWNIDRW/Escribano et al. - 2018 - Balance of Mechanical Forces Drives Endothelial Ga.pdf;/home/guillaume/Zotero/storage/4NLX8XTX/375931.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/YVWNIDRW/Escribano et al. - 2018 - Balance of Mechanical Forces Drives Endothelial Ga.pdf;/home/guillaume/Zotero/storage/4NLX8XTX/375931.html}
 }
 
 @article{etournay_interplay_2015,
@@ -1032,13 +1032,13 @@ @article{etournay_interplay_2015
   date = {2015-06},
   journaltitle = {eLife},
   volume = {4},
+  eprint = {26102528},
+  eprinttype = {pmid},
   pages = {e07090},
   issn = {2050-084X},
   doi = {10.7554/eLife.07090},
   url = {http://elifesciences.org/content/early/2015/06/23/eLife.07090.abstract},
   abstract = {How tissue shape emerges from the collective mechanical properties and behavior of individual cells is not understood. We combine experiment and theory to study this problem in the developing wing epithelium of Drosophila. At pupal stages, the wing-hinge contraction contributes to anisotropic tissue flows that reshape the wing blade. Here, we quantitatively account for this wing-blade shape change on the basis of cell divisions, cell rearrangements and cell shape changes. We show that cells both generate and respond to epithelial stresses during this process, and that the nature of this interplay specifies the pattern of junctional network remodeling that changes wing shape. We show that patterned constrains exerted on the tissue by the extracellular matrix are key to force the tissue into the right shape. We present a continuum mechanical model that quantitatively describes the relationship between epithelial stresses and cell dynamics, and how their interplay reshapes the wing.},
-  eprint = {26102528},
-  eprinttype = {pmid},
   langid = {english}
 }
 
@@ -1055,15 +1055,15 @@ @article{farhadifar_influence_2007
   date = {2007},
   journaltitle = {Current Biology},
   volume = {17},
+  number = {24},
+  eprint = {18082406},
+  eprinttype = {pmid},
   pages = {2095--2104},
   issn = {09609822},
   doi = {10.1016/j.cub.2007.11.049},
   abstract = {Background: Epithelial junctional networks assume packing geometries characterized by different cell shapes, neighbor number distributions and areas. The development of specific packing geometries is tightly controlled; in the Drosophila wing epithelium, cells convert from an irregular to a hexagonal array shortly before hair formation. Packing geometry is determined by developmental mechanisms that likely control the biophysical properties of cells and their interactions. Results: To understand how physical cellular properties and proliferation determine cell-packing geometries, we use a vertex model for the epithelial junctional network in which cell packing geometries correspond to stable and stationary network configurations. The model takes into account cell elasticity and junctional forces arising from cortical contractility and adhesion. By numerically simulating proliferation, we generate different network morphologies that depend on physical parameters. These networks differ in polygon class distribution, cell area variation, and the rate of T1 and T2 transitions during growth. Comparing theoretical results to observed cell morphologies reveals regions of parameter space where calculated network morphologies match observed ones. We independently estimate parameter values by quantifying network deformations caused by laser ablating individual cell boundaries. Conclusions: The vertex model accounts qualitatively and quantitatively for the observed packing geometry in the wing disc and its response to perturbation by laser ablation. Epithelial packing geometry is a consequence of both physical cellular properties and the disordering influence of proliferation. The occurrence of T2 transitions during network growth suggests that elimination of cells from the proliferating disc epithelium may be the result of junctional force balances. © 2007 Elsevier Ltd. All rights reserved.},
-  eprint = {18082406},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/N4ZWB9UK/Farhadifar et al. - 2007 - The Influence of Cell Mechanics, Cell-Cell Interactions, and Proliferation on Epithelial Packing.pdf},
   keywords = {DEVBIO},
-  number = {24}
+  file = {/home/guillaume/Zotero/storage/N4ZWB9UK/Farhadifar et al. - 2007 - The Influence of Cell Mechanics, Cell-Cell Interactions, and Proliferation on Epithelial Packing.pdf}
 }
 
 @article{fineganTricellularVertexspecificAdhesion2019,
@@ -1076,8 +1076,8 @@ @article{fineganTricellularVertexspecificAdhesion2019
   url = {https://www.biorxiv.org/content/10.1101/704932v1},
   urldate = {2019-07-18},
   abstract = {{$<$}p{$>$}In epithelia, tricellular vertices are emerging as important sites for the regulation of epithelial integrity and function. Compared to bicellular contacts, however, much less knowledge is available. In particular, resident proteins at tricellular vertices were identified only at occluding junctions, with none known at adherens junctions. In a previous study, we discovered that in Drosophila embryos, the adhesion molecule Sidekick (Sdk), well known in invertebrates and vertebrates for its role in the visual system, localises at tricellular vertices at the level of adherens junctions. Here, we survey a wide range of Drosophila epithelia and establish that Sdk is a resident protein at tricellular adherens junctions, the first of its kind. Clonal analysis suggests that pair-wise homophilic adhesion is necessary and sufficient for Sdk tricellular vertex localisation. Super-resolution imaging using structured illumination reveals that Sdk proteins form string-like structures at vertices. Postulating that Sdk may have a role in epithelia where adherens junctions are actively remodelled, we analysed the phenotype of sdk null mutant embryos during Drosophila axis extension, using quantitative methods. We find that apical cell shapes are strikingly abnormal in sdk mutants. Moreover, adhesion at apical vertices is compromised in rearranging cells, with holes forming and persisting throughout axis extension. Finally, we show that polarized cell intercalation is decreased and abnormal in sdk mutants. Mathematical modeling of the cell behaviours supports the conclusion that the T1 transitions of polarized cell intercalation are delayed in sdk mutants. We propose that this delay, in combination with a change in the mechanical properties of the converging and extending tissue, causes the striking cell shape phenotype of sdk mutant embryos.{$<$}/p{$>$}},
-  file = {/home/guillaume/Zotero/storage/5RUSMCSI/Finegan et al. - 2019 - The tricellular vertex-specific adhesion molecule .pdf;/home/guillaume/Zotero/storage/7SDEDCDJ/Finegan et al. - 2019 - The tricellular vertex-specific adhesion molecule .pdf;/home/guillaume/Zotero/storage/CDVUQ3PC/704932v1.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/5RUSMCSI/Finegan et al. - 2019 - The tricellular vertex-specific adhesion molecule .pdf;/home/guillaume/Zotero/storage/7SDEDCDJ/Finegan et al. - 2019 - The tricellular vertex-specific adhesion molecule .pdf;/home/guillaume/Zotero/storage/CDVUQ3PC/704932v1.html}
 }
 
 @article{firmino_cell_2016,
@@ -1086,13 +1086,13 @@ @article{firmino_cell_2016
   date = {2016-02},
   journaltitle = {Developmental Cell},
   volume = {36},
+  number = {3},
   pages = {249--261},
   issn = {15345807},
   doi = {10.1016/j.devcel.2016.01.007},
   url = {http://www.cell.com/article/S1534580716000411/fulltext},
-  file = {/home/guillaume/Zotero/storage/ADEZWGKJ/Firmino et al. - 2016 - Cell Division Drives Epithelial Cell Rearrangements during Gastrulation in Chick.pdf},
   langid = {english},
-  number = {3}
+  file = {/home/guillaume/Zotero/storage/ADEZWGKJ/Firmino et al. - 2016 - Cell Division Drives Epithelial Cell Rearrangements during Gastrulation in Chick.pdf}
 }
 
 @article{fletcher_vertex_2014,
@@ -1101,15 +1101,15 @@ @article{fletcher_vertex_2014
   date = {2014},
   journaltitle = {Biophysical Journal},
   volume = {106},
+  number = {11},
+  eprint = {24896108},
+  eprinttype = {pmid},
   pages = {2291--2304},
   issn = {15420086},
   doi = {10.1016/j.bpj.2013.11.4498},
   url = {http://dx.doi.org/10.1016/j.bpj.2013.11.4498},
   abstract = {The dynamic behavior of epithelial cell sheets plays a central role during numerous developmental processes. Genetic and imaging studies of epithelial morphogenesis in a wide range of organisms have led to increasingly detailed mechanisms of cell sheet dynamics. Computational models offer a useful means by which to investigate and test these mechanisms, and have played a key role in the study of cell-cell interactions. A variety of modeling approaches can be used to simulate the balance of forces within an epithelial sheet. Vertex models are a class of such models that consider cells as individual objects, approximated by two-dimensional polygons representing cellular interfaces, in which each vertex moves in response to forces due to growth, interfacial tension, and pressure within each cell. Vertex models are used to study cellular processes within epithelia, including cell motility, adhesion, mitosis, and delamination. This review summarizes how vertex models have been used to provide insight into developmental processes and highlights current challenges in this area, including progressing these models from two to three dimensions and developing new tools for model validation. © 2014 Biophysical Society.},
-  eprint = {24896108},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/4QG5JD5K/Fletcher et al. - 2014 - Vertex models of epithelial morphogenesis.pdf},
-  number = {11}
+  file = {/home/guillaume/Zotero/storage/4QG5JD5K/Fletcher et al. - 2014 - Vertex models of epithelial morphogenesis.pdf}
 }
 
 @online{FluxMusique,
@@ -1126,15 +1126,15 @@ @article{foleyOrganoidsBetterVitro2017
   date = {2017-06},
   journaltitle = {Nat Meth},
   volume = {14},
+  number = {6},
   pages = {559--562},
   issn = {1548-7091},
   doi = {10.1038/nmeth.4307},
   url = {https://www.nature.com/nmeth/journal/v14/n6/full/nmeth.4307.html},
   urldate = {2017-06-10},
   abstract = {3D human cell cultures are changing the way scientists model organ development and function—but they have their own set of complications.},
-  file = {/home/guillaume/Zotero/storage/H498G227/Foley - 2017 - Organoids a better in vitro model.pdf;/home/guillaume/Zotero/storage/XP7GRHHV/nmeth.4307.html},
   langid = {english},
-  number = {6}
+  file = {/home/guillaume/Zotero/storage/H498G227/Foley - 2017 - Organoids a better in vitro model.pdf;/home/guillaume/Zotero/storage/XP7GRHHV/nmeth.4307.html}
 }
 
 @article{fu_imaging_2016,
@@ -1143,15 +1143,15 @@ @article{fu_imaging_2016
   date = {2016-01},
   journaltitle = {Nature communications},
   volume = {7},
+  eprint = {27004937},
+  eprinttype = {pmid},
   pages = {11088},
   issn = {2041-1723},
   doi = {10.1038/ncomms11088},
   url = {http://www.nature.com/ncomms/2016/160323/ncomms11088/full/ncomms11088.html},
   abstract = {Despite the progress made in selective plane illumination microscopy, high-resolution 3D live imaging of multicellular specimens remains challenging. Tiling light-sheet selective plane illumination microscopy (TLS-SPIM) with real-time light-sheet optimization was developed to respond to the challenge. It improves the 3D imaging ability of SPIM in resolving complex structures and optimizes SPIM live imaging performance by using a real-time adjustable tiling light sheet and creating a flexible compromise between spatial and temporal resolution. We demonstrate the 3D live imaging ability of TLS-SPIM by imaging cellular and subcellular behaviours in live C. elegans and zebrafish embryos, and show how TLS-SPIM can facilitate cell biology research in multicellular specimens by studying left-right symmetry breaking behaviour of C. elegans embryos.},
-  eprint = {27004937},
-  eprinttype = {pmid},
-  keywords = {lsfm},
-  langid = {english}
+  langid = {english},
+  keywords = {lsfm}
 }
 
 @article{gachet_sister_2008,
@@ -1160,8 +1160,8 @@ @article{gachet_sister_2008
   date = {2008},
   journaltitle = {Molecular biology of the cell},
   volume = {19},
-  pages = {1646--1662},
-  number = {4}
+  number = {4},
+  pages = {1646--1662}
 }
 
 @article{galle_modeling_2005,
@@ -1170,16 +1170,16 @@ @article{galle_modeling_2005
   date = {2005-01},
   journaltitle = {Biophysical journal},
   volume = {88},
+  number = {1},
+  eprint = {15475585},
+  eprinttype = {pmid},
   pages = {62--75},
   issn = {0006-3495},
   doi = {10.1529/biophysj.104.041459},
   url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1305039&tool=pmcentrez&rendertype=abstract},
   abstract = {We present a three-dimensional individual cell-based, biophysical model to study the effect of normal and malfunctioning growth regulation and control on the spatial-temporal organization of growing cell populations in vitro. The model includes explicit representations of typical epithelial cell growth regulation and control mechanisms, namely 1), a cell-cell contact-mediated form of growth inhibition; 2), a cell-substrate contact-dependent cell-cycle arrest; and 3), a cell-substrate contact-dependent programmed cell death (anoikis). The model cells are characterized by experimentally accessible biomechanical and cell-biological parameters. First, we study by variation of these cell-specific parameters which of them affect the macroscopic morphology and growth kinetics of a cell population within the initial expanding phase. Second, we apply selective knockouts of growth regulation and control mechanisms to investigate how the different mechanisms collectively act together. Thereby our simulation studies cover the growth behavior of epithelial cell populations ranging from undifferentiated stem cell populations via transformed variants up to tumor cell lines in vitro. We find that the cell-specific parameters, and in particular the strength of the cell-substrate anchorage, have a significant impact on the population morphology. Furthermore, they control the efficacy of the growth regulation and control mechanisms, and consequently tune the transition from controlled to uncontrolled growth that is induced by the failures of these mechanisms. Interestingly, however, we find the qualitative and quantitative growth kinetics to be remarkably robust against variations of cell-specific parameters. We compare our simulation results with experimental findings on a number of epithelial and tumor cell populations and suggest in vitro experiments to test our model predictions.},
-  eprint = {15475585},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/3S4ETHPI/Galle, Loeffler, Drasdo - 2005 - Modeling the effect of deregulated proliferation and apoptosis on the growth dynamics of epithelial cel.pdf},
   keywords = {Animals,Anoikis,apoptosis,Biological,Biophysics,Biophysics: methods,Cell Adhesion,Cell Biology,Cell Communication,Cell Cycle,Cell Differentiation,cell division,Cell Proliferation,Computer Simulation,Epithelial Cells,Epithelial Cells: cytology,Humans,In Vitro Techniques,Kinetics,Ligands,Models,Stochastic Processes,Theoretical,Time Factors},
-  number = {1}
+  file = {/home/guillaume/Zotero/storage/3S4ETHPI/Galle, Loeffler, Drasdo - 2005 - Modeling the effect of deregulated proliferation and apoptosis on the growth dynamics of epithelial cel.pdf}
 }
 
 @article{gardiner_discrete_2015,
@@ -1189,11 +1189,11 @@ @article{gardiner_discrete_2015
   date = {2015-10},
   journaltitle = {PLOS Computational Biology},
   volume = {11},
+  number = {10},
   pages = {e1004544},
   issn = {1553-7358},
   doi = {10.1371/journal.pcbi.1004544},
-  url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004544},
-  number = {10}
+  url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004544}
 }
 
 @thesis{gay_atomes_2006,
@@ -1215,8 +1215,8 @@ @article{gay_atomic_2005
   date = {2005},
   journaltitle = {Applied Physics B},
   volume = {81},
-  pages = {871--874},
-  number = {7}
+  number = {7},
+  pages = {871--874}
 }
 
 @inproceedings{gay_champs_2006,
@@ -1241,10 +1241,10 @@ @inproceedings{gay_optical_2006
   title = {Optical Response of Nanostructured Surfaces: Experimental Investigation of the Composite Diffracted Evanescent Wave Model},
   booktitle = {Integrated {{Optoelectronic Devices}} 2006},
   author = {Gay, G and Alloschery, O and de Lesegno, B Viaris and O'Dwyer, C and Lezec, H and Weiner, J},
+  options = {useprefix=true},
   date = {2006},
   pages = {61310J----61310J},
-  publisher = {{International Society for Optics and Photonics}},
-  options = {useprefix=true}
+  publisher = {{International Society for Optics and Photonics}}
 }
 
 @article{gay_optical_2006-2,
@@ -1253,8 +1253,8 @@ @article{gay_optical_2006-2
   date = {2006},
   journaltitle = {Nature Physics},
   volume = {2},
-  pages = {262--267},
-  number = {4}
+  number = {4},
+  pages = {262--267}
 }
 
 @article{gay_stochastic_2012,
@@ -1263,8 +1263,8 @@ @article{gay_stochastic_2012
   date = {2012},
   journaltitle = {The Journal of cell biology},
   volume = {196},
-  pages = {757--774},
-  number = {6}
+  number = {6},
+  pages = {757--774}
 }
 
 @article{gay_surface_2006,
@@ -1273,8 +1273,8 @@ @article{gay_surface_2006
   date = {2006},
   journaltitle = {Physical review letters},
   volume = {96},
-  pages = {213901},
-  number = {21}
+  number = {21},
+  pages = {213901}
 }
 
 @article{gay_surface_2007,
@@ -1283,8 +1283,8 @@ @article{gay_surface_2007
   date = {2007},
   journaltitle = {Physical Review E},
   volume = {75},
-  pages = {16612},
-  number = {1}
+  number = {1},
+  pages = {16612}
 }
 
 @article{ghaffarizadehPhysiCellOpenSource2017,
@@ -1299,8 +1299,8 @@ @article{ghaffarizadehPhysiCellOpenSource2017
   url = {https://www.biorxiv.org/content/10.1101/088773v4},
   urldate = {2021-02-09},
   abstract = {{$<$}h3{$>$}Abstract{$<$}/h3{$>$} {$<$}p{$>$}Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal “virtual laboratory” for such multicellular systems simulates both the biochemical microenvironment (the “stage”) and many mechanically and biochemically interacting cells (the “players” upon the stage).{$<$}/p{$><$}p{$>$}PhysiCell—physics-based multicellular simulator—is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility “out of the box.” The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 10\textsuperscript{5}-10\textsuperscript{6} cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes.{$<$}/p{$><$}p{$>$}We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a “cellular cargo delivery” system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net.{$<$}/p{$><$}h3{$>$}Author Summary{$<$}/h3{$>$} {$<$}p{$>$}This paper introduces PhysiCell: an open source, agent-based modeling framework for 3-D multicellular simulations. It includes a standard library of sub-models for cell fluid and solid volume changes, cycle progression, apoptosis, necrosis, mechanics, and motility. PhysiCell is directly coupled to a biotransport solver to simulate many diffusing substrates and cell-secreted signals. Each cell can dynamically update its phenotype based on its microenvironmental conditions. Users can customize or replace the included sub-models.{$<$}/p{$><$}p{$>$}PhysiCell runs on a variety of platforms (Linux, OSX, and Windows) with few software dependencies. Its computational cost scales linearly in the number of cells. It is feasible to simulate 500,000 cells on quad-core desktop workstations, and millions of cells on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on hanging drop tumor spheroids (HDS) and ductal carcinoma in situ (DCIS) of the breast. We demonstrate contact- and chemokine-based interactions among multiple cell types with examples in synthetic multicellular bioengineering, cancer heterogeneity, and cancer immunology.{$<$}/p{$><$}p{$>$}We developed PhysiCell to help the scientific community tackle multicellular systems biology problems involving many interacting cells in multi-substrate microenvironments. PhysiCell is also an independent, cross-platform codebase for replicating results from other simulators.{$<$}/p{$>$}},
-  file = {/home/guillaume/Zotero/storage/WRB5SKCE/Ghaffarizadeh et al. - 2017 - PhysiCell an Open Source Physics-Based Cell Simul.pdf;/home/guillaume/Zotero/storage/L5SYEZW6/088773v4.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/WRB5SKCE/Ghaffarizadeh et al. - 2017 - PhysiCell an Open Source Physics-Based Cell Simul.pdf;/home/guillaume/Zotero/storage/L5SYEZW6/088773v4.html}
 }
 
 @article{ghaffarizadehPhysiCellOpenSource2018,
@@ -1310,6 +1310,7 @@ @article{ghaffarizadehPhysiCellOpenSource2018
   date = {2018-02-23},
   journaltitle = {PLOS Computational Biology},
   volume = {14},
+  number = {2},
   pages = {e1005991},
   publisher = {{Public Library of Science}},
   issn = {1553-7358},
@@ -1317,10 +1318,9 @@ @article{ghaffarizadehPhysiCellOpenSource2018
   url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005991},
   urldate = {2021-02-09},
   abstract = {Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal “virtual laboratory” for such multicellular systems simulates both the biochemical microenvironment (the “stage”) and many mechanically and biochemically interacting cells (the “players” upon the stage). PhysiCell—physics-based multicellular simulator—is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility “out of the box.” The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a “cellular cargo delivery” system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net.},
-  file = {/home/guillaume/Zotero/storage/V53WZ8C7/Ghaffarizadeh et al. - 2018 - PhysiCell An open source physics-based cell simul.pdf;/home/guillaume/Zotero/storage/9IU9RRPT/article.html},
-  keywords = {Apoptosis,Biochemical simulations,Biophysical simulations,Cancers and neoplasms,Cell cycle and cell division,Cell motility,Malignant tumors,Necrosis},
   langid = {english},
-  number = {2}
+  keywords = {Apoptosis,Biochemical simulations,Biophysical simulations,Cancers and neoplasms,Cell cycle and cell division,Cell motility,Malignant tumors,Necrosis},
+  file = {/home/guillaume/Zotero/storage/V53WZ8C7/Ghaffarizadeh et al. - 2018 - PhysiCell An open source physics-based cell simul.pdf;/home/guillaume/Zotero/storage/9IU9RRPT/article.html}
 }
 
 @article{goldbergOpenMicroscopyEnvironment2005,
@@ -1330,17 +1330,17 @@ @article{goldbergOpenMicroscopyEnvironment2005
   date = {2005},
   journaltitle = {Genome Biology},
   volume = {6},
+  number = {5},
+  eprint = {15892875},
+  eprinttype = {pmid},
   pages = {R47},
   issn = {1474-760X},
   doi = {10.1186/gb-2005-6-5-r47},
   abstract = {The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. OME is designed to support high-content cell-based screening as well as traditional image analysis applications. The OME Data Model, expressed in Extensible Markup Language (XML) and realized in a traditional database, is both extensible and self-describing, allowing it to meet emerging imaging and analysis needs.},
-  eprint = {15892875},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/8VGB4VKN/Goldberg et al. - 2005 - The Open Microscopy Environment (OME) Data Model a.pdf},
-  keywords = {Computational Biology,Computer Simulation,Databases; Factual,Imaging; Three-Dimensional,Microscopy,Software,User-Computer Interface},
   langid = {english},
-  number = {5},
-  pmcid = {PMC1175959}
+  pmcid = {PMC1175959},
+  keywords = {Computational Biology,Computer Simulation,Databases; Factual,Imaging; Three-Dimensional,Microscopy,Software,User-Computer Interface},
+  file = {/home/guillaume/Zotero/storage/8VGB4VKN/Goldberg et al. - 2005 - The Open Microscopy Environment (OME) Data Model a.pdf}
 }
 
 @article{goldstone_tip1/clip-170_2010,
@@ -1349,8 +1349,8 @@ @article{goldstone_tip1/clip-170_2010
   date = {2010},
   journaltitle = {PloS one},
   volume = {5},
-  pages = {e10634},
-  number = {5}
+  number = {5},
+  pages = {e10634}
 }
 
 @incollection{gomezImageBasedSilico2017,
@@ -1363,9 +1363,9 @@ @incollection{gomezImageBasedSilico2017
   doi = {10.1002/9783527696130.ch12},
   url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9783527696130.ch12},
   urldate = {2018-10-12},
-  file = {/home/guillaume/Zotero/storage/7YMQ5HU9/9783527696130.html},
   isbn = {978-3-527-69613-0},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/7YMQ5HU9/9783527696130.html}
 }
 
 @article{goodsellAtomsCellsUsing2018,
@@ -1375,15 +1375,15 @@ @article{goodsellAtomsCellsUsing2018
   date = {2018-10-19},
   journaltitle = {Journal of Molecular Biology},
   volume = {430},
+  number = {21},
   pages = {3954--3968},
   issn = {0022-2836},
   doi = {10.1016/j.jmb.2018.06.009},
   url = {http://www.sciencedirect.com/science/article/pii/S0022283618305850},
   urldate = {2018-10-11},
   abstract = {Modeling and visualization of the cellular mesoscale, bridging the nanometer scale of molecules to the micrometer scale of cells, is being studied by an integrative approach. Data from structural biology, proteomics, and microscopy are combined to simulate the molecular structure of living cells. These cellular landscapes are used as research tools for hypothesis generation and testing, and to present visual narratives of the cellular context of molecular biology for dissemination, education, and outreach.},
-  file = {/home/guillaume/Zotero/storage/P8VUEJEX/goodsell2018.pdf;/home/guillaume/Zotero/storage/3LPZ8W4X/S0022283618305850.html},
   keywords = {cellular mesoscale,integrative structural biology,mesoscale modeling,molecular graphics,science education},
-  number = {21}
+  file = {/home/guillaume/Zotero/storage/P8VUEJEX/goodsell2018.pdf;/home/guillaume/Zotero/storage/3LPZ8W4X/S0022283618305850.html}
 }
 
 @article{goodwinBasalCellExtracellularMatrix2016,
@@ -1392,17 +1392,17 @@ @article{goodwinBasalCellExtracellularMatrix2016
   date = {2016-12-05},
   journaltitle = {Developmental Cell},
   volume = {39},
+  number = {5},
+  eprint = {27923121},
+  eprinttype = {pmid},
   pages = {611--625},
   issn = {1534-5807},
   doi = {10.1016/j.devcel.2016.11.003},
   url = {http://www.cell.com/developmental-cell/abstract/S1534-5807(16)30787-0},
   urldate = {2017-11-20},
   abstract = {Tissue morphogenesis requires force-generating mechanisms to organize cells into complex structures. Although many such mechanisms have been characterized, we know little about how forces are integrated across developing tissues. We provide evidence that integrin-mediated cell-extracellular matrix (ECM) adhesion modulates the transmission of apically generated tension during dorsal closure (DC) in Drosophila. Integrin-containing adhesive structures resembling focal adhesions were identified on the basal surface of the amnioserosa (AS), an extraembryonic epithelium essential for DC. Genetic modulation of integrin-mediated adhesion results in defective DC. Quantitative image analysis and laser ablation experiments reveal that basal cell-ECM adhesions provide resistance to apical cell displacements and force transmission between neighboring cells in the AS. Finally, we provide evidence for integrin-dependent force transmission to the AS substrate. Overall, we find that integrins regulate force transmission within and between cells, thereby playing an essential role in transmitting tension in developing tissues.},
-  eprint = {27923121},
-  eprinttype = {pmid},
-  keywords = {Animals,Animals; Genetically Modified,Biophysical Phenomena,Cell Adhesion,cell-ECM adhesion,dorsal closure,Drosophila,Drosophila Proteins,Extracellular Matrix,focal adhesions,Focal Adhesions,integrins,Integrins,Models; Biological,Morphogenesis,tissue mechanics},
   langid = {english},
-  number = {5}
+  keywords = {Animals,Animals; Genetically Modified,Biophysical Phenomena,Cell Adhesion,cell-ECM adhesion,dorsal closure,Drosophila,Drosophila Proteins,Extracellular Matrix,focal adhesions,Focal Adhesions,integrins,Integrins,Models; Biological,Morphogenesis,tissue mechanics}
 }
 
 @article{gorfinkiel_mechano-chemical_2013,
@@ -1411,14 +1411,14 @@ @article{gorfinkiel_mechano-chemical_2013
   date = {2013},
   journaltitle = {Biophysical Journal},
   volume = {104},
+  number = {1},
+  eprint = {23332051},
+  eprinttype = {pmid},
   pages = {1--3},
   issn = {00063495},
   doi = {10.1016/j.bpj.2012.11.3822},
   url = {http://dx.doi.org/10.1016/j.bpj.2012.11.3822},
-  eprint = {23332051},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/76QSMMZG/Gorfinkiel - 2013 - Mechano-chemical coupling drives cell area oscillations during morphogenesis.pdf},
-  number = {1}
+  file = {/home/guillaume/Zotero/storage/76QSMMZG/Gorfinkiel - 2013 - Mechano-chemical coupling drives cell area oscillations during morphogenesis.pdf}
 }
 
 @article{graciaMechanicalImpactEpithelial2019,
@@ -1427,15 +1427,15 @@ @article{graciaMechanicalImpactEpithelial2019
   date = {2019-07-04},
   journaltitle = {Nature Communications},
   volume = {10},
+  number = {1},
   pages = {2951},
   issn = {2041-1723},
   doi = {10.1038/s41467-019-10720-0},
   url = {https://www.nature.com/articles/s41467-019-10720-0},
   urldate = {2019-07-05},
   abstract = {Tissue folding is a critical process during developmental morphogenesis. Here, Gracia et al. use live imaging, laser ablation and in silico modelling to demonstrate that cells entering EMT generate orthogonal forces necessary to drive mesoderm invagination in Drosophila.},
-  file = {/home/guillaume/Zotero/storage/TFHZNG4S/Gracia et al. - 2019 - Mechanical impact of epithelial−mesenchymal transi.pdf;/home/guillaume/Zotero/storage/4H5NARIY/s41467-019-10720-0.html},
   langid = {english},
-  number = {1}
+  file = {/home/guillaume/Zotero/storage/TFHZNG4S/Gracia et al. - 2019 - Mechanical impact of epithelial−mesenchymal transi.pdf;/home/guillaume/Zotero/storage/4H5NARIY/s41467-019-10720-0.html}
 }
 
 @article{graner_simulation_1992,
@@ -1444,11 +1444,11 @@ @article{graner_simulation_1992
   date = {1992-09},
   journaltitle = {Physical Review Letters},
   volume = {69},
+  number = {13},
   pages = {2013--2016},
   issn = {0031-9007},
   doi = {10.1103/PhysRevLett.69.2013},
-  url = {http://link.aps.org/doi/10.1103/PhysRevLett.69.2013},
-  number = {13}
+  url = {http://link.aps.org/doi/10.1103/PhysRevLett.69.2013}
 }
 
 @article{guerreroNeuronalDifferentiationAffects2019,
@@ -1470,15 +1470,15 @@ @article{guillotMechanicsEpithelialTissue2013
   date = {2013-06-07},
   journaltitle = {Science},
   volume = {340},
+  number = {6137},
+  eprint = {23744939},
+  eprinttype = {pmid},
   pages = {1185--1189},
   issn = {1095-9203},
   doi = {10.1126/science.1235249},
   abstract = {Epithelia are robust tissues that support the structure of embryos and organs and serve as effective barriers against pathogens. Epithelia also chemically separate different physiological environments. These vital functions require tight association between cells through the assembly of junctions that mechanically stabilize the tissue. Remarkably, epithelia are also dynamic and can display a fluid behavior. Cells continuously die or divide, thereby allowing functional tissue homeostasis. Epithelial cells can change shape or intercalate as tissues deform during morphogenesis. We review the mechanical basis of tissue robustness and fluidity, with an emphasis on the pivotal role of junction dynamics. Tissue fluidity emerges from local active stresses acting at cell interfaces and allows the maintenance of epithelial organization during morphogenesis and tissue renewal.},
-  eprint = {23744939},
-  eprinttype = {pmid},
-  keywords = {Animals,Cadherins,Cell Division,Chick Embryo,Drosophila,Epithelial Cells,Epithelium,Homeostasis,Intercellular Junctions,Models; Biological,Morphogenesis,Neural Tube},
   langid = {english},
-  number = {6137}
+  keywords = {Animals,Cadherins,Cell Division,Chick Embryo,Drosophila,Epithelial Cells,Epithelium,Homeostasis,Intercellular Junctions,Models; Biological,Morphogenesis,Neural Tube}
 }
 
 @article{guirao_unified_2015,
@@ -1487,16 +1487,16 @@ @article{guirao_unified_2015
   date = {2015-12},
   journaltitle = {eLife},
   volume = {4},
+  eprint = {26653285},
+  eprinttype = {pmid},
   pages = {e08519},
   issn = {2050-084X},
   doi = {10.7554/eLife.08519},
   url = {http://elifesciences.org/content/4/e08519v3},
   abstract = {Understanding the mechanisms regulating development requires a quantitative characterization of cell divisions, rearrangements, cell size and shape changes, and apoptoses. We developed a multiscale formalism that relates the characterizations of each cell process to tissue growth and morphogenesis. Having validated the formalism on computer simulations, we quantifed separately all morphogenetic events in the Drosophila wing and dorsal thorax pupal epithelia to obtain comprehensive statistical maps linking cell and tissue scale dynamics. While globally cell shape changes, rearrangements and divisions all signifcantly participate in tissue morphogenesis, locally, their relative participations display major variations in space and time. By blocking division we analyzed the impact of division on rearrangements, cell shape changes and tissue morphogenesis. Finally, by combining the formalism with mechanical stress measurement, we evidenced unexpected interplays between patterns of tissue elongation, cell division and stress. Our formalism provides a novel and rigorous approach to uncover mechanisms governing tissue development.},
-  eprint = {26653285},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/ZQEI5JBI/Guirao et al. - 2015 - Unified quantitative characterization of epithelial tissue development.pdf},
+  langid = {english},
   keywords = {/italic&gt,&lt,apoptosis,biomechanic,cell division,cell dynamics,cell processes,cell rearrangements,cell shape changes,cellular material,D. melanogaster&lt,development,force inference,growth,italic&gt,Morphogenesis,tissue deformation,tissue dynamics,tissue mechanics},
-  langid = {english}
+  file = {/home/guillaume/Zotero/storage/ZQEI5JBI/Guirao et al. - 2015 - Unified quantitative characterization of epithelial tissue development.pdf}
 }
 
 @article{haganTenSimpleRules2020,
@@ -1505,16 +1505,16 @@ @article{haganTenSimpleRules2020
   date = {2020-08-27},
   journaltitle = {PLOS Computational Biology},
   volume = {16},
+  number = {8},
   pages = {e1008119},
   publisher = {{Public Library of Science}},
   issn = {1553-7358},
   doi = {10.1371/journal.pcbi.1008119},
   url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008119},
   urldate = {2020-09-03},
-  file = {/home/guillaume/Zotero/storage/ZLKERA52/Hagan et al. - 2020 - Ten simple rules to increase computational skills .pdf;/home/guillaume/Zotero/storage/FKEE4JHI/article.html},
-  keywords = {Biologists,Learning,Programming languages,Reproducibility,RNA sequencing,Scientists,Syntax,Workshops},
   langid = {english},
-  number = {8}
+  keywords = {Biologists,Learning,Programming languages,Reproducibility,RNA sequencing,Scientists,Syntax,Workshops},
+  file = {/home/guillaume/Zotero/storage/ZLKERA52/Hagan et al. - 2020 - Ten simple rules to increase computational skills .pdf;/home/guillaume/Zotero/storage/FKEE4JHI/article.html}
 }
 
 @article{hanEfficacyComputerizedOptokinetic2011,
@@ -1523,14 +1523,14 @@ @article{hanEfficacyComputerizedOptokinetic2011
   date = {2011-09-01},
   journaltitle = {Invest. Ophthalmol. Vis. Sci.},
   volume = {52},
+  number = {10},
   pages = {7492--7497},
   issn = {1552-5783},
   doi = {10.1167/iovs.11-7663},
   url = {https://iovs.arvojournals.org/article.aspx?articleid=2165689},
   urldate = {2019-03-13},
-  file = {/home/guillaume/Zotero/storage/7PILBY6B/Han et al. - 2011 - Efficacy of a Computerized Optokinetic Nystagmus T.pdf;/home/guillaume/Zotero/storage/T8MDJ2DE/article.html},
   langid = {english},
-  number = {10}
+  file = {/home/guillaume/Zotero/storage/7PILBY6B/Han et al. - 2011 - Efficacy of a Computerized Optokinetic Nystagmus T.pdf;/home/guillaume/Zotero/storage/T8MDJ2DE/article.html}
 }
 
 @article{hanMeasurementDistanceObjective2011,
@@ -1539,15 +1539,15 @@ @article{hanMeasurementDistanceObjective2011
   date = {2011-05-21},
   journaltitle = {Graefes Arch Clin Exp Ophthalmol},
   volume = {249},
+  number = {9},
   pages = {1379},
   issn = {1435-702X},
   doi = {10.1007/s00417-011-1705-x},
   url = {https://doi.org/10.1007/s00417-011-1705-x},
   urldate = {2019-03-13},
   abstract = {BackgroundsTo evaluate the efficacy of a computerized optokinetic nystagmus (OKN) test for determination of objective visual acuity (VA) at distance in patients with various ocular diseases.MethodsThis is a prospective, non-interventional study that included 85 eyes of 71 patients with one or more ocular pathologies. Study patients were classified into group C (39 eyes of 30 patients with central visual damage), group P (24 eyes of 20 patients with peripheral visual defect) and group M (22 eyes of 21 patients with media opacity). Objective distance VA was measured with OKN induction and suppression methods, and the correlation between the objective and subjective VA at distance was evaluated using linear regression analysis. Mean subjective VAs were compared among each objective VA step and among the three groups.ResultsSignificant correlation was found between subjective distance VA and objective VA determined by both OKN induction and suppression methods in all three groups and in overall patients. In overall patients, the mean subjective VA was significantly different in several objective VA steps (Welch’s ANOVA, p {$<$} 0.001 for induction and suppression methods). No significant difference in subjective VA among the three groups was found in any objective VA step.ConclusionsOur objective VA test using OKN induction and suppression methods can be useful in estimating distance VA in patients with various ocular diseases.},
-  keywords = {Objective visual acuity,Optokinetic nystagmus,Visual acuity},
   langid = {english},
-  number = {9}
+  keywords = {Objective visual acuity,Optokinetic nystagmus,Visual acuity}
 }
 
 @article{hannezoGrowthHomeostaticRegulation2014,
@@ -1556,16 +1556,16 @@ @article{hannezoGrowthHomeostaticRegulation2014
   date = {2014-04-06},
   journaltitle = {J R Soc Interface},
   volume = {11},
+  number = {93},
+  eprint = {24478279},
+  eprinttype = {pmid},
   pages = {20130895},
   issn = {1742-5662},
   doi = {10.1098/rsif.2013.0895},
   abstract = {The regulation of cell growth in animal tissues is a question of critical importance: most tissues contain different types of cells in interconversion and the fraction of each type has to be controlled in a precise way, by mechanisms that remain unclear. Here, we provide a theoretical framework for the homeostasis of stem-cell-containing epithelial tissues using mechanical equations, which describe the size of the tissue and kinetic equations, which describe the interconversions of the cell populations. We show that several features, such as the evolution of stem cell fractions during intestinal development, the shape of a developing intestinal wall, as well as the increase in the proliferative compartment in cancer initiation, can be studied and understood from generic modelling which does not rely on a particular regulatory mechanism. Finally, inspired by recent experiments, we propose a model where cell division rates are regulated by the mechanical stresses in the epithelial sheet. We show that pressure-controlled growth can, in addition to the previous features, also explain with few parameters the formation of stem cell compartments as well as the morphologies observed when a colonic crypt becomes cancerous. We also discuss optimal strategies of wound healing, in connection with experiments on the cornea.},
-  eprint = {24478279},
-  eprinttype = {pmid},
-  keywords = {Animals,Cell Division,crypt morphogenesis,growth dynamics,Homeostasis,Humans,Models; Biological,Neoplasms,Organ Specificity,stem cells,Tissue mechanics},
   langid = {english},
-  number = {93},
-  pmcid = {PMC3928929}
+  pmcid = {PMC3928929},
+  keywords = {Animals,Cell Division,crypt morphogenesis,growth dynamics,Homeostasis,Humans,Models; Biological,Neoplasms,Organ Specificity,stem cells,Tissue mechanics}
 }
 
 @article{hannezoUnifyingTheoryBranching2017,
@@ -1574,18 +1574,18 @@ @article{hannezoUnifyingTheoryBranching2017
   date = {2017-09-21},
   journaltitle = {Cell},
   volume = {171},
+  number = {1},
+  eprint = {28938116},
+  eprinttype = {pmid},
   pages = {242-255.e27},
   issn = {0092-8674, 1097-4172},
   doi = {10.1016/j.cell.2017.08.026},
   url = {http://www.cell.com/cell/abstract/S0092-8674(17)30951-0},
   urldate = {2017-09-26},
   abstract = {The morphogenesis of branched organs remains a subject of abiding interest. Although much is known about the underlying signaling pathways, it remains unclear how macroscopic features of branched organs, including their size, network topology, and spatial patterning, are encoded. Here, we show that, in mouse mammary gland, kidney, and human~prostate, these features can be explained quantitatively within a single unifying framework of branching and annihilating random walks. Based on quantitative analyses of large-scale organ reconstructions and proliferation kinetics measurements, we propose that morphogenesis follows from the proliferative activity of equipotent tips that stochastically branch and randomly explore their environment but compete neutrally for space, becoming proliferatively inactive when in proximity with neighboring ducts. These results show that complex branched epithelial structures develop as a self-organized process, reliant upon a strikingly simple but generic rule, without recourse to a rigid and deterministic sequence of genetically programmed events.},
-  eprint = {28938116},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/KCSNHMKX/Hannezo et al. - 2017 - A Unifying Theory of Branching Morphogenesis.pdf;/home/guillaume/Zotero/storage/WVBA3XHS/S0092-8674(17)30951-0.html},
-  keywords = {branching and annihilating random walks,branching morphogenesis,kidney,mammary gland,mathematical modeling,prostate,self-organization},
   langid = {english},
-  number = {1}
+  keywords = {branching and annihilating random walks,branching morphogenesis,kidney,mammary gland,mathematical modeling,prostate,self-organization},
+  file = {/home/guillaume/Zotero/storage/KCSNHMKX/Hannezo et al. - 2017 - A Unifying Theory of Branching Morphogenesis.pdf;/home/guillaume/Zotero/storage/WVBA3XHS/S0092-8674(17)30951-0.html}
 }
 
 @article{hanserPhaseretrievedPupilFunctions2004,
@@ -1594,17 +1594,17 @@ @article{hanserPhaseretrievedPupilFunctions2004
   date = {2004},
   journaltitle = {Journal of Microscopy},
   volume = {216},
+  number = {1},
   pages = {32--48},
   issn = {1365-2818},
   doi = {10.1111/j.0022-2720.2004.01393.x},
   url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.0022-2720.2004.01393.x},
   urldate = {2020-05-18},
   abstract = {Pupil functions are compact and modifiable descriptions of the three-dimensional (3D) imaging properties of wide-field optical systems. The pupil function of a microscope can be computationally estimated from the measured point spread function (PSF) using phase retrieval algorithms. The compaction of a 3D PSF into a 2D pupil function suppresses artefacts and measurement noise without resorting to rotational averaging. We show here that such ‘phase-retrieved’ pupil functions can reproduce features in the optical path, both near the sample and in the microscope. Unlike the PSF, the pupil function can be easily modified to include known aberrations, such as those induced by index-mismatched mounting media, simply by multiplying the pupil function by a calculated aberration function. PSFs calculated from such a modified pupil function closely match the corresponding measured PSFs collected under the aberrated imaging conditions. When used for image deconvolution of simulated objects, these phase-retrieved, calculated PSFs perform similarly to directly measured PSFs.},
-  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.0022-2720.2004.01393.x},
-  file = {/home/guillaume/Zotero/storage/XS9FIFSR/j.0022-2720.2004.01393.html},
-  keywords = {Deconvolution,fluorescence microscopy,phase retrieval,PSF,spherical aberration},
   langid = {english},
-  number = {1}
+  keywords = {Deconvolution,fluorescence microscopy,phase retrieval,PSF,spherical aberration},
+  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.0022-2720.2004.01393.x},
+  file = {/home/guillaume/Zotero/storage/XS9FIFSR/j.0022-2720.2004.01393.html}
 }
 
 @article{hayPyOmeroUploadPythonToolkit2020,
@@ -1614,16 +1614,16 @@ @article{hayPyOmeroUploadPythonToolkit2020
   date = {2020},
   journaltitle = {Wellcome Open Research},
   volume = {5},
+  eprint = {32766455},
+  eprinttype = {pmid},
   pages = {96},
   issn = {2398-502X},
   doi = {10.12688/wellcomeopenres.15853.2},
   abstract = {Tools and software that automate repetitive tasks, such as metadata extraction and deposition to data repositories, are essential for researchers to share Open Data, routinely. For research that generates microscopy image data, OMERO is an ideal platform for storage, annotation and publication according to open research principles. We present PyOmeroUpload, a Python toolkit for automatically extracting metadata from experiment logs and text files, processing images and uploading these payloads to OMERO servers to create fully annotated, multidimensional datasets. The toolkit comes packaged in portable, platform-independent Docker images that enable users to deploy and run the utilities easily, regardless of Operating System constraints. A selection of use cases is provided, illustrating the primary capabilities and flexibility offered with the toolkit, along with a discussion of limitations and potential future extensions. PyOmeroUpload is available from: https://github.com/SynthSys/pyOmeroUpload.},
-  eprint = {32766455},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/Y73D4CD2/Hay et al. - 2020 - PyOmeroUpload A Python toolkit for uploading imag.pdf},
-  keywords = {Data sharing,Docker,metadata,microscopy,OMERO,research data management},
   langid = {english},
-  pmcid = {PMC7388197.2}
+  pmcid = {PMC7388197.2},
+  keywords = {Data sharing,Docker,metadata,microscopy,OMERO,research data management},
+  file = {/home/guillaume/Zotero/storage/Y73D4CD2/Hay et al. - 2020 - PyOmeroUpload A Python toolkit for uploading imag.pdf}
 }
 
 @article{heerActomyosinbasedTissueFolding2017,
@@ -1632,17 +1632,17 @@ @article{heerActomyosinbasedTissueFolding2017
   date = {2017-05-15},
   journaltitle = {Development},
   volume = {144},
+  number = {10},
+  eprint = {28432215},
+  eprinttype = {pmid},
   pages = {1876--1886},
   issn = {0950-1991, 1477-9129},
   doi = {10.1242/dev.146761},
   url = {http://dev.biologists.org/content/144/10/1876},
   urldate = {2017-05-29},
   abstract = {Skip to Next Section Tissue folding promotes three-dimensional (3D) form during development. In many cases, folding is associated with myosin accumulation at the apical surface of epithelial cells, as seen in the vertebrate neural tube and the Drosophila ventral furrow. This type of folding is characterized by constriction of apical cell surfaces, and the resulting cell shape change is thought to cause tissue folding. Here, we use quantitative microscopy to measure the pattern of transcription, signaling, myosin activation and cell shape in the Drosophila mesoderm. We found that cells within the ventral domain accumulate different amounts of active apical non-muscle myosin 2 depending on the distance from the ventral midline. This gradient in active myosin depends on a newly quantified gradient in upstream signaling proteins. A 3D continuum model of the embryo with induced contractility demonstrates that contractility gradients, but not contractility per se, promote changes to surface curvature and folding. As predicted by the model, experimental broadening of the myosin domain in vivo disrupts tissue curvature where myosin is uniform. Our data argue that apical contractility gradients are important for tissue folding.},
-  eprint = {28432215},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/BF8688H3/1876.html},
   langid = {english},
-  number = {10}
+  file = {/home/guillaume/Zotero/storage/BF8688H3/1876.html}
 }
 
 @article{heerTensionContractionTissue2017,
@@ -1651,16 +1651,16 @@ @article{heerTensionContractionTissue2017
   date = {2017-12-01},
   journaltitle = {Development},
   volume = {144},
+  number = {23},
+  eprint = {29183938},
+  eprinttype = {pmid},
   pages = {4249--4260},
   issn = {1477-9129},
   doi = {10.1242/dev.151282},
   abstract = {D'Arcy Thompson was a proponent of applying mathematical and physical principles to biological systems, an approach that is becoming increasingly common in developmental biology. Indeed, the recent integration of quantitative experimental data, force measurements and mathematical modeling has changed our understanding of morphogenesis - the shaping of an organism during development. Emerging evidence suggests that the subcellular organization of contractile cytoskeletal networks plays a key role in force generation, while on the tissue level the spatial organization of forces determines the morphogenetic output. Inspired by D'Arcy Thompson'sOn Growth and Form, we review our current understanding of how biological forms are created and maintained by the generation and organization of contractile forces at the cell and tissue levels. We focus on recent advances in our understanding of how cells actively sculpt tissues and how forces are involved in specific morphogenetic processes.},
-  eprint = {29183938},
-  eprinttype = {pmid},
-  keywords = {Actin,Actins,Animals,Biomechanical Phenomena,Cell Movement,Contractility,Epithelial Cells,Humans,Intercellular Junctions,Models; Biological,Molecular Motor Proteins,Morphogenesis,Muscle Contraction,Myosin,Myosins,Tension},
   langid = {english},
-  number = {23},
-  pmcid = {PMC5769629}
+  pmcid = {PMC5769629},
+  keywords = {Actin,Actins,Animals,Biomechanical Phenomena,Cell Movement,Contractility,Epithelial Cells,Humans,Intercellular Junctions,Models; Biological,Molecular Motor Proteins,Morphogenesis,Muscle Contraction,Myosin,Myosins,Tension}
 }
 
 @article{heisenbergForcesTissueMorphogenesis2013,
@@ -1669,15 +1669,15 @@ @article{heisenbergForcesTissueMorphogenesis2013
   date = {2013-05-23},
   journaltitle = {Cell},
   volume = {153},
+  number = {5},
+  eprint = {23706734},
+  eprinttype = {pmid},
   pages = {948--962},
   issn = {1097-4172},
   doi = {10.1016/j.cell.2013.05.008},
   abstract = {During development, mechanical forces cause changes in size, shape, number, position, and gene expression of cells. They are therefore integral to any morphogenetic processes. Force generation by actin-myosin networks and force transmission through adhesive complexes are two self-organizing phenomena driving tissue morphogenesis. Coordination and integration of forces by long-range force transmission and mechanosensing of cells within tissues produce large-scale tissue shape changes. Extrinsic mechanical forces also control tissue patterning by modulating cell fate specification and differentiation. Thus, the interplay between tissue mechanics and biochemical signaling orchestrates tissue morphogenesis and patterning in development.},
-  eprint = {23706734},
-  eprinttype = {pmid},
-  keywords = {Actins,Animals,Biomechanical Phenomena,Cell Shape,Morphogenesis,Myosins,Signal Transduction},
   langid = {english},
-  number = {5}
+  keywords = {Actins,Animals,Biomechanical Phenomena,Cell Shape,Morphogenesis,Myosins,Signal Transduction}
 }
 
 @article{heller_epitools:_2016,
@@ -1686,12 +1686,12 @@ @article{heller_epitools:_2016
   date = {2016},
   journaltitle = {Developmental Cell},
   volume = {36},
-  issn = {18781551},
-  doi = {10.1016/j.devcel.2015.12.012},
-  abstract = {Epithelia grow and undergo extensive rearrangements to achieve their final size and shape. Imaging the dynamics of tissue growth and morphogenesis is now possible with advances in time-lapse microscopy, but a true understanding of their complexities is limited by automated image analysis tools to extract quantitative data. To overcome such limitations, we have designed a new open-source image analysis toolkit called EpiTools. It provides user-friendly graphical user interfaces for accurately segmenting and tracking the contours of cell membrane signals obtained from 4D confocal imaging. It is designed for a broad audience, especially biologists with no computer-science background. Quantitative data extraction is integrated into a larger bioimaging platform, Icy, to increase the visibility and usability of our tools. We demonstrate the usefulness of EpiTools by analyzing Drosophila wing imaginal disc growth, revealing previously overlooked properties of this dynamic tissue, such as the patterns of cellular rearrangements.},
+  number = {1},
   eprint = {26766446},
   eprinttype = {pmid},
-  number = {1}
+  issn = {18781551},
+  doi = {10.1016/j.devcel.2015.12.012},
+  abstract = {Epithelia grow and undergo extensive rearrangements to achieve their final size and shape. Imaging the dynamics of tissue growth and morphogenesis is now possible with advances in time-lapse microscopy, but a true understanding of their complexities is limited by automated image analysis tools to extract quantitative data. To overcome such limitations, we have designed a new open-source image analysis toolkit called EpiTools. It provides user-friendly graphical user interfaces for accurately segmenting and tracking the contours of cell membrane signals obtained from 4D confocal imaging. It is designed for a broad audience, especially biologists with no computer-science background. Quantitative data extraction is integrated into a larger bioimaging platform, Icy, to increase the visibility and usability of our tools. We demonstrate the usefulness of EpiTools by analyzing Drosophila wing imaginal disc growth, revealing previously overlooked properties of this dynamic tissue, such as the patterns of cellular rearrangements.}
 }
 
 @article{heller_tissue_2015,
@@ -1700,14 +1700,14 @@ @article{heller_tissue_2015
   date = {2015},
   journaltitle = {Journal of Cell Biology},
   volume = {211},
+  number = {2},
+  eprint = {26504164},
+  eprinttype = {pmid},
   pages = {219--231},
   issn = {0021-9525},
   doi = {10.1083/jcb.201506106},
   abstract = {In development, cells organize into biological tissues through cell growth, migration, and differentiation. Glob- ally, this process is dictated by a genetically encoded program in which secreted morphogens and cell–cell interactions prompt the adoption of unique cell fates. Yet, at its lowest level, development is achieved through the modification of cell–cell adhesion and actomyosin-based contractility, which set the level of tension within cells and dictate how they pack together into tissues. The regulation of tension within individual cells and across large groups of cells is a major driving force of tissue organization and the basis of all cell shape change and cell movement in development.},
-  eprint = {26504164},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/KW9TBKS4/J Cell Biol-2015-Heller-219-31.pdf},
-  number = {2}
+  file = {/home/guillaume/Zotero/storage/KW9TBKS4/J Cell Biol-2015-Heller-219-31.pdf}
 }
 
 @article{hersztergInterplayDividingCell2013,
@@ -1716,15 +1716,15 @@ @article{hersztergInterplayDividingCell2013
   date = {2013-02-11},
   journaltitle = {Dev. Cell},
   volume = {24},
+  number = {3},
+  eprint = {23410940},
+  eprinttype = {pmid},
   pages = {256--270},
   issn = {1878-1551},
   doi = {10.1016/j.devcel.2012.11.019},
   abstract = {How adherens junctions (AJs) are formed upon cell division is largely unexplored. Here, we found that AJ formation is coordinated with cytokinesis and relies on an interplay between the dividing cell and its neighbors. During contraction of the cytokinetic ring, the neighboring cells locally accumulate Myosin II and produce the cortical tension necessary to set the initial geometry of the daughter cell interface. However, the neighboring cell membranes impede AJ formation. Upon midbody formation and concomitantly to neighboring cell withdrawal, Arp2/3-dependent actin polymerization oriented by the midbody maintains AJ geometry and regulates AJ final length and the epithelial cell arrangement upon division. We propose that cytokinesis in epithelia is a multicellular process, whereby the cooperative actions of the dividing cell and its neighbors define a two-tiered mechanism that spatially and temporally controls AJ formation while maintaining tissue cohesiveness.},
-  eprint = {23410940},
-  eprinttype = {pmid},
-  keywords = {Actin Cytoskeleton,Adherens Junctions,Animals,Cadherins,Cell Adhesion,Cell Communication,Cell Division,Cell Line,Cell Membrane,Cell Polarity,Cytokinesis,Drosophila melanogaster,Epithelium,Thorax},
   langid = {english},
-  number = {3}
+  keywords = {Actin Cytoskeleton,Adherens Junctions,Animals,Cadherins,Cell Adhesion,Cell Communication,Cell Division,Cell Line,Cell Membrane,Cell Polarity,Cytokinesis,Drosophila melanogaster,Epithelium,Thorax}
 }
 
 @article{hiraokaQuantitativeOpticalMicroscopy,
@@ -1732,8 +1732,8 @@ @article{hiraokaQuantitativeOpticalMicroscopy
   author = {HIRAoKA, YASUSHI and Sedat, John W and Agard, David A},
   volume = {238},
   pages = {7},
-  file = {/home/guillaume/Zotero/storage/8P3AIVMT/HIRAoKA et al. - for Quantitative Optical Microscopy of Biological .pdf},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/8P3AIVMT/HIRAoKA et al. - for Quantitative Optical Microscopy of Biological .pdf}
 }
 
 @article{hiraokaUseChargecoupledDevice1987,
@@ -1742,6 +1742,9 @@ @article{hiraokaUseChargecoupledDevice1987
   date = {1987-10-02},
   journaltitle = {Science},
   volume = {238},
+  number = {4823},
+  eprint = {3116667},
+  eprinttype = {pmid},
   pages = {36--41},
   publisher = {{American Association for the Advancement of Science}},
   issn = {0036-8075, 1095-9203},
@@ -1749,11 +1752,8 @@ @article{hiraokaUseChargecoupledDevice1987
   url = {http://science.sciencemag.org/content/238/4823/36},
   urldate = {2020-10-05},
   abstract = {{$<$}p{$>$}The properties of a charge-coupled device (CCD) and its application to the high-resolution analysis of biological structures by optical microscopy are described. The CCD, with its high resolution, high sensitivity, wide dynamic range, photometric accuracy, and geometric stability, can provide data of such high quality that quantitative analysis on two- and three-dimensional microscopic images is possible. For example, the three-dimensional imaging properties of an epifluorescence microscope have been quantitatively determined with the CCD. This description of the imaging properties of the microscope, and the high-quality image data provided by the CCD, allow sophisticated computational image processing methods to be used that greatly improve the effective resolution obtainable for biological structures. Image processing techniques revealed fine substructures in Drosophila embryonic diploid chromosomes in two and three dimensions. The same approach can be extended to structures as small as yeast chromosomes or to other problems in structural cell biology.{$<$}/p{$>$}},
-  eprint = {3116667},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/RC9N6E7G/Hiraoka et al. - 1987 - The use of a charge-coupled device for quantitativ.pdf;/home/guillaume/Zotero/storage/KT7WKBF8/tab-pdf.html},
   langid = {english},
-  number = {4823}
+  file = {/home/guillaume/Zotero/storage/RC9N6E7G/Hiraoka et al. - 1987 - The use of a charge-coupled device for quantitativ.pdf;/home/guillaume/Zotero/storage/KT7WKBF8/tab-pdf.html}
 }
 
 @article{hirashimaAnisotropicCellularMechanoresponse2017,
@@ -1766,8 +1766,8 @@ @article{hirashimaAnisotropicCellularMechanoresponse2017
   url = {https://www.biorxiv.org/content/early/2017/11/15/172916},
   urldate = {2017-11-16},
   abstract = {Cellular behaviors responding to mechanical forces control the size of multicellular tissues as demonstrated in isotropic size maintenance of developing tissues. However, how mechanoresponse systems work to maintain anisotropic tissue size including tube radial size remains unknown. Here we reveal the system underlying radial size maintenance of the murine epididymal tubule by combining quantitative imaging, mathematical modeling, and mechanical perturbations. We found that an oriented cell intercalation making the tubule radial size smaller counteracts a cell tension reduction due to neighbor cell division along the tubule circumferential axis. Moreover, we demonstrated that the tubule cells enhance actomyosin constriction driving the cell intercalation in response to mechanical forces anisotropically applied on the cells. Our results suggest that epididymal tubule cells have endogenous systems for responding as active cell movement to mechanical forces exclusively along the circumferential axis, and the anisotropic cellular mechanoresponse spontaneously controls the tubule radial size.},
-  file = {/home/guillaume/Zotero/storage/H5GXA64L/Hirashima et Adachi - 2017 - Anisotropic Cellular Mechanoresponse for Radial Si.pdf;/home/guillaume/Zotero/storage/F5LCECR8/172916.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/H5GXA64L/Hirashima et Adachi - 2017 - Anisotropic Cellular Mechanoresponse for Radial Si.pdf;/home/guillaume/Zotero/storage/F5LCECR8/172916.html}
 }
 
 @article{hirashimaAnisotropicCellularMechanoresponse2017a,
@@ -1780,8 +1780,8 @@ @article{hirashimaAnisotropicCellularMechanoresponse2017a
   url = {http://www.biorxiv.org/content/early/2017/08/05/172916},
   urldate = {2017-09-05},
   abstract = {Cellular behaviors responding to mechanical forces control the size of multicellular tissues as demonstrated in isotropic size maintenance of developing tissues. However, how mechanoresponse systems work to maintain anisotropic tissue size including tube radial size remains unknown. Here we reveal the system underlying radial size maintenance of the murine epididymal tubule by combining quantitative imaging, mathematical modeling, and mechanical perturbations. We found that an oriented cell intercalation making the tubule radial size smaller counteracts a cell tension reduction due to neighbor cell division along the tubule circumferential axis. Moreover, we demonstrated that the tubule cells enhance actomyosin constriction driving the cell intercalation in response to mechanical forces anisotropically applied on the cells. Our results suggest that epididymal tubule cells have endogenous systems for responding as active cell movement to mechanical forces exclusively along the circumferential axis, and the anisotropic cellular mechanoresponse spontaneously controls the tubule radial size.},
-  file = {/home/guillaume/Zotero/storage/VDTW3EMD/Hirashima et Adachi - 2017 - Anisotropic Cellular Mechanoresponse for Radial Si.pdf;/home/guillaume/Zotero/storage/DXZC5RX2/172916.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/VDTW3EMD/Hirashima et Adachi - 2017 - Anisotropic Cellular Mechanoresponse for Radial Si.pdf;/home/guillaume/Zotero/storage/DXZC5RX2/172916.html}
 }
 
 @article{hoehmeCellbasedSimulationSoftware2010,
@@ -1790,17 +1790,17 @@ @article{hoehmeCellbasedSimulationSoftware2010
   date = {2010-10-15},
   journaltitle = {Bioinformatics},
   volume = {26},
+  number = {20},
+  eprint = {20709692},
+  eprinttype = {pmid},
   pages = {2641--2642},
   issn = {1367-4811},
   doi = {10.1093/bioinformatics/btq437},
   abstract = {CellSys is a modular software tool for efficient off-lattice simulation of growth and organization processes in multi-cellular systems in 2D and 3D. It implements an agent-based model that approximates cells as isotropic, elastic and adhesive objects. Cell migration is modeled by an equation of motion for each cell. The software includes many modules specifically tailored to support the simulation and analysis of virtual tissues including real-time 3D visualization and VRML 2.0 support. All cell and environment parameters can be independently varied which facilitates species specific simulations and allows for detailed analyses of growth dynamics and links between cellular and multi-cellular phenotypes. AVAILABILITY: CellSys is freely available for non-commercial use at http://msysbio.com/software/cellsys. The current version of CellSys permits the simulation of growing monolayer cultures and avascular tumor spheroids in liquid environment. Further functionality will be made available ongoing with published papers. CONTACT: hoehme@izbi.uni-leipzig.de; dirk.drasdo@inria.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
-  eprint = {20709692},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/G4KGD62T/Hoehme et Drasdo - 2010 - A cell-based simulation software for multi-cellula.pdf},
-  keywords = {Cell Movement,Cells,Computer Simulation,Phenotype,Software,User-Computer Interface},
   langid = {english},
-  number = {20},
-  pmcid = {PMC2951083}
+  pmcid = {PMC2951083},
+  keywords = {Cell Movement,Cells,Computer Simulation,Phenotype,Software,User-Computer Interface},
+  file = {/home/guillaume/Zotero/storage/G4KGD62T/Hoehme et Drasdo - 2010 - A cell-based simulation software for multi-cellula.pdf}
 }
 
 @article{hondaThreedimensionalVertexDynamics2004,
@@ -1809,15 +1809,15 @@ @article{hondaThreedimensionalVertexDynamics2004
   date = {2004-02-21},
   journaltitle = {J. Theor. Biol.},
   volume = {226},
+  number = {4},
+  eprint = {14759650},
+  eprinttype = {pmid},
   pages = {439--453},
   issn = {0022-5193},
   doi = {10.1016/j.jtbi.2003.10.001},
   abstract = {We developed a three-dimensional (3D) cell model of a multicellular aggregate consisting of several polyhedral cells to investigate the deformation and rearrangement of cells under the influence of external forces. The polyhedral cells fill the space in the aggregate without gaps or overlaps, consist of contracting interfaces and maintain their volumes. The interfaces and volumes were expressed by 3D vertex coordinates. Vertex movements obey equations of motion that rearrange the cells to minimize total free energy, and undergo an elementary process that exchanges vertex pair connections when vertices approach each other. The total free energy includes the interface energy of cells and the compression or expansion energy of cells. Computer simulations provided the following results: An aggregate of cells becomes spherical to minimize individual cell surface areas; Polygonal interfaces of cells remain flat; Cells within the 3D cell aggregate can move and rearrange despite the absence of free space. We examined cell rearrangement to elucidate the viscoelastic properties of the aggregate, e.g. when an external force flattens a cell aggregate (e.g. under centrifugation) its component cells quickly flatten. Under a continuous external force, the cells slowly rearrange to recover their original shape although the cell aggregate remains flat. The deformation and rearrangement of individual cells is a two-step process with a time lag. Our results showed that morphological and viscoelastic properties of the cell aggregate with long relaxation time are based on component cells where minimization of interfacial energy of cells provides a motive force for cell movement.},
-  eprint = {14759650},
-  eprinttype = {pmid},
-  keywords = {Animals,Cell Aggregation,Cell Movement,Computer Simulation,Elasticity,Models; Biological,Viscosity},
   langid = {english},
-  number = {4}
+  keywords = {Animals,Cell Aggregation,Cell Movement,Computer Simulation,Elasticity,Models; Biological,Viscosity}
 }
 
 @online{huismanMinimumInformationGuidelines2020,
@@ -1825,15 +1825,15 @@ @online{huismanMinimumInformationGuidelines2020
   shorttitle = {Minimum {{Information}} Guidelines for Fluorescence Microscopy},
   author = {Huisman, Maximiliaan and Hammer, Mathias and Rigano, Alex and Farzam, Farzin and Gopinathan, Renu and Smith, Carlas and Grunwald, David and Strambio-De-Castillia, Caterina},
   date = {2020-05-14},
+  eprint = {1910.11370},
+  eprinttype = {arxiv},
+  primaryclass = {cs, q-bio},
   url = {http://arxiv.org/abs/1910.11370},
   urldate = {2020-10-02},
   abstract = {High-resolution digital microscopy provides powerful tools for probing the real-time dynamics of subcellular structures, and adequate record-keeping is necessary to evaluate results, share data, and allow experiments to be repeated. In addition to advances in microscopic techniques, post-acquisition procedures such as image-data processing and analysis are often required for the reproducible and quantitative interpretation of images. While these techniques increase the usefulness of microscopy data, the limits to which quantitative results may be interpreted are often poorly quantified and documented. Keeping notes on microscopy experiments and calibration procedures should be relatively unchallenging, as the microscope is a machine whose performance should be easy to assess. Nevertheless, to this date, no widely adopted data provenance and quality control metadata guidelines to be recorded or published with imaging data exist. Metadata automatically recorded by microscopes from different companies vary widely and pose a substantial challenge for microscope users to create a good faith record of their work. Similarly, the complexity and aim of experiments using microscopes vary, leading to different reporting and quality control requirements from the simple description of a sample to the need to document the complexities of sub-diffraction resolution imaging in living cells and beyond. To solve this problem, the 4DN Imaging Standards Working Group has put forth a tiered system of microscopy calibration and metadata standards for images obtained through fluorescence microscopy. The proposal is an extension of the OME data model and aims at increasing data fidelity, ease future analysis, and facilitate objective comparison of different datasets, experimental setups, and essays.},
   archiveprefix = {arXiv},
-  eprint = {1910.11370},
-  eprinttype = {arxiv},
-  file = {/home/guillaume/Zotero/storage/PNRGESSC/Huisman et al. - 2020 - Minimum Information guidelines for fluorescence mi.pdf;/home/guillaume/Zotero/storage/9K4KIZVS/1910.html},
   keywords = {Computer Science - Databases,Quantitative Biology - Quantitative Methods},
-  primaryclass = {cs, q-bio}
+  file = {/home/guillaume/Zotero/storage/PNRGESSC/Huisman et al. - 2020 - Minimum Information guidelines for fluorescence mi.pdf;/home/guillaume/Zotero/storage/9K4KIZVS/1910.html}
 }
 
 @article{hyonObjectiveMeasurementDistance2010,
@@ -1842,16 +1842,16 @@ @article{hyonObjectiveMeasurementDistance2010
   date = {2010-02},
   journaltitle = {Invest. Ophthalmol. Vis. Sci.},
   volume = {51},
+  number = {2},
+  eprint = {19834033},
+  eprinttype = {pmid},
   pages = {752--757},
   issn = {1552-5783},
   doi = {10.1167/iovs.09-4362},
   abstract = {PURPOSE: To investigate the efficacy of a computerized optokinetic nystagmus (OKN) test in evaluating the objective distance visual acuity and to determine the correlation between subjective and objective visual acuities. METHODS: This prospective, noninterventional study included 83 eyes of 83 volunteers. Objective visual acuity was defined as the smallest size stripe that evoked the OKN response (induction method) or as the smallest dot size that suppressed the OKN response (suppression method). Distance visual acuity was measured by computerized OKN and infrared oculography at distance. The reproducibility of the test was evaluated by intraclass correlation coefficient (ICC). The correlation between measured objective and subjective visual acuity was then evaluated with linear regression analysis. Subjects were grouped according to their objective visual acuity, and the mean subjective visual acuities were compared with those of the objective visual acuity groups. RESULTS: There was a significant correlation between distance objective and subjective visual acuity (correlation coefficient R(2), induction method:suppression method = 0.566:0.832, P {$<$} 0.05). The mean subjective visual acuity was significantly different in the objective visual acuity groups (Welch's ANOVA, P = 0.000 for induction and suppression methods). The objective visual acuity test showed good reproducibility (ICC; induction method:suppression method = 0.945:0.988, P {$<$} 0.05). CONCLUSIONS: The computerized OKN test could serve as an objective and reliable tool for assessing distance visual acuity.},
-  eprint = {19834033},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/78PR87GL/Hyon et al. - 2010 - Objective measurement of distance visual acuity de.pdf},
-  keywords = {Adult,Aged,Diagnosis; Computer-Assisted,Distance Perception,Female,Humans,Male,Middle Aged,Nystagmus; Optokinetic,Prospective Studies,Reproducibility of Results,Vision Tests,Visual Acuity,Young Adult},
   langid = {english},
-  number = {2}
+  keywords = {Adult,Aged,Diagnosis; Computer-Assisted,Distance Perception,Female,Humans,Male,Middle Aged,Nystagmus; Optokinetic,Prospective Studies,Reproducibility of Results,Vision Tests,Visual Acuity,Young Adult},
+  file = {/home/guillaume/Zotero/storage/78PR87GL/Hyon et al. - 2010 - Objective measurement of distance visual acuity de.pdf}
 }
 
 @online{IncidentsFramasoft,
@@ -1868,11 +1868,11 @@ @article{isabella_rab10-mediated_2016
   date = {2016-07},
   journaltitle = {Developmental Cell},
   volume = {38},
+  number = {1},
   pages = {47--60},
   issn = {15345807},
   doi = {10.1016/j.devcel.2016.06.009},
-  url = {http://linkinghub.elsevier.com/retrieve/pii/S1534580716303781},
-  number = {1}
+  url = {http://linkinghub.elsevier.com/retrieve/pii/S1534580716303781}
 }
 
 @article{ishiharaComparativeStudyNoninvasive2013,
@@ -1881,16 +1881,16 @@ @article{ishiharaComparativeStudyNoninvasive2013
   date = {2013-04},
   journaltitle = {Eur Phys J E Soft Matter},
   volume = {36},
+  number = {4},
+  eprint = {23615875},
+  eprinttype = {pmid},
   pages = {9859},
   issn = {1292-895X},
   doi = {10.1140/epje/i2013-13045-8},
   abstract = {In the course of animal development, the shape of tissue emerges in part from mechanical and biochemical interactions between cells. Measuring stress in tissue is essential for studying morphogenesis and its physical constraints. For that purpose, a possible new approach is force inference (up to a single prefactor) from cell shapes and connectivity. It is non-invasive and can provide space-time maps of stress in a whole tissue, unlike existing methods. To validate this approach, three force-inference methods, which differ in their approach of treating indefiniteness in an inverse problem between cell shapes and forces, were compared. Tests using two artificial and two experimental data sets consistently indicate that our Bayesian force inference, by which cell-junction tensions and cell pressures are simultaneously estimated, performs best in terms of accuracy and robustness. Moreover, by measuring the stress anisotropy and relaxation, we cross-validated the force inference and the global annular ablation of tissue, each of which relies on different prefactors. A practical choice of force-inference methods in different systems of interest is discussed.},
-  eprint = {23615875},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/BJ6MCMCF/ishihara2013.pdf},
-  keywords = {Animals,Bayes Theorem,Biomechanical Phenomena,Cell Shape,Drosophila melanogaster,Epithelium,Image Processing; Computer-Assisted,Models; Biological,Pressure,Stress; Mechanical,Wings; Animal},
   langid = {english},
-  number = {4}
+  keywords = {Animals,Bayes Theorem,Biomechanical Phenomena,Cell Shape,Drosophila melanogaster,Epithelium,Image Processing; Computer-Assisted,Models; Biological,Pressure,Stress; Mechanical,Wings; Animal},
+  file = {/home/guillaume/Zotero/storage/BJ6MCMCF/ishihara2013.pdf}
 }
 
 @article{janesDatadrivenModellingSignaltransduction2006,
@@ -1899,12 +1899,12 @@ @article{janesDatadrivenModellingSignaltransduction2006
   date = {2006-11},
   journaltitle = {Nature Reviews Molecular Cell Biology},
   volume = {7},
+  number = {11},
   pages = {820--828},
   issn = {1471-0072, 1471-0080},
   doi = {10.1038/nrm2041},
   url = {http://www.nature.com/doifinder/10.1038/nrm2041},
-  urldate = {2016-11-02},
-  number = {11}
+  urldate = {2016-11-02}
 }
 
 @article{jaqamanLinkingDataModels2006,
@@ -1914,12 +1914,12 @@ @article{jaqamanLinkingDataModels2006
   date = {2006-11},
   journaltitle = {Nature Reviews Molecular Cell Biology},
   volume = {7},
+  number = {11},
   pages = {813--819},
   issn = {1471-0072, 1471-0080},
   doi = {10.1038/nrm2030},
   url = {http://www.nature.com/doifinder/10.1038/nrm2030},
-  urldate = {2016-11-02},
-  number = {11}
+  urldate = {2016-11-02}
 }
 
 @article{jayadev_tissue_2016,
@@ -1928,25 +1928,25 @@ @article{jayadev_tissue_2016
   date = {2016-07},
   journaltitle = {Developmental Cell},
   volume = {38},
+  number = {1},
   pages = {1--3},
   issn = {15345807},
   doi = {10.1016/j.devcel.2016.06.028},
-  url = {http://linkinghub.elsevier.com/retrieve/pii/S1534580716304312},
-  number = {1}
+  url = {http://linkinghub.elsevier.com/retrieve/pii/S1534580716304312}
 }
 
 @online{jensenForceNetworksTorque2019,
   title = {Force Networks, Torque Balance and {{Airy}} Stress in the Planar Vertex Model of a Confluent Epithelium},
   author = {Jensen, Oliver E. and Johns, Emma and Woolner, Sarah},
   date = {2019-10-23},
+  eprint = {1910.10799},
+  eprinttype = {arxiv},
+  primaryclass = {cond-mat, physics:physics, q-bio},
   url = {http://arxiv.org/abs/1910.10799},
   urldate = {2019-10-25},
   abstract = {The vertex model is a popular framework for modelling tightly packed biological cells, such as confluent epithelia. Cells are described by convex polygons tiling the plane and their equilibrium is found by minimizing a global mechanical energy, with vertex locations treated as degrees of freedom. Drawing on analogies with granular materials, we describe the force network for a localized monolayer and derive the corresponding discrete Airy stress function, expressed for each \$N\$-sided cell as \$N\$ scalars defined over kites covering the cell. We show how a torque balance (commonly overlooked in implementations of the vertex model) places a geometric constraint on the stress in the neighbourhood of cellular trijunctions, and requires cell edges to be orthogonal to the links of a dual network that connect neighbouring cell centres and thereby triangulate the monolayer. Torque balance also requires each internal vertex to be the orthocentre of the triangle formed by neighbouring edge centroids, and defines vertex locations relative to adjacent cell centres. We show how the Airy stress function depends on cell shape when a standard energy functional is adopted, and discuss implications for computational implementations of the model.},
   archiveprefix = {arXiv},
-  eprint = {1910.10799},
-  eprinttype = {arxiv},
-  keywords = {Condensed Matter - Disordered Systems and Neural Networks,Physics - Biological Physics,Quantitative Biology - Cell Behavior},
-  primaryclass = {cond-mat, physics:physics, q-bio}
+  keywords = {Condensed Matter - Disordered Systems and Neural Networks,Physics - Biological Physics,Quantitative Biology - Cell Behavior}
 }
 
 @article{kachalo_mechanical_2015,
@@ -1955,16 +1955,16 @@ @article{kachalo_mechanical_2015
   date = {2015-01},
   journaltitle = {PloS one},
   volume = {10},
+  number = {5},
+  eprint = {25974182},
+  eprinttype = {pmid},
   pages = {e0126484},
   issn = {1932-6203},
   doi = {10.1371/journal.pone.0126484},
   url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0126484},
   abstract = {Geometric and mechanical properties of individual cells and interactions among neighboring cells are the basis of formation of tissue patterns. Understanding the complex interplay of cells is essential for gaining insight into embryogenesis, tissue development, and other emerging behavior. Here we describe a cell model and an efficient geometric algorithm for studying the dynamic process of tissue formation in 2D (e.g. epithelial tissues). Our approach improves upon previous methods by incorporating properties of individual cells as well as detailed description of the dynamic growth process, with all topological changes accounted for. Cell size, shape, and division plane orientation are modeled realistically. In addition, cell birth, cell growth, cell shrinkage, cell death, cell division, cell collision, and cell rearrangements are now fully accounted for. Different models of cell-cell interactions, such as lateral inhibition during the process of growth, can be studied in detail. Cellular pattern formation for monolayered tissues from arbitrary initial conditions, including that of a single cell, can also be studied in detail. Computational efficiency is achieved through the employment of a special data structure that ensures access to neighboring cells in constant time, without additional space requirement. We have successfully generated tissues consisting of more than 20,000 cells starting from 2 cells within 1 hour. We show that our model can be used to study embryogenesis, tissue fusion, and cell apoptosis. We give detailed study of the classical developmental process of bristle formation on the epidermis of D. melanogaster and the fundamental problem of homeostatic size control in epithelial tissues. Simulation results reveal significant roles of solubility of secreted factors in both the bristle formation and the homeostatic control of tissue size. Our method can be used to study broad problems in monolayered tissue formation. Our software is publicly available.},
-  eprint = {25974182},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/NSFFN6MH/Kachalo et al. - 2015 - Mechanical model of geometric cell and topological algorithm for cell dynamics from single-cell to formation of.pdf},
   keywords = {Algorithms,Animals,Biological,Cell Communication,Cell Death,cell division,Cell Proliferation,Cell Shape,Cell Size,Computer Simulation,Drosophila melanogaster,Drosophila melanogaster: cytology,Drosophila melanogaster: embryology,Epithelial Cells,Epithelial Cells: cytology,epithelium,Epithelium: embryology,Models,Morphogenesis,Software},
-  number = {5}
+  file = {/home/guillaume/Zotero/storage/NSFFN6MH/Kachalo et al. - 2015 - Mechanical model of geometric cell and topological algorithm for cell dynamics from single-cell to formation of.pdf}
 }
 
 @article{kalkum_surface-wave_2007,
@@ -1973,8 +1973,8 @@ @article{kalkum_surface-wave_2007
   date = {2007},
   journaltitle = {Optics express},
   volume = {15},
-  pages = {2613--2621},
-  number = {5}
+  number = {5},
+  pages = {2613--2621}
 }
 
 @article{karolakPersonalizedComputationalOncology2018,
@@ -1984,17 +1984,17 @@ @article{karolakPersonalizedComputationalOncology2018
   date = {2018-01-01},
   journaltitle = {Journal of The Royal Society Interface},
   volume = {15},
+  number = {138},
+  eprint = {29367239},
+  eprinttype = {pmid},
   pages = {20170703},
   issn = {1742-5689, 1742-5662},
   doi = {10.1098/rsif.2017.0703},
   url = {http://rsif.royalsocietypublishing.org/content/15/138/20170703},
   urldate = {2018-01-28},
   abstract = {A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multi-component tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.},
-  eprint = {29367239},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/93VC72HP/Karolak et al. - 2018 - Towards personalized computational oncology from .pdf;/home/guillaume/Zotero/storage/7SRKSAZC/20170703.html},
   langid = {english},
-  number = {138}
+  file = {/home/guillaume/Zotero/storage/93VC72HP/Karolak et al. - 2018 - Towards personalized computational oncology from .pdf;/home/guillaume/Zotero/storage/7SRKSAZC/20170703.html}
 }
 
 @article{karsentiModellingMicrotubulePatterns2006,
@@ -2003,12 +2003,12 @@ @article{karsentiModellingMicrotubulePatterns2006
   date = {2006-11},
   journaltitle = {Nature Cell Biology},
   volume = {8},
+  number = {11},
   pages = {1204--1211},
   issn = {1465-7392, 1476-4679},
   doi = {10.1038/ncb1498},
   url = {http://www.nature.com/doifinder/10.1038/ncb1498},
-  urldate = {2016-11-02},
-  number = {11}
+  urldate = {2016-11-02}
 }
 
 @article{kerseyLinkingPublicationGene2006,
@@ -2017,12 +2017,12 @@ @article{kerseyLinkingPublicationGene2006
   date = {2006-11},
   journaltitle = {Nature Cell Biology},
   volume = {8},
+  number = {11},
   pages = {1183--1189},
   issn = {1465-7392, 1476-4679},
   doi = {10.1038/ncb1495},
   url = {http://www.nature.com/doifinder/10.1038/ncb1495},
-  urldate = {2016-11-02},
-  number = {11}
+  urldate = {2016-11-02}
 }
 
 @article{khairyContinuumMechanicsModeling2015,
@@ -2035,8 +2035,8 @@ @article{khairyContinuumMechanicsModeling2015
   url = {http://biorxiv.org/content/early/2015/10/16/029355},
   urldate = {2016-12-05},
   abstract = {Mechanics plays a key role in the development of higher organisms. For example, during fruit-fly gastrulation, local forces generated by the acto-myosin meshwork in the region of the future mesoderm lead to formation of a ventral tissue fold. The process is accompanied by substantial changes in cell shape and long-range cell movements whose origin is not understood. It has proven difficult to model the link between local forces, generated at the subcellular level, and global tissue deformation. Here, we adopt an approach first developed for lipid bilayers and cell membranes, in which we model force-generation at the cytoskeletal level as resulting in local changes in preferred tissue curvature. The continuum mechanics problem can thus be completely formulated in terms of tissue strains, which is desirable since mechanical forces themselves are often unknown. The solution then yields global morphogenetic predictions that accommodate the tendency towards this local preferred curvature. Computer simulations, especially in three dimensions, face the additional challenge of high complexity of shapes, gene expression patterns, and mechanical constraints. Our computational framework, which we call SPHARM-MECH, extends a three-dimensional spherical harmonics parameterization known as SPHARM to address this challenge. Using SPHARM-MECH in combination with whole-embryo light-sheet-based live imaging, we study mesoderm invagination in the fruit-fly embryo. Our analysis reveals a striking correlation between calculated and observed tissue movements, predicts the observed cell shape anisotropy on the ventral side of the embryo, and suggests an active mechanical role of the mesoderm invagination process in supporting the first phase of germ-band extension.},
-  file = {/home/guillaume/Zotero/storage/6CCP7X4J/Khairy et al. - 2015 - Continuum mechanics modeling of Drosophila mesoder.pdf;/home/guillaume/Zotero/storage/VIDF9BVN/029355.full.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/6CCP7X4J/Khairy et al. - 2015 - Continuum mechanics modeling of Drosophila mesoder.pdf;/home/guillaume/Zotero/storage/VIDF9BVN/029355.full.html}
 }
 
 @article{khairyPreferredCurvatureBasedContinuum2018,
@@ -2045,17 +2045,17 @@ @article{khairyPreferredCurvatureBasedContinuum2018
   date = {2018-01-23},
   journaltitle = {Biophysical Journal},
   volume = {114},
+  number = {2},
+  eprint = {29401426},
+  eprinttype = {pmid},
   pages = {267--277},
   issn = {0006-3495},
   doi = {10.1016/j.bpj.2017.11.015},
   url = {http://www.cell.com/biophysj/abstract/S0006-3495(17)31244-4},
   urldate = {2018-02-07},
   abstract = {Mechanics plays a key role in the development of higher organisms. However, understanding this relationship is complicated by the difficulty of modeling the link between local forces generated at the subcellular level and deformations observed at the tissue and whole-embryo levels. Here we propose an approach first developed for lipid bilayers and cell membranes, in which force-generation by cytoskeletal elements enters a continuum mechanics formulation for the full system in the form of local changes in preferred curvature. This allows us to express and solve the system using only tissue strains. Locations of preferred curvature are simply related to products of gene expression. A solution, in that context, means relaxing the system’s mechanical energy to yield global morphogenetic predictions that accommodate a tendency toward the local preferred curvature, without a need to explicitly model force-generation mechanisms at the molecular level. Our computational framework, which we call SPHARM-MECH, extends a 3D spherical harmonics parameterization known as SPHARM to combine this level of abstraction with a sparse shape representation. The integration of these two principles allows computer simulations to be performed in three dimensions on highly complex shapes, gene expression patterns, and mechanical constraints. We demonstrate our approach by modeling mesoderm invagination in the fruit-fly embryo, where local forces generated by the acto-myosin meshwork in the region of the future mesoderm lead to formation of a ventral tissue fold. The process is accompanied by substantial changes in cell shape and long-range cell movements. Applying SPHARM-MECH to whole-embryo live imaging data acquired with light-sheet microscopy reveals significant correlation between calculated and observed tissue movements. Our analysis predicts the observed cell shape anisotropy on the ventral side of the embryo and suggests an active mechanical role of mesoderm invagination in supporting the onset of germ-band extension.},
-  eprint = {29401426},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/6PLL4EUR/S0006-3495(17)31244-4.html},
   langid = {english},
-  number = {2}
+  file = {/home/guillaume/Zotero/storage/6PLL4EUR/S0006-3495(17)31244-4.html}
 }
 
 @article{kimEmbryonicTissuesActive2020,
@@ -2069,8 +2069,8 @@ @article{kimEmbryonicTissuesActive2020
   url = {https://www.biorxiv.org/content/10.1101/2020.06.17.157909v1},
   urldate = {2020-06-23},
   abstract = {{$<$}p{$>$}The physical state of embryonic tissues emerges from non-equilibrium, collective interactions among constituent cells. Cellular jamming, rigidity transitions and characteristics of glassy dynamics have all been observed in multicellular systems, but there is no unifying framework to describe all these behaviors. Here we develop a general computational framework that enables the description of embryonic tissue dynamics, accounting for the presence of extracellular spaces, complex cell shapes and tension fluctuations. In addition to previously reported rigidity transitions, we find a distinct rigidity transition governed by the magnitude of tension fluctuations. Our results indicate that tissues are maximally rigid at the structural transition between confluent and non-confluent states, with actively-generated tension fluctuations controlling stress relaxation and tissue fluidization. Comparing simulation results to experimental data, we show that tension fluctuations do control rigidity transitions in embryonic tissues, highlighting a key role of non-equilibrium tension dynamics in developmental processes.{$<$}/p{$>$}},
-  file = {/home/guillaume/Zotero/storage/5RABZG6L/Kim et al. - 2020 - Embryonic Tissues as Active Foams.pdf;/home/guillaume/Zotero/storage/KD9J8CXH/2020.06.17.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/5RABZG6L/Kim et al. - 2020 - Embryonic Tissues as Active Foams.pdf;/home/guillaume/Zotero/storage/KD9J8CXH/2020.06.17.html}
 }
 
 @article{kimHarnessingMechanobiologyTissue2021,
@@ -2079,17 +2079,17 @@ @article{kimHarnessingMechanobiologyTissue2021
   date = {2021-01-25},
   journaltitle = {Developmental Cell},
   volume = {56},
+  number = {2},
+  eprint = {33453155},
+  eprinttype = {pmid},
   pages = {180--191},
   publisher = {{Elsevier}},
   issn = {1534-5807},
   doi = {10.1016/j.devcel.2020.12.017},
   url = {http://www.cell.com/developmental-cell/abstract/S1534-5807(20)31023-6},
   urldate = {2021-01-29},
-  eprint = {33453155},
-  eprinttype = {pmid},
-  keywords = {cytoskeleton and adhesion,mechanical environment,mechanobiology,tissue engineering},
   langid = {english},
-  number = {2}
+  keywords = {cytoskeleton and adhesion,mechanical environment,mechanobiology,tissue engineering}
 }
 
 @article{kondoInverseTissueMechanics2018,
@@ -2098,16 +2098,16 @@ @article{kondoInverseTissueMechanics2018
   date = {2018-03-01},
   journaltitle = {PLOS Computational Biology},
   volume = {14},
+  number = {3},
   pages = {e1006029},
   issn = {1553-7358},
   doi = {10.1371/journal.pcbi.1006029},
   url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006029},
   urldate = {2018-03-02},
   abstract = {Living tissues undergo deformation during morphogenesis. In this process, cells generate mechanical forces that drive the coordinated cell motion and shape changes. Recent advances in experimental and theoretical techniques have enabled in situ measurement of the mechanical forces, but the characterization of mechanical properties that determine how these forces quantitatively affect tissue deformation remains challenging, and this represents a major obstacle for the complete understanding of morphogenesis. Here, we proposed a non-invasive reverse-engineering approach for the estimation of the mechanical properties, by combining tissue mechanics modeling and statistical machine learning. Our strategy is to model the tissue as a continuum mechanical system and to use passive observations of spontaneous tissue deformation and force fields to statistically estimate the model parameters. This method was applied to the analysis of the collective migration of Madin-Darby canine kidney cells, and the tissue flow and force were simultaneously observed by the phase contrast imaging and traction force microscopy. We found that our monolayer elastic model, whose elastic moduli were reverse-engineered, enabled a long-term forecast of the traction force fields when given the tissue flow fields, indicating that the elasticity contributes to the evolution of the tissue stress. Furthermore, we investigated the tissues in which myosin was inhibited by blebbistatin treatment, and observed a several-fold reduction in the elastic moduli. The obtained results validate our framework, which paves the way to the estimation of mechanical properties of living tissues during morphogenesis.},
-  file = {/home/guillaume/Zotero/storage/LKGG94C5/Kondo et al. - 2018 - Inverse tissue mechanics of cell monolayer expansi.pdf;/home/guillaume/Zotero/storage/FPCZI38L/article.html},
-  keywords = {Cell biology,Cell cycle and cell division,Deformation,Fluorescence imaging,Mechanical properties,Morphogenesis,Myosins,Tissue mechanics},
   langid = {english},
-  number = {3}
+  keywords = {Cell biology,Cell cycle and cell division,Deformation,Fluorescence imaging,Mechanical properties,Morphogenesis,Myosins,Tissue mechanics},
+  file = {/home/guillaume/Zotero/storage/LKGG94C5/Kondo et al. - 2018 - Inverse tissue mechanics of cell monolayer expansi.pdf;/home/guillaume/Zotero/storage/FPCZI38L/article.html}
 }
 
 @article{kongForcesDirectingGermband2017,
@@ -2115,6 +2115,7 @@ @article{kongForcesDirectingGermband2017
   author = {Kong, Deqing and Wolf, Fred and Großhans, Jörg},
   date = {2017-04},
   journaltitle = {Mechanisms of Development},
+  series = {Roles of Physical Forces in Development},
   volume = {144, Part A},
   pages = {11--22},
   issn = {0925-4773},
@@ -2122,9 +2123,8 @@ @article{kongForcesDirectingGermband2017
   url = {http://www.sciencedirect.com/science/article/pii/S0925477316300971},
   urldate = {2017-04-04},
   abstract = {Body axis elongation by convergent extension is a conserved developmental process found in all metazoans. Drosophila embryonic germ-band extension is an important morphogenetic process during embryogenesis, by which the length of the germ-band is more than doubled along the anterior-posterior axis. This lengthening is achieved by typical convergent extension, i.e. narrowing the lateral epidermis along the dorsal-ventral axis and simultaneous extension along the anterior-posterior axis. Germ-band extension is largely driven by cell intercalation, whose directionality is determined by the planar polarity of the tissue and ultimately by the anterior-posterior patterning system. In addition, extrinsic tensile forces originating from the invaginating endoderm induce cell shape changes, which transiently contribute to germ-band extension. Here, we review recent progress in understanding of the role of mechanical forces in germ-band extension.},
-  file = {/home/guillaume/Zotero/storage/RPZ9PEP7/Kong et al. - 2017 - Forces directing germ-band extension in Drosophila.pdf;/home/guillaume/Zotero/storage/XU9M6EZH/S0925477316300971.html},
   keywords = {Cell intercalation,Drosophila,Forces,Germ-band extension},
-  series = {Roles of Physical Forces in Development}
+  file = {/home/guillaume/Zotero/storage/RPZ9PEP7/Kong et al. - 2017 - Forces directing germ-band extension in Drosophila.pdf;/home/guillaume/Zotero/storage/XU9M6EZH/S0925477316300971.html}
 }
 
 @article{koyamaMechanicalRegulationThreeDimensional2016,
@@ -2133,16 +2133,16 @@ @article{koyamaMechanicalRegulationThreeDimensional2016
   date = {2016-08-09},
   journaltitle = {Biophysical Journal},
   volume = {111},
+  number = {3},
+  eprint = {27508448},
+  eprinttype = {pmid},
   pages = {650--665},
   issn = {0006-3495},
   doi = {10.1016/j.bpj.2016.06.032},
   url = {http://www.cell.com/biophysj/abstract/S0006-3495(16)30479-9},
   urldate = {2017-07-27},
-  eprint = {27508448},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/MN7GVCUQ/Koyama et al. - 2016 - Mechanical Regulation of Three-Dimensional Epithel.pdf;/home/guillaume/Zotero/storage/WIECWVHB/S0006-3495(16)30479-9.html},
   langid = {english},
-  number = {3}
+  file = {/home/guillaume/Zotero/storage/MN7GVCUQ/Koyama et al. - 2016 - Mechanical Regulation of Three-Dimensional Epithel.pdf;/home/guillaume/Zotero/storage/WIECWVHB/S0006-3495(16)30479-9.html}
 }
 
 @article{landsberg_increased_2009,
@@ -2151,16 +2151,16 @@ @article{landsberg_increased_2009
   date = {2009},
   journaltitle = {Current Biology},
   volume = {19},
+  number = {22},
+  eprint = {19879142},
+  eprinttype = {pmid},
   pages = {1950--1955},
   issn = {09609822},
   doi = {10.1016/j.cub.2009.10.021},
   url = {http://dx.doi.org/10.1016/j.cub.2009.10.021},
   abstract = {Subdividing proliferating tissues into compartments is an evolutionarily conserved strategy of animal development [1-6]. Signals across boundaries between compartments can result in local expression of secreted proteins organizing growth and patterning of tissues [1-6]. Sharp and straight interfaces between compartments are crucial for stabilizing the position of such organizers and therefore for precise implementation of body plans. Maintaining boundaries in proliferating tissues requires mechanisms to counteract cell rearrangements caused by cell division; however, the nature of such mechanisms remains unclear. Here we quantitatively analyzed cell morphology and the response to the laser ablation of cell bonds in the vicinity of the anteroposterior compartment boundary in developing Drosophila wings. We found that mechanical tension is approximately 2.5-fold increased on cell bonds along this compartment boundary as compared to the remaining tissue. Cell bond tension is decreased in the presence of Y-27632 [7], an inhibitor of Rho-kinase whose main effector is Myosin II [8]. Simulations using a vertex model [9] demonstrate that a 2.5-fold increase in local cell bond tension suffices to guide the rearrangement of cells after cell division to maintain compartment boundaries. Our results provide a physical mechanism in which the local increase in Myosin II-dependent cell bond tension directs cell sorting at compartment boundaries. © 2009 Elsevier Ltd. All rights reserved.},
-  eprint = {19879142},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/48BIV7VP/Landsberg et al. - 2009 - Increased Cell Bond Tension Governs Cell Sorting at the Drosophila Anteroposterior Compartment Boundary.pdf},
   keywords = {CELLBIO,DEVBIO},
-  number = {22}
+  file = {/home/guillaume/Zotero/storage/48BIV7VP/Landsberg et al. - 2009 - Increased Cell Bond Tension Governs Cell Sorting at the Drosophila Anteroposterior Compartment Boundary.pdf}
 }
 
 @article{landsberg_supplemental_????,
@@ -2181,8 +2181,8 @@ @article{lardennoisActinbasedViscoplasticLock2019
   url = {https://www.nature.com/articles/s41586-019-1509-4},
   urldate = {2019-08-30},
   abstract = {Molecular analysis and mathematical modelling are combined to identify a network of factors that account for viscoplastic deformation in elongation of Caenorhabditis elegans during embryonic development.},
-  file = {/home/guillaume/Zotero/storage/LM8X8TZP/s41586-019-1509-4.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/LM8X8TZP/s41586-019-1509-4.html}
 }
 
 @article{laurentConvergenceMicroengineeringCellular2017,
@@ -2191,15 +2191,15 @@ @article{laurentConvergenceMicroengineeringCellular2017
   date = {2017-12},
   journaltitle = {Nature Biomedical Engineering},
   volume = {1},
+  number = {12},
   pages = {939},
   issn = {2157-846X},
   doi = {10.1038/s41551-017-0166-x},
   url = {https://www.nature.com/articles/s41551-017-0166-x},
   urldate = {2017-12-15},
   abstract = {{$<$}p{$>$}This Perspective argues that tissue-manufacturing approaches relying on directed self-organization will enable the production of functional tissues with complex biological features.{$<$}/p{$>$}},
-  file = {/home/guillaume/Zotero/storage/PPLGZ29G/Laurent et al. - 2017 - Convergence of microengineering and cellular self-.pdf;/home/guillaume/Zotero/storage/7XYB7BRW/s41551-017-0166-x.html},
   langid = {english},
-  number = {12}
+  file = {/home/guillaume/Zotero/storage/PPLGZ29G/Laurent et al. - 2017 - Convergence of microengineering and cellular self-.pdf;/home/guillaume/Zotero/storage/7XYB7BRW/s41551-017-0166-x.html}
 }
 
 @article{lawrimore_dna_2015,
@@ -2208,12 +2208,12 @@ @article{lawrimore_dna_2015
   date = {2015-08},
   journaltitle = {The Journal of Cell Biology},
   volume = {210},
+  number = {4},
   pages = {553--564},
   issn = {0021-9525},
   doi = {10.1083/jcb.201502046},
   url = {http://jcb.rupress.org/content/210/4/553},
-  abstract = {The centromere is the DNA locus that dictates kinetochore formation and is visibly apparent as heterochromatin that bridges sister kinetochores in metaphase. Sister centromeres are compacted and held together by cohesin, condensin, and topoisomerase-mediated entanglements until all sister chromosomes bi-orient along the spindle apparatus. The establishment of tension between sister chromatids is essential for quenching a checkpoint kinase signal generated from kinetochores lacking microtubule attachment or tension. How the centromere chromatin spring is organized and functions as a tensiometer is largely unexplored. We have discovered that centromere chromatin loops generate an extensional/poleward force sufficient to release nucleosomes proximal to the spindle axis. This study describes how the physical consequences of DNA looping directly underlie the biological mechanism for sister centromere separation and the spring-like properties of the centromere in mitosis.},
-  number = {4}
+  abstract = {The centromere is the DNA locus that dictates kinetochore formation and is visibly apparent as heterochromatin that bridges sister kinetochores in metaphase. Sister centromeres are compacted and held together by cohesin, condensin, and topoisomerase-mediated entanglements until all sister chromosomes bi-orient along the spindle apparatus. The establishment of tension between sister chromatids is essential for quenching a checkpoint kinase signal generated from kinetochores lacking microtubule attachment or tension. How the centromere chromatin spring is organized and functions as a tensiometer is largely unexplored. We have discovered that centromere chromatin loops generate an extensional/poleward force sufficient to release nucleosomes proximal to the spindle axis. This study describes how the physical consequences of DNA looping directly underlie the biological mechanism for sister centromere separation and the spring-like properties of the centromere in mitosis.}
 }
 
 @article{lecuit_cell_2007,
@@ -2222,14 +2222,14 @@ @article{lecuit_cell_2007
   date = {2007},
   journaltitle = {Nature reviews. Molecular cell biology},
   volume = {8},
+  number = {8},
+  eprint = {17643125},
+  eprinttype = {pmid},
   pages = {633--644},
   issn = {1471-0072},
   doi = {10.1038/nrm2222},
   abstract = {Embryonic morphogenesis requires the execution of complex mechanisms that regulate the local behaviour of groups of cells. The orchestration of such mechanisms has been mainly deciphered through the identification of conserved families of signalling pathways that spatially and temporally control cell behaviour. However, how this information is processed to control cell shape and cell dynamics is an open area of investigation. The framework that emerges from diverse disciplines such as cell biology, physics and developmental biology points to adhesion and cortical actin networks as regulators of cell surface mechanics. In this context, a range of developmental phenomena can be explained by the regulation of cell surface tension.},
-  eprint = {17643125},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/NNJ2P6G4/Lecuit, Lenne - 2007 - Cell surface mechanics and the control of cell shape, tissue patterns and morphogenesis.pdf},
-  number = {8}
+  file = {/home/guillaume/Zotero/storage/NNJ2P6G4/Lecuit, Lenne - 2007 - Cell surface mechanics and the control of cell shape, tissue patterns and morphogenesis.pdf}
 }
 
 @article{leeuwenIntegrativeComputationalModel2009,
@@ -2238,13 +2238,13 @@ @article{leeuwenIntegrativeComputationalModel2009
   date = {2009-10-01},
   journaltitle = {Cell Proliferation},
   volume = {42},
+  number = {5},
   pages = {617--636},
   issn = {1365-2184},
   doi = {10.1111/j.1365-2184.2009.00627.x},
   url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-2184.2009.00627.x},
   urldate = {2018-10-09},
-  langid = {english},
-  number = {5}
+  langid = {english}
 }
 
 @article{leeuwenIntegrativeComputationalModel2009a,
@@ -2253,14 +2253,14 @@ @article{leeuwenIntegrativeComputationalModel2009a
   date = {2009-10-01},
   journaltitle = {Cell Proliferation},
   volume = {42},
+  number = {5},
   pages = {617--636},
   issn = {1365-2184},
   doi = {10.1111/j.1365-2184.2009.00627.x},
   url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-2184.2009.00627.x},
   urldate = {2018-03-20},
-  file = {/home/guillaume/Zotero/storage/UDGEVRFB/j.1365-2184.2009.00627.html},
   langid = {english},
-  number = {5}
+  file = {/home/guillaume/Zotero/storage/UDGEVRFB/j.1365-2184.2009.00627.html}
 }
 
 @article{levayer_tissue_2016,
@@ -2273,9 +2273,9 @@ @article{levayer_tissue_2016
   issn = {0960-9822},
   doi = {10.1016/j.cub.2015.12.072},
   url = {http://www.cell.com/article/S0960982216000592/fulltext},
-  file = {/home/guillaume/Zotero/storage/SCE6SX65/Levayer, Dupont, Moreno - Unknown - Tissue Crowding Induces Caspase-Dependent Competition for Space.pdf},
+  langid = {english},
   keywords = {apoptosis},
-  langid = {english}
+  file = {/home/guillaume/Zotero/storage/SCE6SX65/Levayer, Dupont, Moreno - Unknown - Tissue Crowding Induces Caspase-Dependent Competition for Space.pdf}
 }
 
 @article{liuSimpleSelfcollisionAvoidance1998,
@@ -2284,18 +2284,19 @@ @article{liuSimpleSelfcollisionAvoidance1998
   date = {1998-02-25},
   journaltitle = {Computers \& Graphics},
   volume = {22},
+  number = {1},
   pages = {117--128},
   issn = {0097-8493},
   doi = {10.1016/S0097-8493(97)00087-3},
   url = {http://www.sciencedirect.com/science/article/pii/S0097849397000873},
   urldate = {2018-11-16},
   abstract = {This paper discusses the self-collision avoidance problem in simulating the dynamic behavior of deformable cloth. It is not an easy task to simulate realistically the response of cloth in various types of complicated self-collisions. In this paper, the bounding box technique in addition to ordinary prechecking for collision detection is used to avoid self-collision. Instead of avoiding self-collision of cloth triangles, we relax to avoid the collision of the bounding boxes of cloth triangles. Some constraints are enforced on the vertices of the cloth triangles to prevent their bounding boxes from penetrating in the direction of collision. The experimental results show that the relaxed self-collision avoidance method can create realistic cloth behavior and wrinkling formation processes. Since the types of interaction between bounding boxes are simple, our method is simple but robust in avoiding self-collisions.},
-  file = {/home/guillaume/Zotero/storage/FDU3U79S/liu1998.pdf;/home/guillaume/Zotero/storage/DTS3ZB8Q/S0097849397000873.html},
   keywords = {bounding box,cloth animation,iteration method,self-collision},
-  number = {1}
+  file = {/home/guillaume/Zotero/storage/FDU3U79S/liu1998.pdf;/home/guillaume/Zotero/storage/DTS3ZB8Q/S0097849397000873.html}
 }
 
 @thesis{lontos3DModelingSimulation2013,
+  type = {phdthesis},
   title = {{{3D}} Modeling and Simulation of Morphogenesis},
   author = {Lontos, Athanasios},
   date = {2013-12-06},
@@ -2303,9 +2304,8 @@ @thesis{lontos3DModelingSimulation2013
   url = {https://tel.archives-ouvertes.fr/tel-01168475/document},
   urldate = {2018-01-16},
   abstract = {The embryo of the Drosophila Melanogaster undergoes a series of cell movements during its early development. Gastrulation is the process describing the segregation of the future internal tissues into the interior of the developing embryo. Gastrulation starts with the formation of the ventral furrow, a process commonly known as the ventral furrow invagination. During this process, the most ventrally located blastoderm cells flatten and progressively constrict their apical sides until they are wedge shaped. As a result of these cell-shape changes, the blastoderm epithelium first forms an indentation, the ventral furrow, which is then completely internalized. We focus on the study of the mechanisms that drive the invagination. The main questions that gave birth to this thesis are: “What is the role of the apical constriction of the ventral cells in the invagination?” and “Once the ventral cells are internalized, what is the mechanism that drives the ventral closure?” We attempt to answer to these two questions from a biomechanical point of view. For this purpose, a 3D mesh of the embryo of the Drosophila Melanogaster has been created. Based on this mesh, two “a minima” biomechanical models of the Drosophila embryo have been created, a physically based discrete model and a model based on the Finite Element Method. The results of the simulations in both models show that the geometry of the embryo plays a crucial role in the internalization of the ventral cells. The two models efficiently simulate the internalization of the ventral cells but are incapable of reproducing the ventral closure. We hypothesize that the ventral closure can be explained by the interplay of forces developed in the embryo once the internalized ventral cells undergo cell division. We propose an approach to divide elements in a Finite Element Mesh and we integrate it to the Finite Element Model of the Drosophila Melanogaster.},
-  file = {/home/guillaume/Zotero/storage/3JPH9TC8/Lontos - 2013 - 3D modeling and simulation of morphogenesis.pdf;/home/guillaume/Zotero/storage/NIRAMRLQ/tel-01168475.html},
   langid = {english},
-  type = {phdthesis}
+  file = {/home/guillaume/Zotero/storage/3JPH9TC8/Lontos - 2013 - 3D modeling and simulation of morphogenesis.pdf;/home/guillaume/Zotero/storage/NIRAMRLQ/tel-01168475.html}
 }
 
 @article{lorenzo_live_2011,
@@ -2314,8 +2314,8 @@ @article{lorenzo_live_2011
   date = {2011},
   journaltitle = {Cell Division},
   volume = {6},
-  pages = {22},
-  number = {1}
+  number = {1},
+  pages = {22}
 }
 
 @inproceedings{lorenzo_r154:_2009,
@@ -2333,14 +2333,14 @@ @article{macklinProgressComputational3D2016
   date = {2016},
   journaltitle = {Adv. Exp. Med. Biol.},
   volume = {936},
+  eprint = {27739051},
+  eprinttype = {pmid},
   pages = {225--246},
   issn = {0065-2598},
   doi = {10.1007/978-3-319-42023-3_12},
   abstract = {Tumors cannot be understood in isolation from their microenvironment. Tumor and stromal cells change phenotype based upon biochemical and biophysical inputs from their surroundings, even as they interact with and remodel the microenvironment. Cancer should be investigated as an adaptive, multicellular system in a dynamical microenvironment. Computational modeling offers the potential to detangle this complex system, but the modeling platform must ideally account for tumor heterogeneity, substrate and signaling factor biotransport, cell and tissue biophysics, tissue and vascular remodeling, microvascular and interstitial flow, and links between all these sub-systems. Such a platform should leverage high-throughput experimental data, while using open data standards for reproducibility. In this chapter, we review advances by our groups in these key areas, particularly in advanced models of tissue mechanics and interstitial flow, open source simulation software, high-throughput phenotypic screening, and multicellular data standards. In the future, we expect a transformation of computational cancer biology from individual groups modeling isolated parts of cancer, to coalitions of groups combining compatible tools to simulate the 3-D multicellular systems biology of cancer tissues.},
-  eprint = {27739051},
-  eprinttype = {pmid},
-  keywords = {Cancer microenvironment,Computational modeling,Multicellular systems biology,Tissue engineering},
-  langid = {english}
+  langid = {english},
+  keywords = {Cancer microenvironment,Computational modeling,Multicellular systems biology,Tissue engineering}
 }
 
 @article{maitreAsymmetricDivisionContractile2016,
@@ -2349,6 +2349,7 @@ @article{maitreAsymmetricDivisionContractile2016
   date = {2016-08},
   journaltitle = {Nature},
   volume = {536},
+  number = {7616},
   pages = {344--348},
   publisher = {{Nature Publishing Group}},
   issn = {1476-4687},
@@ -2356,10 +2357,9 @@ @article{maitreAsymmetricDivisionContractile2016
   url = {https://www.nature.com/articles/nature18958},
   urldate = {2021-02-10},
   abstract = {Here, a combination of biophysical measurement, modelling, and genetic and experimental manipulation of cell contractile components is used to analyse the formation of the inner cell mass in the early mouse embryo.},
-  file = {/home/guillaume/Zotero/storage/HZJRUL8S/Maître et al. - 2016 - Asymmetric division of contractile domains couples.pdf;/home/guillaume/Zotero/storage/SSVPE9C7/nature18958.html},
   issue = {7616},
   langid = {english},
-  number = {7616}
+  file = {/home/guillaume/Zotero/storage/HZJRUL8S/Maître et al. - 2016 - Asymmetric division of contractile domains couples.pdf;/home/guillaume/Zotero/storage/SSVPE9C7/nature18958.html}
 }
 
 @article{mao_planar_2011,
@@ -2368,16 +2368,16 @@ @article{mao_planar_2011
   date = {2011-01},
   journaltitle = {Genes \& development},
   volume = {25},
+  number = {2},
+  eprint = {21245166},
+  eprinttype = {pmid},
   pages = {131--6},
   issn = {1549-5477},
   doi = {10.1101/gad.610511},
   url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3022259&tool=pmcentrez&rendertype=abstract},
   abstract = {Tissues can grow in a particular direction by controlling the orientation of cell divisions. This phenomenon is evident in the developing Drosophila wing epithelium, where the tissue becomes elongated along the proximal-distal axis. We show that orientation of cell divisions in the wing requires planar polarization of an atypical myosin, Dachs. Our evidence suggests that Dachs constricts cell-cell junctions to alter the geometry of cell shapes at the apical surface, and that cell shape then determines the orientation of the mitotic spindle. Using a computational model of a growing epithelium, we show that polarized cell tension is sufficient to orient cell shapes, cell divisions, and tissue growth. Planar polarization of Dachs is ultimately oriented by long-range gradients emanating from compartment boundaries, and is therefore a mechanism linking these gradients with the control of tissue shape.},
-  eprint = {21245166},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/E9NURGNI/Mao et al. - 2011 - Planar polarization of the atypical myosin Dachs orients cell divisions in Drosophila.pdf},
   keywords = {Animal,Animal: cytology,Animal: embryology,Animals,cell division,Cell Division: genetics,Cell Polarity,Cell Polarity: physiology,Developmental,Drosophila melanogaster,Drosophila melanogaster: cytology,Drosophila melanogaster: embryology,Drosophila melanogaster: genetics,Drosophila Proteins,Drosophila Proteins: metabolism,Gene Expression Regulation,Myosins,Myosins: metabolism,Spindle Apparatus,Spindle Apparatus: metabolism,Wings},
-  number = {2}
+  file = {/home/guillaume/Zotero/storage/E9NURGNI/Mao et al. - 2011 - Planar polarization of the atypical myosin Dachs orients cell divisions in Drosophila.pdf}
 }
 
 @article{marin-rieraComputationalModelingDevelopment2016,
@@ -2387,15 +2387,15 @@ @article{marin-rieraComputationalModelingDevelopment2016
   date = {2016-01-15},
   journaltitle = {Bioinformatics},
   volume = {32},
+  number = {2},
+  eprint = {26342230},
+  eprinttype = {pmid},
   pages = {219--225},
   issn = {1367-4811},
   doi = {10.1093/bioinformatics/btv527},
   abstract = {MOTIVATION: The transformation of the embryo during development requires complex gene networks, cell signaling and gene-regulated cell behaviors (division, adhesion, polarization, apoptosis, contraction, extracellular matrix secretion, signal secretion and reception, etc.). There are several models of development implementing these phenomena, but none considers at the same time the very different bio-mechanical properties of epithelia, mesenchyme, extracellular matrix and their interactions. RESULTS: Here, we present a new computational model and accompanying open-source software, EmbryoMaker, that allows the user to simulate custom developmental processes by designing custom gene networks capable of regulating cell signaling and all animal basic cell behaviors. We also include an editor to implement different initial conditions, mutations and experimental manipulations. We show the applicability of the model by simulating several complex examples of animal development. AVAILABILITY AND IMPLEMENTATION: The source code can be downloaded from: http://www.biocenter.helsinki.fi/salazar/software.html. CONTACT: isalazar@mappi.helsinki.fi SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
-  eprint = {26342230},
-  eprinttype = {pmid},
-  keywords = {Animals,Computer Simulation,Embryonic Development,Epithelium,Extracellular Matrix,Gene Regulatory Networks,Mesoderm,Models; Biological,Morphogenesis,Signal Transduction,Software},
   langid = {english},
-  number = {2}
+  keywords = {Animals,Computer Simulation,Embryonic Development,Epithelium,Extracellular Matrix,Gene Regulatory Networks,Mesoderm,Models; Biological,Morphogenesis,Signal Transduction,Software}
 }
 
 @article{marinari_live-cell_2012,
@@ -2404,16 +2404,16 @@ @article{marinari_live-cell_2012
   date = {2012},
   journaltitle = {Nature},
   volume = {484},
+  number = {7395},
+  eprint = {22504180},
+  eprinttype = {pmid},
   pages = {542--545},
   issn = {0028-0836},
   doi = {10.1038/nature10984},
   url = {http://discovery.ucl.ac.uk/1344856/},
   abstract = {The development and maintenance of an epithelium requires finely balanced rates of growth and cell death. However, the mechanical and biochemical mechanisms that ensure proper feedback control of tissue growth, which when deregulated contribute to tumorigenesis, are poorly understood. Here we use the fly notum as a model system to identify a novel process of crowding-induced cell delamination that balances growth to ensure the development of well-ordered cell packing. In crowded regions of the tissue, a proportion of cells undergo a serial loss of cell-cell junctions and a progressive loss of apical area, before being squeezed out by their neighbours. This path of delamination is recapitulated by a simple computational model of epithelial mechanics, in which stochastic cell loss relieves overcrowding as the system tends towards equilibrium. We show that this process of delamination is mechanistically distinct from apoptosis-mediated cell extrusion and precedes the first signs of cell death. Overall, this analysis reveals a simple mechanism that buffers epithelia against variations in growth. Because live-cell delamination constitutes a mechanistic link between epithelial hyperplasia and cell invasion, this is likely to have important implications for our understanding of the early stages of cancer development.},
-  eprint = {22504180},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/BEW4A3NG/Marinari et al. - 2012 - Live-cell delamination counterbalances epithelial growth to limit tissue overcrowding.pdf},
   keywords = {Animals,apoptosis,Cell Communication,Cell Count},
-  number = {7395}
+  file = {/home/guillaume/Zotero/storage/BEW4A3NG/Marinari et al. - 2012 - Live-cell delamination counterbalances epithelial growth to limit tissue overcrowding.pdf}
 }
 
 @article{martin_integration_2010,
@@ -2422,14 +2422,14 @@ @article{martin_integration_2010
   date = {2010},
   journaltitle = {Journal of Cell Biology},
   volume = {188},
+  number = {5},
+  eprint = {20194639},
+  eprinttype = {pmid},
   pages = {735--749},
   issn = {00219525},
   doi = {10.1083/jcb.200910099},
   abstract = {Contractile forces generated by the actomyosin cytoskeleton within individual cells collectively generate tissue-level force during epithelial morphogenesis. During Drosophila mesoderm invagination, pulsed actomyosin meshwork contractions and a ratchet-like stabilization of cell shape drive apical constriction. Here, we investigate how contractile forces are integrated across the tissue. Reducing adherens junction (AJ) levels or ablating actomyosin meshworks causes tissue-wide epithelial tears, which release tension that is predominantly oriented along the anterior-posterior (a-p) embryonic axis. Epithelial tears allow cells normally elongated along the a-p axis to constrict isotropically, which suggests that apical constriction generates anisotropic epithelial tension that feeds back to control cell shape. Epithelial tension requires the transcription factor Twist, which stabilizes apical myosin II, promoting the formation of a supracellular actomyosin meshwork in which radial actomyosin fibers are joined end-to-end at spot AJs. Thus, pulsed actomyosin contractions require a supracellular, tensile meshwork to transmit cellular forces to the tissue level during morphogenesis.},
-  eprint = {20194639},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/IQEA8KCH/Martin et al. - 2010 - Integration of contractile forces during tissue invagination.pdf},
-  number = {5}
+  file = {/home/guillaume/Zotero/storage/IQEA8KCH/Martin et al. - 2010 - Integration of contractile forces during tissue invagination.pdf}
 }
 
 @article{martin_pulsed_2009,
@@ -2438,15 +2438,15 @@ @article{martin_pulsed_2009
   date = {2009},
   journaltitle = {Nature},
   volume = {457},
+  number = {7228},
+  eprint = {19029882},
+  eprinttype = {pmid},
   pages = {495--499},
   issn = {0028-0836},
   doi = {10.1038/nature07522},
   url = {http://dx.doi.org/10.1038/nature07522},
   abstract = {Apical constriction facilitates epithelial sheet bending and invagination during morphogenesis. Apical constriction is conventionally thought to be driven by the continuous purse-string-like contraction of a circumferential actin and non-muscle myosin-II (myosin) belt underlying adherens junctions. However, it is unclear whether other force-generating mechanisms can drive this process. Here we show, with the use of real-time imaging and quantitative image analysis of Drosophila gastrulation, that the apical constriction of ventral furrow cells is pulsed. Repeated constrictions, which are asynchronous between neighbouring cells, are interrupted by pauses in which the constricted state of the cell apex is maintained. In contrast to the purse-string model, constriction pulses are powered by actin-myosin network contractions that occur at the medial apical cortex and pull discrete adherens junction sites inwards. The transcription factors Twist and Snail differentially regulate pulsed constriction. Expression of snail initiates actin-myosin network contractions, whereas expression of twist stabilizes the constricted state of the cell apex. Our results suggest a new model for apical constriction in which a cortical actin-myosin cytoskeleton functions as a developmentally controlled subcellular ratchet to reduce apical area incrementally.},
-  eprint = {19029882},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/H8RTDFXU/Martin, Kaschube, Wieschaus - 2009 - Pulsed contractions of an actin-myosin network drive apical constriction.pdf},
-  number = {7228}
+  file = {/home/guillaume/Zotero/storage/H8RTDFXU/Martin, Kaschube, Wieschaus - 2009 - Pulsed contractions of an actin-myosin network drive apical constriction.pdf}
 }
 
 @article{martinArp23dependentMechanical2021,
@@ -2459,9 +2459,9 @@ @article{martinArp23dependentMechanical2021
   url = {https://www.sciencedirect.com/science/article/pii/S1534580721000368},
   urldate = {2021-02-10},
   abstract = {Epithelial sheets undergo highly reproducible remodeling to shape organs. This stereotyped morphogenesis depends on a well-defined sequence of events leading to the regionalized expression of developmental patterning genes that finally triggers downstream mechanical forces to drive tissue remodeling at a pre-defined position. However, how tissue mechanics controls morphogenetic robustness when challenged by intrinsic perturbations in close proximity has never been addressed. Using Drosophila developing leg, we show that a bias in force propagation ensures stereotyped morphogenesis despite the presence of mechanical noise in the environment. We found that knockdown of the Arp2/3 complex member Arpc5 specifically affects fold directionality while altering neither the developmental nor the force generation patterns. By combining in silico modeling, biophysical tools, and ad hoc genetic tools, our data reveal that junctional myosin II planar polarity favors long-range force channeling and ensures folding robustness, avoiding force scattering and thus isolating the fold domain from surrounding mechanical perturbations.},
-  file = {/home/guillaume/Zotero/storage/X85MXLJC/S1534580721000368.html},
+  langid = {english},
   keywords = {force transmission,invagination,mechanical noise,morphogenesis robustness,Myosin II planar polarity},
-  langid = {english}
+  file = {/home/guillaume/Zotero/storage/X85MXLJC/S1534580721000368.html}
 }
 
 @article{marxMayMechanobiologyWork2019,
@@ -2470,15 +2470,15 @@ @article{marxMayMechanobiologyWork2019
   date = {2019-11},
   journaltitle = {Nat Methods},
   volume = {16},
+  number = {11},
   pages = {1083--1086},
   issn = {1548-7105},
   doi = {10.1038/s41592-019-0621-6},
   url = {https://www.nature.com/articles/s41592-019-0621-6},
   urldate = {2020-01-03},
   abstract = {Mechanical measurements would be easy if cells were homogeneous objects — they’re not.},
-  file = {/home/guillaume/Zotero/storage/TYZT5JV9/Marx - 2019 - May mechanobiology work forcefully for you.pdf;/home/guillaume/Zotero/storage/7ZFM2QR5/s41592-019-0621-6.html},
   langid = {english},
-  number = {11}
+  file = {/home/guillaume/Zotero/storage/TYZT5JV9/Marx - 2019 - May mechanobiology work forcefully for you.pdf;/home/guillaume/Zotero/storage/7ZFM2QR5/s41592-019-0621-6.html}
 }
 
 @article{maryBuildingAccurateChromosome2016,
@@ -2487,14 +2487,14 @@ @article{maryBuildingAccurateChromosome2016
   date = {2016-02-16},
   journaltitle = {Biophysical Journal},
   volume = {110},
+  number = {3},
   pages = {477a},
   issn = {0006-3495},
   doi = {10.1016/j.bpj.2015.11.2550},
   url = {http://www.cell.com/biophysj/abstract/S0006-3495(15)03733-9},
   urldate = {2016-12-11},
-  file = {/home/guillaume/Zotero/storage/FU9J4MA4/Mary et al. - 2016 - Building an Accurate Chromosome Segregation Machin.pdf;/home/guillaume/Zotero/storage/S8NXGUJU/S0006-3495(15)03733-9.html},
   langid = {english},
-  number = {3}
+  file = {/home/guillaume/Zotero/storage/FU9J4MA4/Mary et al. - 2016 - Building an Accurate Chromosome Segregation Machin.pdf;/home/guillaume/Zotero/storage/S8NXGUJU/S0006-3495(15)03733-9.html}
 }
 
 @article{maryFissionYeastKinesin82015,
@@ -2503,17 +2503,17 @@ @article{maryFissionYeastKinesin82015
   date = {2015-10-15},
   journaltitle = {J Cell Sci},
   volume = {128},
+  number = {20},
+  eprint = {26359299},
+  eprinttype = {pmid},
   pages = {3720--3730},
   issn = {0021-9533, 1477-9137},
   doi = {10.1242/jcs.160465},
   url = {http://jcs.biologists.org/content/128/20/3720},
   urldate = {2017-06-29},
   abstract = {Skip to Next Section In higher eukaryotes, efficient chromosome congression relies, among other players, on the activity of chromokinesins. Here, we provide a quantitative analysis of kinetochore oscillations and positioning in Schizosaccharomyces pombe, a model organism lacking chromokinesins. In wild-type cells, chromosomes align during prophase and, while oscillating, maintain this alignment throughout metaphase. Chromosome oscillations are dispensable both for kinetochore congression and stable kinetochore alignment during metaphase. In higher eukaryotes, kinesin-8 family members control chromosome congression by regulating their oscillations. By contrast, here, we demonstrate that fission yeast kinesin-8 controls chromosome congression by an alternative mechanism. We propose that kinesin-8 aligns chromosomes by controlling pulling forces in a length-dependent manner. A coarse-grained model of chromosome segregation implemented with a length-dependent process that controls the force at kinetochores is necessary and sufficient to mimic kinetochore alignment, and prevents the appearance of lagging chromosomes. Taken together, these data illustrate how the local action of a motor protein at kinetochores provides spatial cues within the spindle to align chromosomes and to prevent aneuploidy.},
-  eprint = {26359299},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/387UQS4P/Mary et al. - 2015 - Fission yeast kinesin-8 controls chromosome congre.pdf;/home/guillaume/Zotero/storage/MKNQA7M6/3720.html},
   langid = {english},
-  number = {20}
+  file = {/home/guillaume/Zotero/storage/387UQS4P/Mary et al. - 2015 - Fission yeast kinesin-8 controls chromosome congre.pdf;/home/guillaume/Zotero/storage/MKNQA7M6/3720.html}
 }
 
 @article{maryFissionYeastKinesin82015a,
@@ -2522,17 +2522,17 @@ @article{maryFissionYeastKinesin82015a
   date = {2015-10-15},
   journaltitle = {J Cell Sci},
   volume = {128},
+  number = {20},
+  eprint = {26359299},
+  eprinttype = {pmid},
   pages = {3720--3730},
   issn = {0021-9533, 1477-9137},
   doi = {10.1242/jcs.160465},
   url = {http://jcs.biologists.org/content/128/20/3720},
   urldate = {2016-12-11},
   abstract = {In higher eukaryotes, efficient chromosome congression relies, among other players, on the activity of chromokinesins. Here, we provide a quantitative analysis of kinetochore oscillations and positioning in Schizosaccharomyces pombe, a model organism lacking chromokinesins. In wild-type cells, chromosomes align during prophase and, while oscillating, maintain this alignment throughout metaphase. Chromosome oscillations are dispensable both for kinetochore congression and stable kinetochore alignment during metaphase. In higher eukaryotes, kinesin-8 family members control chromosome congression by regulating their oscillations. By contrast, here, we demonstrate that fission yeast kinesin-8 controls chromosome congression by an alternative mechanism. We propose that kinesin-8 aligns chromosomes by controlling pulling forces in a length-dependent manner. A coarse-grained model of chromosome segregation implemented with a length-dependent process that controls the force at kinetochores is necessary and sufficient to mimic kinetochore alignment, and prevents the appearance of lagging chromosomes. Taken together, these data illustrate how the local action of a motor protein at kinetochores provides spatial cues within the spindle to align chromosomes and to prevent aneuploidy.},
-  eprint = {26359299},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/C62M3B3W/Mary et al. - 2015 - Fission yeast kinesin-8 controls chromosome congre.pdf;/home/guillaume/Zotero/storage/ZDAM3UK2/3720.html},
   langid = {english},
-  number = {20}
+  file = {/home/guillaume/Zotero/storage/C62M3B3W/Mary et al. - 2015 - Fission yeast kinesin-8 controls chromosome congre.pdf;/home/guillaume/Zotero/storage/ZDAM3UK2/3720.html}
 }
 
 @article{mason_tuning_2011,
@@ -2541,15 +2541,15 @@ @article{mason_tuning_2011
   date = {2011},
   journaltitle = {Current Opinion in Genetics and Development},
   volume = {21},
+  number = {5},
+  eprint = {21893409},
+  eprinttype = {pmid},
   pages = {671--679},
   issn = {0959437X},
   doi = {10.1016/j.gde.2011.08.002},
   url = {http://dx.doi.org/10.1016/j.gde.2011.08.002},
   abstract = {Throughout the lifespan of an organism, shape changes are necessary for cells to carry out their essential functions. Nowhere is this more dramatic than embryonic development and gastrulation, when cell shape changes drive large-scale rearrangements in tissue architecture to establish the body plan of the organism. A longstanding question for both cell and developmental biologists has been how are forces generated to change cell shape? Recent studies in both cell culture and developing embryos have combined live imaging, computational analysis, genetics, and biophysics to identify ratchet-like behaviors in actomyosin networks that operate to incrementally change cell shape, drive cell movement, and deform tissues. Our analysis of several cell shape changes leads us to propose four regulatory modules associated with ratchet-like deformations that are tuned to generate diverse cell behaviors, coordinating cell shape change across a tissue. © 2011 Elsevier Ltd.},
-  eprint = {21893409},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/PTM5KXFV/Mason, Martin - 2011 - Tuning cell shape change with contractile ratchets.pdf},
-  number = {5}
+  file = {/home/guillaume/Zotero/storage/PTM5KXFV/Mason, Martin - 2011 - Tuning cell shape change with contractile ratchets.pdf}
 }
 
 @article{mcintosh_motors_2012,
@@ -2559,15 +2559,15 @@ @article{mcintosh_motors_2012
   date = {2012-12},
   journaltitle = {Nature cell biology},
   volume = {14},
+  number = {12},
+  eprint = {23196840},
+  eprinttype = {pmid},
   pages = {1234},
   issn = {1476-4679},
   doi = {10.1038/ncb2649},
   url = {http://dx.doi.org/10.1038/ncb2649},
-  eprint = {23196840},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/VEDRM3I9/McIntosh - 2012 - Motors or dynamics what really moves chromosomes.pdf},
   keywords = {Chromosomes,Chromosomes: metabolism,Microtubules,Microtubules: metabolism,Mitosis,Mitosis: physiology,Muscle Contraction,Muscle Contraction: physiology},
-  number = {12}
+  file = {/home/guillaume/Zotero/storage/VEDRM3I9/McIntosh - 2012 - Motors or dynamics what really moves chromosomes.pdf}
 }
 
 @article{mckinleyCellularAspectRatio2018,
@@ -2581,8 +2581,8 @@ @article{mckinleyCellularAspectRatio2018
   doi = {10.7554/eLife.36739},
   url = {https://elifesciences.org/articles/36739},
   urldate = {2018-06-14},
-  file = {/home/guillaume/Zotero/storage/5LSRBHC5/McKinley et al. - 2018 - Cellular aspect ratio and cell division mechanics .pdf;/home/guillaume/Zotero/storage/39I7HUBW/36739.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/5LSRBHC5/McKinley et al. - 2018 - Cellular aspect ratio and cell division mechanics .pdf;/home/guillaume/Zotero/storage/39I7HUBW/36739.html}
 }
 
 @article{merksBuildingSimulationModels2013,
@@ -2591,14 +2591,14 @@ @article{merksBuildingSimulationModels2013
   date = {2013},
   journaltitle = {Methods Mol. Biol.},
   volume = {959},
+  eprint = {23299687},
+  eprinttype = {pmid},
   pages = {333--352},
   issn = {1940-6029},
   doi = {10.1007/978-1-62703-221-6_23},
   abstract = {Cell-based computational modeling and simulation are becoming invaluable tools in analyzing plant -development. In a cell-based simulation model, the inputs are behaviors and dynamics of individual cells and the rules describe responses to signals from adjacent cells. The outputs are the growing tissues, shapes and cell-differentiation patterns that emerge from the local, chemical and biomechanical cell-cell interactions. Here, we present a step-by-step, practical tutorial for building cell-based simulations of plant development with VirtualLeaf, a freely available, open-source software framework for modeling plant development. We show how to build a model of a growing tissue, a reaction-diffusion system on a growing domain, and an auxin transport model. The aim of VirtualLeaf is to make computational modeling better accessible to experimental plant biologists with relatively little computational background.},
-  eprint = {23299687},
-  eprinttype = {pmid},
-  keywords = {Computational Biology,Computer Simulation,Plant Development,Software,Systems Biology},
-  langid = {english}
+  langid = {english},
+  keywords = {Computational Biology,Computer Simulation,Plant Development,Software,Systems Biology}
 }
 
 @article{merksVirtualLeafOpensourceFramework2011,
@@ -2608,17 +2608,17 @@ @article{merksVirtualLeafOpensourceFramework2011
   date = {2011-02},
   journaltitle = {Plant Physiol},
   volume = {155},
+  number = {2},
+  eprint = {21148415},
+  eprinttype = {pmid},
   pages = {656--666},
   issn = {1532-2548},
   doi = {10.1104/pp.110.167619},
   abstract = {Plant organs, including leaves and roots, develop by means of a multilevel cross talk between gene regulation, patterned cell division and cell expansion, and tissue mechanics. The multilevel regulatory mechanisms complicate classic molecular genetics or functional genomics approaches to biological development, because these methodologies implicitly assume a direct relation between genes and traits at the level of the whole plant or organ. Instead, understanding gene function requires insight into the roles of gene products in regulatory networks, the conditions of gene expression, etc. This interplay is impossible to understand intuitively. Mathematical and computer modeling allows researchers to design new hypotheses and produce experimentally testable insights. However, the required mathematics and programming experience makes modeling poorly accessible to experimental biologists. Problem-solving environments provide biologically intuitive in silico objects ("cells", "regulation networks") required for setting up a simulation and present those to the user in terms of familiar, biological terminology. Here, we introduce the cell-based computer modeling framework VirtualLeaf for plant tissue morphogenesis. The current version defines a set of biologically intuitive C++ objects, including cells, cell walls, and diffusing and reacting chemicals, that provide useful abstractions for building biological simulations of developmental processes. We present a step-by-step introduction to building models with VirtualLeaf, providing basic example models of leaf venation and meristem development. VirtualLeaf-based models provide a means for plant researchers to analyze the function of developmental genes in the context of the biophysics of growth and patterning. VirtualLeaf is an ongoing open-source software project (http://virtualleaf.googlecode.com) that runs on Windows, Mac, and Linux.},
-  eprint = {21148415},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/BRMFRDIK/Merks et al. - 2011 - VirtualLeaf an open-source framework for cell-bas.pdf},
-  keywords = {Algorithms,Cell Wall,Computer Simulation,Models; Biological,Plant Development,Software,Systems Biology},
   langid = {english},
-  number = {2},
-  pmcid = {PMC3032457}
+  pmcid = {PMC3032457},
+  keywords = {Algorithms,Cell Wall,Computer Simulation,Models; Biological,Plant Development,Software,Systems Biology},
+  file = {/home/guillaume/Zotero/storage/BRMFRDIK/Merks et al. - 2011 - VirtualLeaf an open-source framework for cell-bas.pdf}
 }
 
 @article{mesaEpidermalStemCells2017,
@@ -2631,8 +2631,8 @@ @article{mesaEpidermalStemCells2017
   url = {http://biorxiv.org/content/early/2017/06/25/155408},
   urldate = {2017-06-26},
   abstract = {Many adult tissues are dynamically sustained by the rapid turnover of stem cells. Yet, how cell fates such as self-renewal and differentiation are orchestrated to achieve long-term homeostasis remains elusive. Studies utilizing clonal tracing experiments in multiple tissues have argued that while stem cell fate is balanced at the population level, individual cell fate - to divide or differentiate - is determined intrinsically by each cell seemingly at random. These studies leave open the question of how cell fates are regulated to achieve fate balance across the tissue. Stem cell fate choices could be made autonomously by each cell throughout the tissue or be the result of cell coordination. Here we developed a novel live tracking strategy that allowed recording of every division and differentiation event within a region of epidermis for a week. These measurements reveal that stem cell fates are not autonomous. Rather, direct neighbors undergo coupled opposite fate decisions. We further found a clear ordering of events, with self-renewal triggered by neighbor differentiation, but not vice-versa. Typically, around 1-2 days after cell delamination, a neighboring cell entered S/G2 phase and divided. Functional blocking of this local feedback showed that differentiation continues to occur in the absence of cell division, resulting in a rapid depletion of the epidermal stem cell pool. We thus demonstrate that the epidermis is maintained by nearest neighbor coordination of cell fates, rather than by asymmetric divisions or fine-tuned cell-autonomous stochastic fate choices. These findings establish differentiation-dependent division as a core feature of homeostatic control, and define the relevant time and length scales over which homeostasis is enforced in epithelial tissues.},
-  file = {/home/guillaume/Zotero/storage/KQF6Z9WM/Mesa et al. - 2017 - Epidermal stem cells self-renew upon neighboring d.pdf;/home/guillaume/Zotero/storage/QEWDX5R4/155408.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/KQF6Z9WM/Mesa et al. - 2017 - Epidermal stem cells self-renew upon neighboring d.pdf;/home/guillaume/Zotero/storage/QEWDX5R4/155408.html}
 }
 
 @article{messalTissueCurvatureApicobasal2019,
@@ -2646,8 +2646,8 @@ @article{messalTissueCurvatureApicobasal2019
   url = {https://www.nature.com/articles/s41586-019-0891-2},
   urldate = {2019-02-01},
   abstract = {Three-dimensional imaging of mouse pancreatic ducts before and after oncogenic transformation reveals that epithelial tumorigenesis is determined by the relationship between tissue curvature and apical–basal mechanical tension.},
-  file = {/home/guillaume/Zotero/storage/LWF98XGR/10.1038@s41586-019-0891-2.pdf;/home/guillaume/Zotero/storage/RBAI96Q2/s41586-019-0891-2.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/LWF98XGR/10.1038@s41586-019-0891-2.pdf;/home/guillaume/Zotero/storage/RBAI96Q2/s41586-019-0891-2.html}
 }
 
 @article{mietkeSelforganizedShapeDynamics2019,
@@ -2656,6 +2656,9 @@ @article{mietkeSelforganizedShapeDynamics2019
   date = {2019-01-02},
   journaltitle = {PNAS},
   volume = {116},
+  number = {1},
+  eprint = {30567977},
+  eprinttype = {pmid},
   pages = {29--34},
   publisher = {{National Academy of Sciences}},
   issn = {0027-8424, 1091-6490},
@@ -2663,12 +2666,9 @@ @article{mietkeSelforganizedShapeDynamics2019
   url = {https://www.pnas.org/content/116/1/29},
   urldate = {2020-06-03},
   abstract = {Mechanochemical processes in thin biological structures, such as the cellular cortex or epithelial sheets, play a key role during the morphogenesis of cells and tissues. In particular, they are responsible for the dynamical organization of active stresses that lead to flows and deformations of the material. Consequently, advective transport redistributes force-generating molecules and thereby contributes to a complex mechanochemical feedback loop. It has been shown in fixed geometries that this mechanism enables patterning, but the interplay of these processes with shape changes of the material remains to be explored. In this work, we study the fully self-organized shape dynamics using the theory of active fluids on deforming surfaces and develop a numerical approach to solve the corresponding force and torque balance equations. We describe the spontaneous generation of nontrivial surface shapes, shape oscillations, and directed surface flows that resemble peristaltic waves from self-organized, mechanochemical processes on the deforming surface. Our approach provides opportunities to explore the dynamics of self-organized active surfaces and can help to understand the role of shape as an integral element of the mechanochemical organization of morphogenetic processes.},
-  eprint = {30567977},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/GT7R8MB2/Mietke et al. - 2019 - Self-organized shape dynamics of active surfaces.pdf;/home/guillaume/Zotero/storage/GLBHZE5P/29.html},
-  keywords = {active fluids,morphogenesis,self-organization,surface mechanics},
   langid = {english},
-  number = {1}
+  keywords = {active fluids,morphogenesis,self-organization,surface mechanics},
+  file = {/home/guillaume/Zotero/storage/GT7R8MB2/Mietke et al. - 2019 - Self-organized shape dynamics of active surfaces.pdf;/home/guillaume/Zotero/storage/GLBHZE5P/29.html}
 }
 
 @article{mirams_chaste:_2013,
@@ -2678,15 +2678,15 @@ @article{mirams_chaste:_2013
   date = {2013-03-14},
   journaltitle = {PLOS Comput Biol},
   volume = {9},
+  number = {3},
   pages = {e1002970},
   issn = {1553-7358},
   doi = {10.1371/journal.pcbi.1002970},
   url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002970},
   urldate = {2016-11-04},
   abstract = {Chaste —  C ancer,  H eart  A nd  S oft  T issue  E nvironment — is an open source C++ library for the computational simulation of mathematical models developed for physiology and biology. Code development has been driven by two initial applications: cardiac electrophysiology and cancer development. A large number of cardiac electrophysiology studies have been enabled and performed, including high-performance computational investigations of defibrillation on realistic human cardiac geometries. New models for the initiation and growth of tumours have been developed. In particular, cell-based simulations have provided novel insight into the role of stem cells in the colorectal crypt. Chaste is constantly evolving and is now being applied to a far wider range of problems. The code provides modules for handling common scientific computing components, such as meshes and solvers for ordinary and partial differential equations (ODEs/PDEs). Re-use of these components avoids the need for researchers to ‘re-invent the wheel’ with each new project, accelerating the rate of progress in new applications. Chaste is developed using industrially-derived techniques, in particular test-driven development, to ensure code quality, re-use and reliability. In this article we provide examples that illustrate the types of problems Chaste can be used to solve, which can be run on a desktop computer. We highlight some scientific studies that have used or are using Chaste, and the insights they have provided. The source code, both for specific releases and the development version, is available to download under an open source Berkeley Software Distribution (BSD) licence at  http://www.cs.ox.ac.uk/chaste , together with details of a mailing list and links to documentation and tutorials.},
-  file = {/home/guillaume/Zotero/storage/XV7QURAQ/Mirams et al. - 2013 - Chaste An Open Source C++ Library for Computation.pdf;/home/guillaume/Zotero/storage/6468FD7J/article.html},
   keywords = {Cardiac electrophysiology,Computational Biology,Heart,Open source software,Oxygen,Simulation and modeling,Software development,Source code},
-  number = {3}
+  file = {/home/guillaume/Zotero/storage/XV7QURAQ/Mirams et al. - 2013 - Chaste An Open Source C++ Library for Computation.pdf;/home/guillaume/Zotero/storage/6468FD7J/article.html}
 }
 
 @incollection{miuraMeasurementsIntensityDynamics2020,
@@ -2695,6 +2695,7 @@ @incollection{miuraMeasurementsIntensityDynamics2020
   author = {Miura, Kota},
   editor = {Miura, Kota and Sladoje, Nataša},
   date = {2020},
+  series = {Learning {{Materials}} in {{Biosciences}}},
   pages = {9--32},
   publisher = {{Springer International Publishing}},
   location = {{Cham}},
@@ -2703,8 +2704,7 @@ @incollection{miuraMeasurementsIntensityDynamics2020
   urldate = {2019-10-24},
   abstract = {The aim of this chapter is to learn how to construct a workflow for measuring the fluorescence intensity localized to the nuclear envelope. For this purpose, the nucleus image is segmented to create a mask along the nuclear rim. The reader will learn a typical technique for automatically delineating the segmented area by post-processing using the mathematical morphology algorithm, and how to loop that piece of ImageJ macro and iterate through multiple image frames to measure changes in fluorescence intensity over time. This chapter is also a good guide for learning how to convert ImageJ macro commands recorded by the Command Recorder to a stand-alone ImageJ macro.},
   isbn = {978-3-030-22386-1},
-  langid = {english},
-  series = {Learning {{Materials}} in {{Biosciences}}}
+  langid = {english}
 }
 
 @incollection{miuraWorkflowsComponentsBioimage2020,
@@ -2713,6 +2713,7 @@ @incollection{miuraWorkflowsComponentsBioimage2020
   author = {Miura, Kota and Paul-Gilloteaux, Perrine and Tosi, Sébastien and Colombelli, Julien},
   editor = {Miura, Kota and Sladoje, Nataša},
   date = {2020},
+  series = {Learning {{Materials}} in {{Biosciences}}},
   pages = {1--7},
   publisher = {{Springer International Publishing}},
   location = {{Cham}},
@@ -2721,18 +2722,17 @@ @incollection{miuraWorkflowsComponentsBioimage2020
   urldate = {2019-10-24},
   abstract = {Definitions of three types of bioimage analysis software—Component, Collection, and Workflow—are introduced in this chapter. The aim is to promote the structured designing of bioimage analysis methods, and to improve related learning and teaching.},
   isbn = {978-3-030-22386-1},
-  langid = {english},
-  series = {Learning {{Materials}} in {{Biosciences}}}
+  langid = {english}
 }
 
 @online{mlUsingCellDeformation,
   title = {Using Cell Deformation and Motion to Predict Forces and Collective Behavior in Morphogenesis. - {{PubMed}} - {{NCBI}}},
   author = {ML, Merkel M. {and} Manning},
-  abstract = {Semin Cell Dev Biol. 2017 Jul;67:161-169. doi: 10.1016/j.semcdb.2016.07.029. Epub 2016 Aug 2. Review; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, N.I.H., Extramural},
   eprint = {27496334},
   eprinttype = {pubmed},
-  file = {/home/guillaume/Zotero/storage/L647QJSA/27496334.html},
-  langid = {english}
+  abstract = {Semin Cell Dev Biol. 2017 Jul;67:161-169. doi: 10.1016/j.semcdb.2016.07.029. Epub 2016 Aug 2. Review; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, N.I.H., Extramural},
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/L647QJSA/27496334.html}
 }
 
 @article{mongeraFluidtosolidJammingTransition2018,
@@ -2746,8 +2746,8 @@ @article{mongeraFluidtosolidJammingTransition2018
   url = {https://www.nature.com/articles/s41586-018-0479-2},
   urldate = {2018-09-13},
   abstract = {Cell collectives in embryonic tissues undergo a fluid-to-solid jamming transition, similar to those that occur in soft materials such as foams, emulsions and colloidal suspensions, to physically sculpt the vertebrate body axis.},
-  file = {/home/guillaume/Zotero/storage/76RJU8BF/10.1038@s41586-018-0479-2.pdf;/home/guillaume/Zotero/storage/ZZGU9PBF/s41586-018-0479-2.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/76RJU8BF/10.1038@s41586-018-0479-2.pdf;/home/guillaume/Zotero/storage/ZZGU9PBF/s41586-018-0479-2.html}
 }
 
 @article{monier_actomyosin-based_2010,
@@ -2756,14 +2756,14 @@ @article{monier_actomyosin-based_2010
   date = {2010},
   journaltitle = {Nature cell biology},
   volume = {12},
+  number = {1},
+  eprint = {19966783},
+  eprinttype = {pmid},
   pages = {60--65; sup pp 1--9},
   issn = {1465-7392},
   doi = {10.1038/ncb2005},
   abstract = {Partitioning tissues into compartments that do not intermix is essential for the correct morphogenesis of animal embryos and organs. Several hypotheses have been proposed to explain compartmental cell sorting, mainly differential adhesion, but also regulation of the cytoskeleton or of cell proliferation. Nevertheless, the molecular and cellular mechanisms that keep cells apart at boundaries remain unclear. Here we demonstrate, in early Drosophila melanogaster embryos, that actomyosin-based barriers stop cells from invading neighbouring compartments. Our analysis shows that cells can transiently invade neighbouring compartments, especially when they divide, but are then pushed back into their compartment of origin. Actomyosin cytoskeletal components are enriched at compartmental boundaries, forming cable-like structures when the epidermis is mitotically active. When MyoII (non-muscle myosin II) function is inhibited, including locally at the cable by chromophore-assisted laser inactivation (CALI), in live embryos, dividing cells are no longer pushed back, leading to compartmental cell mixing. We propose that local regulation of actomyosin contractibility, rather than differential adhesion, is the primary mechanism sorting cells at compartmental boundaries.},
-  eprint = {19966783},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/CI8C7T5P/Monier et al. - 2010 - An actomyosin-based barrier inhibits cell mixing at compartmental boundaries in Drosophila embryos(2).pdf;/home/guillaume/Zotero/storage/IEZ654K7/Monier et al. - 2010 - An actomyosin-based barrier inhibits cell mixing at compartmental boundaries in Drosophila embryos.pdf},
-  number = {1}
+  file = {/home/guillaume/Zotero/storage/CI8C7T5P/Monier et al. - 2010 - An actomyosin-based barrier inhibits cell mixing at compartmental boundaries in Drosophila embryos(2).pdf;/home/guillaume/Zotero/storage/IEZ654K7/Monier et al. - 2010 - An actomyosin-based barrier inhibits cell mixing at compartmental boundaries in Drosophila embryos.pdf}
 }
 
 @article{monier_apico-basal_2015,
@@ -2772,12 +2772,12 @@ @article{monier_apico-basal_2015
   date = {2015},
   journaltitle = {Nature},
   volume = {518},
+  number = {7538},
   pages = {245--248},
   issn = {0028-0836},
   doi = {10.1038/nature14152},
   url = {http://dx.doi.org/10.1038/nature14152},
-  file = {/home/guillaume/Zotero/storage/V65678H4/Monier et al. - 2015 - Apico-basal forces exerted by apoptotic cells drive epithelium folding.pdf},
-  number = {7538}
+  file = {/home/guillaume/Zotero/storage/V65678H4/Monier et al. - 2015 - Apico-basal forces exerted by apoptotic cells drive epithelium folding.pdf}
 }
 
 @article{mooreOMENGFFScalableFormat2021,
@@ -2792,8 +2792,8 @@ @article{mooreOMENGFFScalableFormat2021
   url = {https://www.biorxiv.org/content/10.1101/2021.03.31.437929v3},
   urldate = {2021-04-13},
   abstract = {{$<$}p{$>$}Biological imaging is one of the most innovative fields in the modern biological sciences. New imaging modalities, probes, and analysis tools appear every few months and often prove decisive for enabling new directions in scientific discovery. One feature of this dynamic field is the need to capture new types of data and data structures. While there is a strong drive to make scientific data Findable, Accessible, Interoperable and Reproducible (FAIR, 1), the rapid rate of innovation in imaging impedes the unification and adoption of standardized data formats. Despite this, the opportunities for sharing and integrating bioimaging data and, in particular, linking these data to other "omics" datasets have never been greater; therefore, to every extent possible, increasing "FAIRness" of bioimaging data is critical for maximizing scientific value, as well as for promoting openness and integrity. In the absence of a common, FAIR format, two approaches have emerged to provide access to bioimaging data: translation and conversion. On-the-fly translation produces a transient representation of bioimage metadata and binary data but must be repeated on each use. In contrast, conversion produces a permanent copy of the data, ideally in an open format that makes the data more accessible and improves performance and parallelization in reads and writes. Both approaches have been implemented successfully in the bioimaging community but both have limitations. At cloud-scale, those shortcomings limit scientific analysis and the sharing of results. We introduce here next-generation file formats (NGFF) as a solution to these challenges.{$<$}/p{$>$}},
-  file = {/home/guillaume/Zotero/storage/INB5RLFA/Moore et al. - 2021 - OME-NGFF scalable format strategies for interoper.pdf;/home/guillaume/Zotero/storage/9P268EGM/2021.03.31.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/INB5RLFA/Moore et al. - 2021 - OME-NGFF scalable format strategies for interoper.pdf;/home/guillaume/Zotero/storage/9P268EGM/2021.03.31.html}
 }
 
 @article{moreno-moralIntegrativeGenomicsSystems2016,
@@ -2802,17 +2802,17 @@ @article{moreno-moralIntegrativeGenomicsSystems2016
   date = {2016-10-01},
   journaltitle = {Disease Models \& Mechanisms},
   volume = {9},
+  number = {10},
+  eprint = {27736746},
+  eprinttype = {pmid},
   pages = {1097--1110},
   issn = {1754-8403, 1754-8411},
   doi = {10.1242/dmm.026104},
   url = {http://dmm.biologists.org/content/9/10/1097},
   urldate = {2016-11-02},
   abstract = {Complementary to traditional gene mapping approaches used to identify the hereditary components of complex diseases, integrative genomics and systems genetics have emerged as powerful strategies to decipher the key genetic drivers of molecular pathways that underlie disease. Broadly speaking, integrative genomics aims to link cellular-level traits (such as mRNA expression) to the genome to identify their genetic determinants. With the characterization of several cellular-level traits within the same system, the integrative genomics approach evolved into a more comprehensive study design, called systems genetics, which aims to unravel the complex biological networks and pathways involved in disease, and in turn map their genetic control points. The first fully integrated systems genetics study was carried out in rats, and the results, which revealed conserved trans -acting genetic regulation of a pro-inflammatory network relevant to type 1 diabetes, were translated to humans. Many studies using different organisms subsequently stemmed from this example. The aim of this Review is to describe the most recent advances in the fields of integrative genomics and systems genetics applied in the rat, with a focus on studies of complex diseases ranging from inflammatory to cardiometabolic disorders. We aim to provide the genetics community with a comprehensive insight into how the systems genetics approach came to life, starting from the first integrative genomics strategies [such as expression quantitative trait loci (eQTLs) mapping] and concluding with the most sophisticated gene network-based analyses in multiple systems and disease states. Although not limited to studies that have been directly translated to humans, we will focus particularly on the successful investigations in the rat that have led to primary discoveries of genes and pathways relevant to human disease.},
-  eprint = {27736746},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/42QGW9J5/Moreno-Moral et Petretto - 2016 - From integrative genomics to systems genetics in t.pdf;/home/guillaume/Zotero/storage/9SD92A7Q/1097.html},
   langid = {english},
-  number = {10}
+  file = {/home/guillaume/Zotero/storage/42QGW9J5/Moreno-Moral et Petretto - 2016 - From integrative genomics to systems genetics in t.pdf;/home/guillaume/Zotero/storage/9SD92A7Q/1097.html}
 }
 
 @article{munozStressdependentMorphogenesisContinuum2010,
@@ -2822,15 +2822,15 @@ @article{munozStressdependentMorphogenesisContinuum2010
   date = {2010-08},
   journaltitle = {Biomech Model Mechanobiol},
   volume = {9},
+  number = {4},
+  eprint = {20069442},
+  eprinttype = {pmid},
   pages = {451--467},
   issn = {1617-7940},
   doi = {10.1007/s10237-009-0187-9},
   abstract = {A set of equilibrium equations is derived for the stress-controlled shape change of cells due to the remodelling and growth of their internal architecture. The approach involves the decomposition of the deformation gradient into an active and a passive component; the former is allowed to include a growth process, while the latter is assumed to be hyperelastic and mass-preserving. The two components are coupled with a control function that provides the required feedback mechanism. The balance equations for general continua are derived and, using a variational approach, we deduce the equilibrium equations and study the effects of the control function on these equations. The results are applied to a truss system whose function is to simulate the cytoskeletal network constituted by myosin microfilaments and microtubules, which are found experimentally to control shape change in cells. Special attention is paid to the conditions that a thermodynamically consistent formulation should satisfy. The model is used to simulate the multicellular shape changes observed during ventral furrow invagination of the Drosophila melanogaster embryo. The results confirm that ventral furrow invagination can be achieved through stress control alone, without the need for other regulatory or signalling mechanisms. The model also reveals that the yolk plays a distinct role in the process, which is different to its role during invagination with externally imposed strains. In stress control, the incompressibility constraint of the yolk leads, via feedback, to the generation of a pressure in the ventral zone of the epithelium that eventually eases its rise and internalisation.},
-  eprint = {20069442},
-  eprinttype = {pmid},
-  keywords = {Actins,Animals,Biomechanical Phenomena,Drosophila melanogaster,Embryo; Nonmammalian,Epithelium,Models; Biological,Morphogenesis,Myosins,Stress; Mechanical,Time Factors},
   langid = {english},
-  number = {4}
+  keywords = {Actins,Animals,Biomechanical Phenomena,Drosophila melanogaster,Embryo; Nonmammalian,Epithelium,Models; Biological,Morphogenesis,Myosins,Stress; Mechanical,Time Factors}
 }
 
 @article{munozStressdependentMorphogenesisContinuum2010a,
@@ -2840,15 +2840,15 @@ @article{munozStressdependentMorphogenesisContinuum2010a
   date = {2010-08},
   journaltitle = {Biomech Model Mechanobiol},
   volume = {9},
+  number = {4},
+  eprint = {20069442},
+  eprinttype = {pmid},
   pages = {451--467},
   issn = {1617-7940},
   doi = {10.1007/s10237-009-0187-9},
   abstract = {A set of equilibrium equations is derived for the stress-controlled shape change of cells due to the remodelling and growth of their internal architecture. The approach involves the decomposition of the deformation gradient into an active and a passive component; the former is allowed to include a growth process, while the latter is assumed to be hyperelastic and mass-preserving. The two components are coupled with a control function that provides the required feedback mechanism. The balance equations for general continua are derived and, using a variational approach, we deduce the equilibrium equations and study the effects of the control function on these equations. The results are applied to a truss system whose function is to simulate the cytoskeletal network constituted by myosin microfilaments and microtubules, which are found experimentally to control shape change in cells. Special attention is paid to the conditions that a thermodynamically consistent formulation should satisfy. The model is used to simulate the multicellular shape changes observed during ventral furrow invagination of the Drosophila melanogaster embryo. The results confirm that ventral furrow invagination can be achieved through stress control alone, without the need for other regulatory or signalling mechanisms. The model also reveals that the yolk plays a distinct role in the process, which is different to its role during invagination with externally imposed strains. In stress control, the incompressibility constraint of the yolk leads, via feedback, to the generation of a pressure in the ventral zone of the epithelium that eventually eases its rise and internalisation.},
-  eprint = {20069442},
-  eprinttype = {pmid},
-  keywords = {Actins,Animals,Biomechanical Phenomena,Drosophila melanogaster,Embryo; Nonmammalian,Epithelium,Models; Biological,Morphogenesis,Myosins,Stress; Mechanical,Time Factors},
   langid = {english},
-  number = {4}
+  keywords = {Actins,Animals,Biomechanical Phenomena,Drosophila melanogaster,Embryo; Nonmammalian,Epithelium,Models; Biological,Morphogenesis,Myosins,Stress; Mechanical,Time Factors}
 }
 
 @article{murisic_discrete_2015,
@@ -2857,12 +2857,12 @@ @article{murisic_discrete_2015
   date = {2015-07},
   journaltitle = {Biophysical Journal},
   volume = {109},
+  number = {1},
   pages = {154--163},
   issn = {00063495},
   doi = {10.1016/j.bpj.2015.05.019},
   url = {http://www.cell.com/article/S0006349515005044/fulltext},
-  langid = {english},
-  number = {1}
+  langid = {english}
 }
 
 @article{murphyIndividualbasedMechanicalModel2018,
@@ -2875,8 +2875,8 @@ @article{murphyIndividualbasedMechanicalModel2018
   url = {https://www.biorxiv.org/content/early/2018/12/03/485276},
   urldate = {2018-12-03},
   abstract = {Mechanical heterogeneity in biological tissues, in particular mechanical stiffness, can be used to distinguish between healthy and diseased states. However, it is often difficult to explore relationships between cellular-level properties and tissue-level outcomes when biological experiments are performed at a single scale only. To overcome this difficulty we develop a multi-scale mathematical model which provides a clear framework to explore these connections across biological scales. Starting with an individual-based mechanical model of cell movement, we subsequently derive a novel coarse-grained system of partial differential equations governing the evolution of the cell density due to heterogeneous cellular properties. We demonstrate that solutions of the individual-based model converge to numerical solutions of the coarse-grained model, for both slowly-varying-in-space and rapidly-varying-in-space cellular properties. Applications of the model are discussed, including determining relative cellular-level properties and an interpretation of data from a breast cancer detection experiment.},
-  file = {/home/guillaume/Zotero/storage/5HIHI4XJ/Murphy et al. - 2018 - An individual-based mechanical model of cell movem.pdf;/home/guillaume/Zotero/storage/UJ2L3S2S/485276.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/5HIHI4XJ/Murphy et al. - 2018 - An individual-based mechanical model of cell movem.pdf;/home/guillaume/Zotero/storage/UJ2L3S2S/485276.html}
 }
 
 @article{mutzel_atom_2005-1,
@@ -2885,8 +2885,8 @@ @article{mutzel_atom_2005-1
   date = {2005},
   journaltitle = {Applied Physics B},
   volume = {80},
-  pages = {941--944},
-  number = {8}
+  number = {8},
+  pages = {941--944}
 }
 
 @article{negishiPhysicalAssociationNovel2016,
@@ -2895,17 +2895,17 @@ @article{negishiPhysicalAssociationNovel2016
   date = {2016-08-09},
   journaltitle = {eLife},
   volume = {5},
+  eprint = {27502556},
+  eprinttype = {pmid},
   pages = {e16550},
   issn = {2050-084X},
   doi = {10.7554/eLife.16550},
   url = {https://elifesciences.org/content/5/e16550v1},
   urldate = {2016-11-09},
   abstract = {A newly discovered membrane structure associates with one of the centrioles and affects two important centrosomal events in epidermal cells – ciliary positioning and spindle orientation – through a physical interaction.},
-  eprint = {27502556},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/IKT9HZFZ/Negishi et al. - 2016 - Physical association between a novel plasma-membra.pdf;/home/guillaume/Zotero/storage/GHFJXAQN/e16550.html},
+  langid = {english},
   keywords = {<italic>C. intestinalis</italic>,Ciliary positioning,spindle orientation},
-  langid = {english}
+  file = {/home/guillaume/Zotero/storage/IKT9HZFZ/Negishi et al. - 2016 - Physical association between a novel plasma-membra.pdf;/home/guillaume/Zotero/storage/GHFJXAQN/e16550.html}
 }
 
 @article{nematbakhshMultiscaleComputationalStudy2017,
@@ -2914,15 +2914,15 @@ @article{nematbakhshMultiscaleComputationalStudy2017
   date = {2017-05-22},
   journaltitle = {PLOS Computational Biology},
   volume = {13},
+  number = {5},
   pages = {e1005533},
   issn = {1553-7358},
   doi = {10.1371/journal.pcbi.1005533},
   url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005533},
   urldate = {2017-06-01},
   abstract = {Author summary Mitotic rounding (MR) during cell division which is critical for the robust segregation of chromosomes into daughter cells, plays important roles in tissue growth and morphogenesis, and is frequently perturbed in cancerous cells. Mechanisms of MR have been investigated in individual cultured cells, but mechanisms regulating MR in tissues are still poorly understood. We developed and calibrated an advanced subcellular element-based computational model called Epi-Scale that enables quantitative testing of hypothesized mechanisms governing epithelial cell behavior within the developing tissue microenvironment. Regression analysis of predictive model simulation results reveals the relative contributions of osmotic pressure, cell-cell adhesion and cortical stiffness to mitotic rounding and establishes a novel mechanism for ensuring robustness in mitotic rounding within densely packed epithelia.},
-  file = {/home/guillaume/Zotero/storage/JC6PH85J/article.pdf;/home/guillaume/Zotero/storage/QJ4XQHPS/Nematbakhsh et al. - 2017 - Multi-scale computational study of the mechanical .pdf},
   keywords = {a,b,C,d,e,f,g,h,i,l,m,n,o,p,r,S,t,u,v,y},
-  number = {5}
+  file = {/home/guillaume/Zotero/storage/JC6PH85J/article.pdf;/home/guillaume/Zotero/storage/QJ4XQHPS/Nematbakhsh et al. - 2017 - Multi-scale computational study of the mechanical .pdf}
 }
 
 @article{nestor-bergmannAdhesionDynamicsRegulate2021,
@@ -2936,8 +2936,8 @@ @article{nestor-bergmannAdhesionDynamicsRegulate2021
   url = {https://www.biorxiv.org/content/10.1101/2021.04.11.439313v1},
   urldate = {2021-04-12},
   abstract = {{$<$}p{$>$}Cell intercalation is a key cell behaviour of morphogenesis and wound healing, where local cell neighbour exchanges can cause dramatic tissue deformations such as body axis extension. Here, we develop a mechanical model to understand active cell intercalation behaviours in the context of an epithelial tissue. Extending existing descriptions, such as vertex models, the junctional actomyosin cortex of every cell is modelled as a continuum morphoelastic rod, explicitly representing cortices facing each other at bicellular junctions. Cells are described directly in terms of the key subcellular constituents that drive dynamics, with localised stresses from the contractile actomyosin cortex and adhesion molecules coupling apposed cortices. This multi-scale apposed-cortex formulation reveals key behaviours that drive tissue dynamics, such as cell-cell shearing and flow of junctional material past cell vertices. We show that cell neighbour exchanges can be driven by purely junctional mechanisms. Active contractility and viscous turnover in a single bicellular junction are sufficient to shrink and remove a junction. Next, the 4-way vertex is resolved and a new, orthogonal junction extends passively. The adhesion timescale defines a frictional viscosity that is an important regulator of these dynamics, modulating tension transmission in the tissue as well as the speeds of junction shrinkage and growth. The model additionally predicts that rosettes, which form when a vertex becomes common to many cells, are likely to occur in active tissues with high adhesive friction.{$<$}/p{$>$}},
-  file = {/home/guillaume/Zotero/storage/XXFEVU36/Nestor-Bergmann et al. - 2021 - Adhesion dynamics regulate cell intercalation beha.pdf;/home/guillaume/Zotero/storage/DGSAINY4/2021.04.11.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/XXFEVU36/Nestor-Bergmann et al. - 2021 - Adhesion dynamics regulate cell intercalation beha.pdf;/home/guillaume/Zotero/storage/DGSAINY4/2021.04.11.html}
 }
 
 @article{nikolicHumanEmbryonicLung2017,
@@ -2952,8 +2952,8 @@ @article{nikolicHumanEmbryonicLung2017
   url = {https://elifesciences.org/articles/26575},
   urldate = {2017-09-08},
   abstract = {Improved characterisation of human embryonic lung development highlights human-mouse differences and facilitates the development of defined culture conditions for the expansion of self-renewing, multipotent human lung epithelial progenitor cells.},
-  file = {/home/guillaume/Zotero/storage/ZMFCX3NB/Nikolić et al. - 2017 - Human embryonic lung epithelial tips are multipote.pdf;/home/guillaume/Zotero/storage/TVTT7FR9/26575.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/ZMFCX3NB/Nikolić et al. - 2017 - Human embryonic lung epithelial tips are multipote.pdf;/home/guillaume/Zotero/storage/TVTT7FR9/26575.html}
 }
 
 @inproceedings{odwyer_advancing_2005,
@@ -2971,8 +2971,8 @@ @article{odwyer_atomic_2005
   date = {2005},
   journaltitle = {Nanotechnology},
   volume = {16},
-  pages = {1536},
-  number = {9}
+  number = {9},
+  pages = {1536}
 }
 
 @article{odwyer_nature_2004,
@@ -2981,8 +2981,8 @@ @article{odwyer_nature_2004
   date = {2004},
   journaltitle = {Langmuir},
   volume = {20},
-  pages = {8172--8182},
-  number = {19}
+  number = {19},
+  pages = {8172--8182}
 }
 
 @article{odwyer_writing_2005,
@@ -2991,8 +2991,8 @@ @article{odwyer_writing_2005
   date = {2005},
   journaltitle = {Journal of applied physics},
   volume = {97},
-  pages = {114309},
-  number = {11}
+  number = {11},
+  pages = {114309}
 }
 
 @article{okuda_modeling_2015,
@@ -3012,12 +3012,12 @@ @article{okuda_reversible_2013
   date = {2013-08},
   journaltitle = {Biomechanics and Modeling in Mechanobiology},
   volume = {12},
+  number = {4},
   pages = {627--644},
   issn = {1617-7959},
   doi = {10.1007/s10237-012-0430-7},
   url = {http://link.springer.com/10.1007/s10237-012-0430-7},
-  file = {/home/guillaume/Zotero/storage/SCESWZFP/Okuda et al. - 2013 - Reversible network reconnection model for simulating large deformation in dynamic tissue morphogenesis.pdf},
-  number = {4}
+  file = {/home/guillaume/Zotero/storage/SCESWZFP/Okuda et al. - 2013 - Reversible network reconnection model for simulating large deformation in dynamic tissue morphogenesis.pdf}
 }
 
 @article{okuda_vertex_2014,
@@ -3026,13 +3026,13 @@ @article{okuda_vertex_2014
   date = {2014},
   journaltitle = {Biomechanics and Modeling in Mechanobiology},
   volume = {14},
+  number = {2},
   pages = {413--425},
   issn = {1617-7959},
   doi = {10.1007/s10237-014-0613-5},
   url = {http://link.springer.com/10.1007/s10237-014-0613-5},
-  file = {/home/guillaume/Zotero/storage/NDR3V2CA/Okuda et al. - 2014 - Vertex dynamics simulations of viscosity-dependent deformation during tissue morphogenesis.pdf},
   keywords = {Computational biomechanics,deformation process,Developmental,dynamic,model,Multicellular morphogenesis,vertex dynamics,viscous property},
-  number = {2}
+  file = {/home/guillaume/Zotero/storage/NDR3V2CA/Okuda et al. - 2014 - Vertex dynamics simulations of viscosity-dependent deformation during tissue morphogenesis.pdf}
 }
 
 @article{okudaThreedimensionalVertexModel2015,
@@ -3041,14 +3041,14 @@ @article{okudaThreedimensionalVertexModel2015
   date = {2015},
   journaltitle = {Biophys Physicobiol},
   volume = {12},
+  eprint = {27493850},
+  eprinttype = {pmid},
   pages = {13--20},
   doi = {10.2142/biophysico.12.0_13},
   abstract = {During morphogenesis, various cellular activities are spatiotemporally coordinated on the protein regulatory background to construct the complicated, three-dimensional (3D) structures of organs. Computational simulations using 3D vertex models have been the focus of efforts to approach the mechanisms underlying 3D multicellular constructions, such as dynamics of the 3D monolayer or multilayer cell sheet like epithelia as well as the 3D compacted cell aggregate, including dynamic changes in layer structures. 3D vertex models enable the quantitative simulation of multicellular morphogenesis on the basis of single-cell mechanics, with complete control of various cellular activities such as cell contraction, growth, rearrangement, division, and death. This review describes the general use of the 3D vertex model, along with its applications to several simplified problems of developmental phenomena.},
-  eprint = {27493850},
-  eprinttype = {pmid},
-  keywords = {3D vertex model,biomechanics,computational simulation,developmental biology,reversible network reconnection},
   langid = {english},
-  pmcid = {PMC4736843}
+  pmcid = {PMC4736843},
+  keywords = {3D vertex model,biomechanics,computational simulation,developmental biology,reversible network reconnection}
 }
 
 @article{OptokineticNystagmusDetection2015,
@@ -3056,16 +3056,16 @@ @article{OptokineticNystagmusDetection2015
   date = {2015-03-05},
   journaltitle = {IEEE J Transl Eng Health Med},
   volume = {3},
+  eprint = {27170889},
+  eprinttype = {pmid},
   issn = {2168-2372},
   doi = {10.1109/JTEHM.2015.2410286},
   url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4848063/},
   urldate = {2019-03-13},
   abstract = {The detection of vision problems in early childhood can prevent neurodevelopmental disorders such as amblyopia. However, accurate clinical assessment of visual function in young children is challenging. optokinetic nystagmus (OKN) is a reflexive sawtooth motion of the eye that occurs in response to drifting stimuli, that may allow for objective measurement of visual function in young children if appropriate child-friendly eye tracking techniques are available. In this paper, we present offline tools to detect the presence and direction of the optokinetic reflex in children using consumer grade video equipment. Our methods are tested on video footage of children (\textbackslash documentclass[12pt]\{minimal\} \textbackslash usepackage\{amsmath\} \textbackslash usepackage\{wasysym\}  \textbackslash usepackage\{amsfonts\}  \textbackslash usepackage\{amssymb\}  \textbackslash usepackage\{amsbsy\} \textbackslash usepackage\{upgreek\} \textbackslash usepackage\{mathrsfs\} \textbackslash setlength\{\textbackslash oddsidemargin\}\{-69pt\} \textbackslash begin\{document\} \vphantom\{\}\{\}\$N = 5\$ \textbackslash end\{document\} children and 20 trials) taken as they freely observed visual stimuli that induced horizontal OKN. Using results from an experienced observer as a baseline, we found the sensitivity and specificity of our OKN detection method to be 89.13\% and 98.54\%, respectively, across all trials. Our OKN detection results also compared well (85\%) with results obtained from a clinically trained assessor. In conclusion, our results suggest that OKN presence and direction can be measured objectively in children using consumer grade equipment, and readily implementable algorithms.},
-  eprint = {27170889},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/6RCDPXZ5/2015 - An Optokinetic Nystagmus Detection Method for Use .pdf},
+  pmcid = {PMC4848063},
   keywords = {nystagmus},
-  pmcid = {PMC4848063}
+  file = {/home/guillaume/Zotero/storage/6RCDPXZ5/2015 - An Optokinetic Nystagmus Detection Method for Use .pdf}
 }
 
 @article{osborneComparingIndividualBasedApproaches2016,
@@ -3077,8 +3077,8 @@ @article{osborneComparingIndividualBasedApproaches2016
   url = {http://biorxiv.org/lookup/doi/10.1101/074351},
   urldate = {2019-09-02},
   abstract = {Authors’ contributions: JO and AF conceived of the study, designed the study, coordinated the study, carried out the computational modelling and drafted the manuscript. JP contributed to the computational modelling and helped draft the manuscript. PM and DG conceived of the study, designed the study and helped draft the manuscript. All authors gave final approval for publication.},
-  file = {/home/guillaume/Zotero/storage/Z9AYEYR5/Osborne et al. - 2016 - Comparing Individual-Based Approaches to Modelling.pdf},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/Z9AYEYR5/Osborne et al. - 2016 - Comparing Individual-Based Approaches to Modelling.pdf}
 }
 
 @article{osborneComparingIndividualbasedApproaches2017,
@@ -3087,6 +3087,7 @@ @article{osborneComparingIndividualbasedApproaches2017
   date = {2017-02-13},
   journaltitle = {PLOS Computational Biology},
   volume = {13},
+  number = {2},
   pages = {e1005387},
   publisher = {{Public Library of Science}},
   issn = {1553-7358},
@@ -3094,10 +3095,9 @@ @article{osborneComparingIndividualbasedApproaches2017
   url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005387},
   urldate = {2021-01-29},
   abstract = {The coordinated behaviour of populations of cells plays a central role in tissue growth and renewal. Cells react to their microenvironment by modulating processes such as movement, growth and proliferation, and signalling. Alongside experimental studies, computational models offer a useful means by which to investigate these processes. To this end a variety of cell-based modelling approaches have been developed, ranging from lattice-based cellular automata to lattice-free models that treat cells as point-like particles or extended shapes. However, it remains unclear how these approaches compare when applied to the same biological problem, and what differences in behaviour are due to different model assumptions and abstractions. Here, we exploit the availability of an implementation of five popular cell-based modelling approaches within a consistent computational framework, Chaste (http://www.cs.ox.ac.uk/chaste). This framework allows one to easily change constitutive assumptions within these models. In each case we provide full details of all technical aspects of our model implementations. We compare model implementations using four case studies, chosen to reflect the key cellular processes of proliferation, adhesion, and short- and long-range signalling. These case studies demonstrate the applicability of each model and provide a guide for model usage.},
-  file = {/home/guillaume/Zotero/storage/NHMHBS6N/Osborne et al. - 2017 - Comparing individual-based approaches to modelling.pdf;/home/guillaume/Zotero/storage/8BZPKIA2/article.html},
-  keywords = {Cell cycle and cell division,Cell differentiation,Cell movement,Cell proliferation,Cloning,Morphogens,Notch signaling,Simulation and modeling},
   langid = {english},
-  number = {2}
+  keywords = {Cell cycle and cell division,Cell differentiation,Cell movement,Cell proliferation,Cloning,Morphogens,Notch signaling,Simulation and modeling},
+  file = {/home/guillaume/Zotero/storage/NHMHBS6N/Osborne et al. - 2017 - Comparing individual-based approaches to modelling.pdf;/home/guillaume/Zotero/storage/8BZPKIA2/article.html}
 }
 
 @article{osterfield_three-dimensional_2013,
@@ -3106,14 +3106,14 @@ @article{osterfield_three-dimensional_2013
   date = {2013},
   journaltitle = {Developmental Cell},
   volume = {24},
+  number = {4},
+  eprint = {23449472},
+  eprinttype = {pmid},
   pages = {400--410},
   issn = {15345807},
   doi = {10.1016/j.devcel.2013.01.017},
   abstract = {Morphogenesis of the respiratory appendages on eggshells of Drosophila species provides a powerful experimental system for studying how cell sheets give rise to complex three-dimensional structures. In Drosophila melanogaster, each of the two tubular eggshell appendages is derived from a primordium comprising two distinct cell types. Using live imaging and three-dimensional image reconstruction, we demonstrate that the transformation of this two-dimensional primordium into a tube involves out-of-plane bending followed by a sequence of spatially ordered cell intercalations. These morphological transformations correlate with the appearance of complementary distributions of myosin and Bazooka in the primordium. These distributions suggest that a two-dimensional pattern of line tensions along cell-cell edges on the apical side of the epithelium is sufficient to produce the observed changes in morphology. Computational modeling shows that this mechanism could explain the main features of tissue deformation and cell rearrangements observed during three-dimensional morphogenesis. © 2013 Elsevier Inc.},
-  eprint = {23449472},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/VG6ZDVZ7/Osterfield et al. - 2013 - Three-Dimensional Epithelial Morphogenesis in the Developing Drosophila Egg.pdf},
-  number = {4}
+  file = {/home/guillaume/Zotero/storage/VG6ZDVZ7/Osterfield et al. - 2013 - Three-Dimensional Epithelial Morphogenesis in the Developing Drosophila Egg.pdf}
 }
 
 @article{paluch_biology_2009,
@@ -3122,15 +3122,15 @@ @article{paluch_biology_2009
   date = {2009},
   journaltitle = {Current biology : CB},
   volume = {19},
+  number = {17},
+  eprint = {19906581},
+  eprinttype = {pmid},
   pages = {R790--R799},
   issn = {1879-0445},
   doi = {10.1016/j.cub.2009.07.029},
   url = {http://dx.doi.org/10.1016/j.cub.2009.07.029},
   abstract = {Together with cell growth, division and death, changes in cell shape are of central importance for tissue morphogenesis during development. Cell shape is the product of a cell's material and active properties balanced by external forces. Control of cell shape, therefore, relies on both tight regulation of intracellular mechanics and the cell's physical interaction with its environment. In this review, we first discuss the biological and physical mechanisms of cell shape control. We next examine a number of developmental processes in which cell shape change - either individually or in a coordinated manner - drives embryonic morphogenesis and discuss how cell shape is controlled in these processes. Finally, we emphasize that cell shape control during tissue morphogenesis can only be fully understood by using a combination of cellular, molecular, developmental and biophysical approaches.},
-  eprint = {19906581},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/WZ7PRX2Z/Paluch, Heisenberg - 2009 - Biology and physics of cell shape changes in development.pdf},
-  number = {17}
+  file = {/home/guillaume/Zotero/storage/WZ7PRX2Z/Paluch, Heisenberg - 2009 - Biology and physics of cell shape changes in development.pdf}
 }
 
 @online{Panorama2015Industrie,
@@ -3138,8 +3138,8 @@ @online{Panorama2015Industrie
   url = {https://www.scribd.com/doc/295447535/Panorama-2015-de-l-Industrie-Des-Sciences-de-La-Vie-en-France},
   urldate = {2016-11-08},
   abstract = {France Biotech présente la 13ème édition du Panorama 2015 des Sciences de la Vie en France®},
-  file = {/home/guillaume/Zotero/storage/24BH93P9/Panorama-2015-de-l-Industrie-Des-Sciences-de-La-Vie-en-France.html},
-  organization = {{Scribd}}
+  organization = {{Scribd}},
+  file = {/home/guillaume/Zotero/storage/24BH93P9/Panorama-2015-de-l-Industrie-Des-Sciences-de-La-Vie-en-France.html}
 }
 
 @article{pedregosaScikitlearnMachineLearning2011,
@@ -3162,9 +3162,9 @@ @online{PhysicalReview
   url = {https://journals.aps.org/pre/},
   urldate = {2018-11-16},
   abstract = {Physical Review E},
-  file = {/home/guillaume/Zotero/storage/6FSD4P7N/pre.html},
   langid = {english},
-  organization = {{Physical Review E}}
+  organization = {{Physical Review E}},
+  file = {/home/guillaume/Zotero/storage/6FSD4P7N/pre.html}
 }
 
 @online{PhysRev98,
@@ -3187,17 +3187,17 @@ @article{polyakovPassiveMechanicalForces2014
   date = {2014-08-19},
   journaltitle = {Biophys J},
   volume = {107},
+  number = {4},
+  eprint = {25140436},
+  eprinttype = {pmid},
   pages = {998--1010},
   issn = {0006-3495},
   doi = {10.1016/j.bpj.2014.07.013},
   url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4142243/},
   urldate = {2018-01-16},
   abstract = {During Drosophila gastrulation, the ventral mesodermal cells constrict their apices, undergo a series of coordinated cell-shape changes to form a ventral furrow (VF) and are subsequently internalized. Although it has been well documented that apical constriction is necessary for VF formation, the mechanism by which apical constriction transmits forces throughout the bulk tissue of the cell remains poorly understood. In this work, we develop a computational vertex model to investigate the role of the passive mechanical properties of the cellular blastoderm during gastrulation. We introduce to our knowledge novel data that confirm that the volume of apically constricting cells is conserved throughout the entire course of invagination. We show that maintenance of this constant volume is sufficient to generate invagination as a passive response to apical constriction when it is combined with region-specific elasticities in the membranes surrounding individual cells. We find that the specific sequence of cell-shape changes during VF formation is critically controlled by the stiffness of the lateral and basal membrane surfaces. In particular, our model demonstrates that a transition in basal rigidity is sufficient to drive VF formation along the same sequence of cell-shape change that we observed in the actual embryo, with no active force generation required other than apical constriction.},
-  eprint = {25140436},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/WVKXVGAE/Polyakov et al. - 2014 - Passive Mechanical Forces Control Cell-Shape Chang.pdf},
-  number = {4},
-  pmcid = {PMC4142243}
+  pmcid = {PMC4142243},
+  file = {/home/guillaume/Zotero/storage/WVKXVGAE/Polyakov et al. - 2014 - Passive Mechanical Forces Control Cell-Shape Chang.pdf}
 }
 
 @article{popkin_physics_2016,
@@ -3206,11 +3206,11 @@ @article{popkin_physics_2016
   date = {2016-01},
   journaltitle = {Nature},
   volume = {529},
+  number = {7584},
   pages = {16--18},
   issn = {0028-0836},
   doi = {10.1038/529016a},
-  url = {http://www.nature.com/news/the-physics-of-life-1.19105},
-  number = {7584}
+  url = {http://www.nature.com/news/the-physics-of-life-1.19105}
 }
 
 @article{pouilleHydrodynamicSimulationMulticellular2008,
@@ -3219,15 +3219,15 @@ @article{pouilleHydrodynamicSimulationMulticellular2008
   date = {2008},
   journaltitle = {Phys. Biol.},
   volume = {5},
+  number = {1},
   pages = {015005},
   issn = {1478-3975},
   doi = {10.1088/1478-3975/5/1/015005},
   url = {http://stacks.iop.org/1478-3975/5/i=1/a=015005},
   urldate = {2017-03-04},
   abstract = {The mechanical aspects of embryonic morphogenesis have been widely analysed by numerical simulations of invagination in sea urchins and Drosophila gastrulation. Finite element models, which describe the tissue as a continuous medium, lead to the global invagination morphogenesis observed in vivo . Here we develop a simulation of multicellular embryo invagination that allows access to both cellular and multicellular mechanical behaviours of the embryo. In this model, the tissue is composed of adhesive individual cells, in which shape change dynamics is governed by internal acto-myosin forces and the hydrodynamic flow associated with membrane movements. We investigated the minimal structural and force elements sufficient to phenocopy mesoderm invagination. The minimal structures are cell membranes characterized by an acto-myosin cortical tension and connected by apical and basal junctions and an acto-myosin contractile ring connected to the apical junctions. An increase in the apical–cortical surface tension is the only control parameter change required to phenocopy most known multicellular and cellular shape changes of Drosophila gastrulation. Specifically, behaviours observed in vivo , including apical junction movements at the onset of gastrulation, cell elongation and subsequent shortening during invagination, and the development of a dorso-ventral gradient of thickness of the embryo, are predicted by this model as passive mechanical consequences of the genetically controlled increase in the apical surface tension in invaginating mesoderm cells, thus demonstrating the accurate description of structures at both global and single cell scales.},
-  file = {/home/guillaume/Zotero/storage/3CS6542J/pouille2008.pdf},
   langid = {english},
-  number = {1}
+  file = {/home/guillaume/Zotero/storage/3CS6542J/pouille2008.pdf}
 }
 
 @article{ragkousiCellCycleCoupledOscillationsApical2017,
@@ -3236,17 +3236,17 @@ @article{ragkousiCellCycleCoupledOscillationsApical2017
   date = {2017-05-08},
   journaltitle = {Current Biology},
   volume = {27},
+  number = {9},
+  eprint = {28457868},
+  eprinttype = {pmid},
   pages = {1381--1386},
   issn = {0960-9822},
   doi = {10.1016/j.cub.2017.03.064},
   url = {http://www.cell.com/current-biology/abstract/S0960-9822(17)30393-7},
   urldate = {2017-05-15},
-  eprint = {28457868},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/XU24MCAU/S0960-9822(17)30393-7.html},
-  keywords = {apical polarity,cell cycle,Cnidarian,compaction,embryonic epithelium,evolution,Moesin,oscillations},
   langid = {english},
-  number = {9}
+  keywords = {apical polarity,cell cycle,Cnidarian,compaction,embryonic epithelium,evolution,Moesin,oscillations},
+  file = {/home/guillaume/Zotero/storage/XU24MCAU/S0960-9822(17)30393-7.html}
 }
 
 @article{ramasubramanianComputationalModelingMorphogenesis2008,
@@ -3255,15 +3255,15 @@ @article{ramasubramanianComputationalModelingMorphogenesis2008
   date = {2008-04-01},
   journaltitle = {Biomech Model Mechanobiol},
   volume = {7},
+  number = {2},
   pages = {77--91},
   issn = {1617-7959, 1617-7940},
   doi = {10.1007/s10237-007-0077-y},
   url = {https://link.springer.com/article/10.1007/s10237-007-0077-y},
   urldate = {2017-03-06},
   abstract = {Mechanical forces cause changes in form during embryogenesis and likely play a role in regulating these changes. This paper explores the idea that changes in homeostatic tissue stress (target stress), possibly modulated by genes, drive some morphogenetic processes. Computational models are presented to illustrate how regional variations in target stress can cause a range of complex behaviors involving the bending of epithelia. These models include growth and cytoskeletal contraction regulated by stress-based mechanical feedback. All simulations were carried out using the commercial finite element code ABAQUS, with growth and contraction included by modifying the zero-stress state in the material constitutive relations. Results presented for bending of bilayered beams and invagination of cylindrical and spherical shells provide insight into some of the mechanical aspects that must be considered in studying morphogenetic mechanisms.},
-  file = {/home/guillaume/Zotero/storage/Z2PTMXHT/ramasubramanian2007.pdf;/home/guillaume/Zotero/storage/XE757STQ/s10237-007-0077-y.html},
   langid = {english},
-  number = {2}
+  file = {/home/guillaume/Zotero/storage/Z2PTMXHT/ramasubramanian2007.pdf;/home/guillaume/Zotero/storage/XE757STQ/s10237-007-0077-y.html}
 }
 
 @article{ramasubramanianComputationalModelingMorphogenesis2008a,
@@ -3272,15 +3272,15 @@ @article{ramasubramanianComputationalModelingMorphogenesis2008a
   date = {2008-04-01},
   journaltitle = {Biomech Model Mechanobiol},
   volume = {7},
+  number = {2},
   pages = {77--91},
   issn = {1617-7959, 1617-7940},
   doi = {10.1007/s10237-007-0077-y},
   url = {https://link.springer.com/article/10.1007/s10237-007-0077-y},
   urldate = {2017-03-06},
   abstract = {Mechanical forces cause changes in form during embryogenesis and likely play a role in regulating these changes. This paper explores the idea that changes in homeostatic tissue stress (target stress), possibly modulated by genes, drive some morphogenetic processes. Computational models are presented to illustrate how regional variations in target stress can cause a range of complex behaviors involving the bending of epithelia. These models include growth and cytoskeletal contraction regulated by stress-based mechanical feedback. All simulations were carried out using the commercial finite element code ABAQUS, with growth and contraction included by modifying the zero-stress state in the material constitutive relations. Results presented for bending of bilayered beams and invagination of cylindrical and spherical shells provide insight into some of the mechanical aspects that must be considered in studying morphogenetic mechanisms.},
-  file = {/home/guillaume/Zotero/storage/SDPQDQJM/s10237-007-0077-y.html},
   langid = {english},
-  number = {2}
+  file = {/home/guillaume/Zotero/storage/SDPQDQJM/s10237-007-0077-y.html}
 }
 
 @article{ranftFluidizationTissuesCell2010,
@@ -3289,17 +3289,17 @@ @article{ranftFluidizationTissuesCell2010
   date = {2010-12-07},
   journaltitle = {Proc Natl Acad Sci U S A},
   volume = {107},
+  number = {49},
+  eprint = {21078958},
+  eprinttype = {pmid},
   pages = {20863--20868},
   issn = {1091-6490},
   doi = {10.1073/pnas.1011086107},
   abstract = {During the formation of tissues, cells organize collectively by cell division and apoptosis. The multicellular dynamics of such systems is influenced by mechanical conditions and can give rise to cell rearrangements and movements. We develop a continuum description of tissue dynamics, which describes the stress distribution and the cell flow field on large scales. In the absence of division and apoptosis, we consider the tissue to behave as an elastic solid. Cell division and apoptosis introduce stress sources that, in general, are anisotropic. By combining cell number balance with dynamic equations for the stress source, we show that the tissue effectively behaves as a viscoelastic fluid with a relaxation time set by the rates of division and apoptosis. If the system is confined in a fixed volume, it reaches a homeostatic state in which division and apoptosis balance. In this state, cells undergo a diffusive random motion driven by the stochasticity of division and apoptosis. We calculate the expression for the effective diffusion coefficient as a function of the tissue parameters and compare our results concerning both diffusion and viscosity to simulations of multicellular systems using dissipative particle dynamics.},
-  eprint = {21078958},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/25W3SWMI/Ranft et al. - 2010 - Fluidization of tissues by cell division and apopt.pdf},
-  keywords = {Apoptosis,Body Fluids,Cell Division,Computer Simulation,Diffusion,Models; Biological,Rheology,Stress; Mechanical,Viscosity},
   langid = {english},
-  number = {49},
-  pmcid = {PMC3000289}
+  pmcid = {PMC3000289},
+  keywords = {Apoptosis,Body Fluids,Cell Division,Computer Simulation,Diffusion,Models; Biological,Rheology,Stress; Mechanical,Viscosity},
+  file = {/home/guillaume/Zotero/storage/25W3SWMI/Ranft et al. - 2010 - Fluidization of tissues by cell division and apopt.pdf}
 }
 
 @article{rauziEmbryoscaleTissueMechanics2015,
@@ -3308,15 +3308,15 @@ @article{rauziEmbryoscaleTissueMechanics2015
   date = {2015-10-26},
   journaltitle = {Nat Commun},
   volume = {6},
+  eprint = {26497898},
+  eprinttype = {pmid},
   pages = {8677},
   issn = {2041-1723},
   doi = {10.1038/ncomms9677},
   abstract = {Morphogenesis of an organism requires the development of its parts to be coordinated in time and space. While past studies concentrated on defined cell populations, a synthetic view of the coordination of these events in a whole organism is needed for a full understanding. Drosophila gastrulation begins with the embryo forming a ventral furrow, which is eventually internalized. It is not understood how the rest of the embryo participates in this process. Here we use multiview selective plane illumination microscopy coupled with infrared laser manipulation and mutant analysis to dissect embryo-scale cell interactions during early gastrulation. Lateral cells have a denser medial-apical actomyosin network and shift ventrally as a compact cohort, whereas dorsal cells become stretched. We show that the behaviour of these cells affects furrow internalization. A computational model predicts different mechanical properties associated with tissue behaviour: lateral cells are stiff, whereas dorsal cells are soft. Experimental analysis confirms these properties in vivo.},
-  eprint = {26497898},
-  eprinttype = {pmid},
-  keywords = {Animals,Cell Movement,Drosophila,Embryo; Nonmammalian,Female,Gastrula,Gastrulation,Male},
   langid = {english},
-  pmcid = {PMC4846315}
+  pmcid = {PMC4846315},
+  keywords = {Animals,Cell Movement,Drosophila,Embryo; Nonmammalian,Female,Gastrula,Gastrulation,Male}
 }
 
 @article{rauziPhysicalModelsMesoderm2013,
@@ -3325,16 +3325,16 @@ @article{rauziPhysicalModelsMesoderm2013
   date = {2013-07-02},
   journaltitle = {Biophysical Journal},
   volume = {105},
+  number = {1},
+  eprint = {23823218},
+  eprinttype = {pmid},
   pages = {3--10},
   issn = {0006-3495},
   doi = {10.1016/j.bpj.2013.05.039},
   url = {http://www.cell.com/biophysj/abstract/S0006-3495(13)00626-7},
   urldate = {2017-07-25},
-  eprint = {23823218},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/9SQA4CXG/Rauzi et al. - 2013 - Physical Models of Mesoderm Invagination in Drosop.pdf;/home/guillaume/Zotero/storage/TXCSBJXN/S0006-3495(13)00626-7.html},
   langid = {english},
-  number = {1}
+  file = {/home/guillaume/Zotero/storage/9SQA4CXG/Rauzi et al. - 2013 - Physical Models of Mesoderm Invagination in Drosop.pdf;/home/guillaume/Zotero/storage/TXCSBJXN/S0006-3495(13)00626-7.html}
 }
 
 @article{rey-suarez_role_2009,
@@ -3343,8 +3343,8 @@ @article{rey-suarez_role_2009
   date = {2009},
   journaltitle = {Biophysical Journal},
   volume = {96},
-  pages = {459a},
-  number = {3}
+  number = {3},
+  pages = {459a}
 }
 
 @article{riosImagingOrganoidsBright2018,
@@ -3354,13 +3354,13 @@ @article{riosImagingOrganoidsBright2018
   date = {2018-01-03},
   journaltitle = {Nature Methods},
   volume = {15},
+  number = {1},
   pages = {24--26},
   issn = {1548-7091, 1548-7105},
   doi = {10.1038/nmeth.4537},
   url = {http://www.nature.com/doifinder/10.1038/nmeth.4537},
   urldate = {2018-10-01},
-  file = {/home/guillaume/Zotero/storage/AGCSMDJZ/rios2018.pdf},
-  number = {1}
+  file = {/home/guillaume/Zotero/storage/AGCSMDJZ/rios2018.pdf}
 }
 
 @article{roca-cusachsQuantifyingForcesCell2017,
@@ -3374,9 +3374,9 @@ @article{roca-cusachsQuantifyingForcesCell2017
   url = {https://www.nature.com/ncb/journal/vaop/ncurrent/full/ncb3564.html},
   urldate = {2017-06-21},
   abstract = {Cells exert, sense, and respond to physical forces through an astounding diversity of mechanisms. Here we review recently developed tools to quantify the forces generated by cells. We first review technologies based on sensors of known or assumed mechanical properties, and discuss their applicability and limitations. We then proceed to draw an analogy between these human-made sensors and force sensing in the cell. As mechanics is increasingly revealed to play a fundamental role in cell function we envisage that tools to quantify physical forces may soon become widely applied in life-sciences laboratories.},
-  file = {/home/guillaume/Zotero/storage/A78E9DV6/ncb3564.html},
+  langid = {english},
   keywords = {Biological techniques,Mechanotransduction,microscopy},
-  langid = {english}
+  file = {/home/guillaume/Zotero/storage/A78E9DV6/ncb3564.html}
 }
 
 @article{roper_anisotropy_2012,
@@ -3385,13 +3385,13 @@ @article{roper_anisotropy_2012
   date = {2012},
   journaltitle = {Developmental Cell},
   volume = {23},
+  number = {5},
+  eprint = {23153493},
+  eprinttype = {pmid},
   pages = {939--953},
   issn = {15345807},
   doi = {10.1016/j.devcel.2012.09.013},
-  abstract = {The formation of tubular structures from epithelial sheets is a key process of organ formation in all animals, but the cytoskeletal rearrangements that cause the cell shape changes that drive tubulogenesis are not well understood. Using live imaging and super-resolution microscopy to analyze the tubulogenesis of the Drosophila salivary glands, I find that an anisotropic plasma membrane distribution of the protein Crumbs, mediated by its large extracellular domain, determines the subcellular localization of a supracellular actomyosin cable in the cells at the placode border, with myosin II accumulating at edges where Crumbs is lowest. Laser ablation shows that the cable is under increased tension, implying an active involvement in the invagination process. Crumbs anisotropy leads to anisotropic distribution of aPKC, which in turn can negatively regulate Rok, thus preventing the formation of a cable where Crumbs and aPKC are localized. Formation of tubular structures from epithelial sheets is a key process of organ formation in all animals. Röper shows that anisotropic localization of Crumbs, mediated by homophilic interactions of its extracellular domains, positions a myosin cable that is part of the machinery driving tube formation in the Drosophila embryo. © 2012 Elsevier Inc.},
-  eprint = {23153493},
-  eprinttype = {pmid},
-  number = {5}
+  abstract = {The formation of tubular structures from epithelial sheets is a key process of organ formation in all animals, but the cytoskeletal rearrangements that cause the cell shape changes that drive tubulogenesis are not well understood. Using live imaging and super-resolution microscopy to analyze the tubulogenesis of the Drosophila salivary glands, I find that an anisotropic plasma membrane distribution of the protein Crumbs, mediated by its large extracellular domain, determines the subcellular localization of a supracellular actomyosin cable in the cells at the placode border, with myosin II accumulating at edges where Crumbs is lowest. Laser ablation shows that the cable is under increased tension, implying an active involvement in the invagination process. Crumbs anisotropy leads to anisotropic distribution of aPKC, which in turn can negatively regulate Rok, thus preventing the formation of a cable where Crumbs and aPKC are localized. Formation of tubular structures from epithelial sheets is a key process of organ formation in all animals. Röper shows that anisotropic localization of Crumbs, mediated by homophilic interactions of its extracellular domains, positions a myosin cable that is part of the machinery driving tube formation in the Drosophila embryo. © 2012 Elsevier Inc.}
 }
 
 @article{runserBiomechanicalBasisBiased2020,
@@ -3405,8 +3405,8 @@ @article{runserBiomechanicalBasisBiased2020
   url = {https://www.biorxiv.org/content/10.1101/2020.06.22.166231v1},
   urldate = {2020-06-23},
   abstract = {{$<$}p{$>$}During lung development, epithelial branches expand preferentially in longitudinal direction. This bias in outgrowth has been linked to a bias in cell shape and in the cell division plane. How such bias arises is unknown. Here, we show that biased epithelial outgrowth occurs independent of the surrounding mesenchyme. Biased outgrowth is also not the consequence of a growth factor gradient, as biased outgrowth is obtained with uniform growth factor cultures, and in the presence of the FGFR inhibitor SU5402. Furthermore, we note that epithelial tubes are largely closed during early lung and kidney development. By simulating the reported fluid flow inside segmented narrow epithelial tubes, we show that the shear stress levels on the apical surface are sufficient to explain the reported bias in cell shape and outgrowth. We use a cell-based vertex model to confirm that apical shear forces, unlike constricting forces, can give rise to both the observed bias in cell shapes and tube elongation. We conclude that shear stress may be a more general driver of biased tube elongation beyond its established role in angiogenesis.{$<$}/p{$>$}},
-  file = {/home/guillaume/Zotero/storage/LGFEQ2Q4/Runser et al. - 2020 - The Biomechanical Basis of Biased Epithelial Tube .pdf;/home/guillaume/Zotero/storage/YKWIRKSU/2020.06.22.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/LGFEQ2Q4/Runser et al. - 2020 - The Biomechanical Basis of Biased Epithelial Tube .pdf;/home/guillaume/Zotero/storage/YKWIRKSU/2020.06.22.html}
 }
 
 @article{sakoImagingSingleMolecules2006,
@@ -3428,15 +3428,15 @@ @article{savillModellingMorphogenesisSingle1997
   date = {1997-02-07},
   journaltitle = {Journal of Theoretical Biology},
   volume = {184},
+  number = {3},
   pages = {229--235},
   issn = {0022-5193},
   doi = {10.1006/jtbi.1996.0237},
   url = {https://www.sciencedirect.com/science/article/pii/S0022519396902374},
   urldate = {2021-02-08},
   abstract = {We present a three-dimensional hybrid cellular automata (CA)/partial differential equation (PDE) model that allows for the study of morphogenesis in simple cellular systems. We apply the model to the cellular slime moldDictyostelium discoideum“from single cells to crawling slug”. Using simple local interactions we can achieve the basic morphogenesis with only three processes: production of and chemotaxis to cAMP and cellular adhesion. The interplay of these processes causes the amoebae to spatially self-organize leading to the complex behaviour of stream and mound formation, cell sorting and slug migration all without any change of parameters during the complete morphogenetic process.},
-  file = {/home/guillaume/Zotero/storage/W2A9XH6X/Savill et Hogeweg - 1997 - Modelling Morphogenesis From Single Cells to Craw.pdf;/home/guillaume/Zotero/storage/Y2TF8HFQ/S0022519396902374.html},
   langid = {english},
-  number = {3}
+  file = {/home/guillaume/Zotero/storage/W2A9XH6X/Savill et Hogeweg - 1997 - Modelling Morphogenesis From Single Cells to Craw.pdf;/home/guillaume/Zotero/storage/Y2TF8HFQ/S0022519396902374.html}
 }
 
 @article{sawTopologicalDefectsEpithelia2017,
@@ -3445,31 +3445,31 @@ @article{sawTopologicalDefectsEpithelia2017
   date = {2017-04-13},
   journaltitle = {Nature},
   volume = {544},
+  number = {7649},
   pages = {212--216},
   issn = {0028-0836},
   doi = {10.1038/nature21718},
   url = {https://www.nature.com/nature/journal/v544/n7649/full/nature21718.html#affil-auth},
   urldate = {2017-04-29},
   abstract = {Epithelial tissues (epithelia) remove excess cells through extrusion, preventing the accumulation of unnecessary or pathological cells. The extrusion process can be triggered by apoptotic signalling, oncogenic transformation and overcrowding of cells. Despite the important linkage of cell extrusion to developmental, homeostatic and pathological processes such as cancer metastasis, its underlying mechanism and connections to the intrinsic mechanics of the epithelium are largely unexplored. We approach this problem by modelling the epithelium as an active nematic liquid crystal (that has a long range directional order), and comparing numerical simulations to strain rate and stress measurements within monolayers of MDCK (Madin Darby canine kidney) cells. Here we show that apoptotic cell extrusion is provoked by singularities in cell alignments in the form of comet-shaped topological defects. We find a universal correlation between extrusion sites and positions of nematic defects in the cell orientation field in different epithelium types. The results confirm the active nematic nature of epithelia, and demonstrate that defect-induced isotropic stresses are the primary precursors of mechanotransductive responses in cells, including YAP (Yes-associated protein) transcription factor activity, caspase-3-mediated cell death, and extrusions. Importantly, the defect-driven extrusion mechanism depends on intercellular junctions, because the weakening of cell–cell interactions in an α-catenin knockdown monolayer reduces the defect size and increases both the number of defects and extrusion rates, as is also predicted by our model. We further demonstrate the ability to control extrusion hotspots by geometrically inducing defects through microcontact printing of patterned monolayers. On the basis of these results, we propose a mechanism for apoptotic cell extrusion: spontaneously formed topological defects in epithelia govern cell fate. This will be important in predicting extrusion hotspots and dynamics in vivo, with potential applications to tissue regeneration and the suppression of metastasis. Moreover, we anticipate that the analogy between the epithelium and active nematic liquid crystals will trigger further investigations of the link between cellular processes and the material properties of epithelia.},
-  file = {/home/guillaume/Zotero/storage/NMHQ3JED/nature21718.html},
-  keywords = {Biophysics,Materials science},
   langid = {english},
-  number = {7649}
+  keywords = {Biophysics,Materials science},
+  file = {/home/guillaume/Zotero/storage/NMHQ3JED/nature21718.html}
 }
 
 @online{schmidtCellDetectionStarconvex2018,
   title = {Cell {{Detection}} with {{Star}}-Convex {{Polygons}}},
   author = {Schmidt, Uwe and Weigert, Martin and Broaddus, Coleman and Myers, Gene},
   date = {2018-06-09},
+  eprint = {1806.03535},
+  eprinttype = {arxiv},
+  primaryclass = {cs},
   url = {http://arxiv.org/abs/1806.03535},
   urldate = {2018-06-13},
   abstract = {Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications. Recent successful learning-based approaches include per-pixel cell segmentation with subsequent pixel grouping, or localization of bounding boxes with subsequent shape refinement. In situations of crowded cells, these can be prone to segmentation errors, such as falsely merging bordering cells or suppressing valid cell instances due to the poor approximation with bounding boxes. To overcome these issues, we propose to localize cell nuclei via star-convex polygons, which are a much better shape representation as compared to bounding boxes and thus do not need shape refinement. To that end, we train a convolutional neural network that predicts for every pixel a polygon for the cell instance at that position. We demonstrate the merits of our approach on two synthetic datasets and one challenging dataset of diverse fluorescence microscopy images.},
   archiveprefix = {arXiv},
-  eprint = {1806.03535},
-  eprinttype = {arxiv},
-  file = {/home/guillaume/Zotero/storage/YF2MP9WF/Schmidt et al. - 2018 - Cell Detection with Star-convex Polygons.pdf;/home/guillaume/Zotero/storage/R8Y6RZR3/1806.html},
   keywords = {Computer Science - Computer Vision and Pattern Recognition},
-  primaryclass = {cs}
+  file = {/home/guillaume/Zotero/storage/YF2MP9WF/Schmidt et al. - 2018 - Cell Detection with Star-convex Polygons.pdf;/home/guillaume/Zotero/storage/R8Y6RZR3/1806.html}
 }
 
 @incollection{schorbResolvingProcessClathrin2020,
@@ -3478,6 +3478,7 @@ @incollection{schorbResolvingProcessClathrin2020
   author = {Schorb, Martin and Paul-Gilloteaux, Perrine},
   editor = {Miura, Kota and Sladoje, Nataša},
   date = {2020},
+  series = {Learning {{Materials}} in {{Biosciences}}},
   pages = {143--166},
   publisher = {{Springer International Publishing}},
   location = {{Cham}},
@@ -3486,8 +3487,7 @@ @incollection{schorbResolvingProcessClathrin2020
   urldate = {2019-10-24},
   abstract = {This chapter will present the computational approach of registering images from different modalities based on manual selection of matching pairs of landmarks. Here we will present an image registration workflow based on MATLAB’s image processing toolbox using the identification of sites of clathrin-mediated endocytosis by correlative light electron microscopy (CLEM) as an example. In the Appendix section, we will discuss the concept of image transformations and how to generate them based on pairs of landmarks. We will also learn how to fit a 2D Gaussian for a more accurate positioning of the landmarks.},
   isbn = {978-3-030-22386-1},
-  langid = {english},
-  series = {Learning {{Materials}} in {{Biosciences}}}
+  langid = {english}
 }
 
 @article{shinObjectiveMeasurementVisual2006,
@@ -3496,14 +3496,14 @@ @article{shinObjectiveMeasurementVisual2006
   date = {2006-02-01},
   journaltitle = {American Journal of Ophthalmology},
   volume = {141},
+  number = {2},
   pages = {327--332},
   issn = {0002-9394},
   doi = {10.1016/j.ajo.2005.09.025},
   url = {http://www.sciencedirect.com/science/article/pii/S0002939405010330},
   urldate = {2019-03-13},
   abstract = {Purpose To investigate the efficacy of optokinetic nystagmus (OKN) suppression and induction methods for the objective estimation of visual acuities in patients with various ocular diseases. Design Prospective, descriptive study. Methods One hundred seventy-three eyes of 89 patients aged between 27 and 75 years who registered at our institution from January to December 2004 were prospectively enrolled onto this study. Ocular diseases included generalized retinal diseases (47 eyes), media opacity (32 eyes), refractive errors (31 eyes), glaucoma (27 eyes), maculopathies (26 eyes), and optic neuropathies (10 eyes). Horizontal optokinetic stimuli were presented on a 17-inch monitor screen at a distance of 40 cm from the subject in a dark room. Horizontal eye movements were recorded in each eye separately by infrared oculography. Objective visual acuities measured by using OKN suppression or induction methods were compared with subjective visual acuity assessments. The logarithm of minimal angle of resolution visual acuity was 1.03, and ranged from −0.08 to hand movement. Results Linear regression identified minimum stripe stimuli required to induce OKN by using the OKN induction method, and the minimum dot size required to suppress OKN was found to be correlated with subjective visual acuity (P {$<$} .01). The induction method was useful in patients with visual acuities of 20/60 or worse, and the suppression method was useful in patients with visual acuities of 20/200 or better. Conclusions Combined use of the OKN induction and suppression methods provides a satisfactory means of determining objective visual acuity.},
-  file = {/home/guillaume/Zotero/storage/F24REGFB/S0002939405010330.html},
-  number = {2}
+  file = {/home/guillaume/Zotero/storage/F24REGFB/S0002939405010330.html}
 }
 
 @article{sidhayeCollectiveEpithelialMigration2016,
@@ -3516,8 +3516,8 @@ @article{sidhayeCollectiveEpithelialMigration2016
   url = {http://biorxiv.org/content/early/2016/10/25/082958},
   urldate = {2016-11-26},
   abstract = {Organ formation is a complex, multi-scale event involving changes at the intracellular, cellular and tissue level. A key step in organogenesis is the formation of characteristically shaped organ precursors. However, the cellular mechanisms driving organ precursor formation are not well understood. Here, we investigate the epithelial rearrangements responsible for the development of the hemispherical retinal neuroepithelium (RNE), a part of the optic cup. We show that, surprisingly, active collective epithelial migration of cells at the rim of the cup is the most prominent player in RNE formation. This rim involution is driven by progressive cell-matrix contacts and actively translocates prospective RNE cells to their correct location before they adopt neuroepithelial fate. Failure of rim migration during neuroepithelium formation leads to ectopic determination of RNE cells and consequently impairs eye formation. Overall, this study illustrates how spatiotemporal coordination between morphogenic movements and fate determination critically influences organogenesis.},
-  file = {/home/guillaume/Zotero/storage/8MZE4XQB/Sidhaye et al. - 2016 - Collective epithelial migration drives timely morp.pdf;/home/guillaume/Zotero/storage/45R2FZSZ/082958.html;/home/guillaume/Zotero/storage/F7XNR9M6/082958.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/8MZE4XQB/Sidhaye et al. - 2016 - Collective epithelial migration drives timely morp.pdf;/home/guillaume/Zotero/storage/45R2FZSZ/082958.html;/home/guillaume/Zotero/storage/F7XNR9M6/082958.html}
 }
 
 @article{sluka_cell_2014,
@@ -3526,16 +3526,16 @@ @article{sluka_cell_2014
   date = {2014-08},
   journaltitle = {Bioinformatics (Oxford, England)},
   volume = {30},
+  number = {16},
+  eprint = {24755304},
+  eprinttype = {pmid},
   pages = {2367--74},
   issn = {1367-4811},
   doi = {10.1093/bioinformatics/btu210},
   url = {http://bioinformatics.oxfordjournals.org/content/early/2014/05/18/bioinformatics.btu210.long},
   abstract = {MOTIVATION: Currently, there are no ontologies capable of describing both the spatial organization of groups of cells and the behaviors of those cells. The lack of a formalized method for describing the spatiality and intrinsic biological behaviors of cells makes it difficult to adequately describe cells, tissues and organs as spatial objects in living tissues, in vitro assays and in computational models of tissues. RESULTS: We have developed an OWL-2 ontology to describe the intrinsic physical and biological characteristics of cells and tissues. The Cell Behavior Ontology (CBO) provides a basis for describing the spatial and observable behaviors of cells and extracellular components suitable for describing in vivo, in vitro and in silico multicell systems. Using the CBO, a modeler can create a meta-model of a simulation of a biological model and link that meta-model to experiment or simulation results. Annotation of a multicell model and its computational representation, using the CBO, makes the statement of the underlying biology explicit. The formal representation of such biological abstraction facilitates the validation, falsification, discovery, sharing and reuse of both models and experimental data. AVAILABILITY AND IMPLEMENTATION: The CBO, developed using Protégé 4, is available at http://cbo.biocomplexity.indiana.edu/cbo/ and at BioPortal (http://bioportal.bioontology.org/ontologies/CBO).},
-  eprint = {24755304},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/DTTTJG9H/Sluka et al. - 2014 - The cell behavior ontology describing the intrinsic biological behaviors of real and model cells seen as active ag.pdf},
   keywords = {Biological,Biological Ontologies,Cell Physiological Processes,Computer Simulation,Models},
-  number = {16}
+  file = {/home/guillaume/Zotero/storage/DTTTJG9H/Sluka et al. - 2014 - The cell behavior ontology describing the intrinsic biological behaviors of real and model cells seen as active ag.pdf}
 }
 
 @article{spahnVertexModelDrosophila2013,
@@ -3544,15 +3544,15 @@ @article{spahnVertexModelDrosophila2013
   date = {2013-09-16},
   journaltitle = {PLOS ONE},
   volume = {8},
+  number = {9},
   pages = {e75051},
   issn = {1932-6203},
   doi = {10.1371/journal.pone.0075051},
   url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0075051},
   urldate = {2017-03-04},
   abstract = {Ventral furrow formation in Drosophila is an outstanding model system to study the mechanisms involved in large-scale tissue rearrangements. Ventral cells accumulate myosin at their apical sides and, while being tightly coupled to each other via apical adherens junctions, execute actomyosin contractions that lead to reduction of their apical cell surface. Thereby, a band of constricted cells along the ventral epithelium emerges which will form a tissue indentation along the ventral midline (the ventral furrow). Here we adopt a 2D vertex model to simulate ventral furrow formation in a surface view allowing easy comparison with confocal live-recordings. We show that in order to reproduce furrow morphology seen in vivo, a gradient of contractility must be assumed in the ventral epithelium which renders cells more contractile the closer they lie to the ventral midline. The model predicts previous experimental findings, such as the gain of eccentric morphology of constricting cells and an incremental fashion of apical cell area reduction. Analysis of the model suggests that this incremental area reduction is caused by the dynamical interplay of cell elasticity and stochastic contractility as well as by the opposing forces from contracting neighbour cells. We underpin results from the model through in vivo analysis of ventral furrow formation in wildtype and twi mutant embryos. Our results show that ventral furrow formation can be accomplished as a “tug-of-war” between stochastically contracting, mechanically coupled cells and may require less rigorous regulation than previously thought.  Summary For the developmental biologist it is a fascinating question how cells can coordinate major tissue movements during embryonic development. The so-called ventral furrow of the Drosophila embryo is a well-studied example of such a process when cells from a ventral band, spanning nearly the entire length of the embryo, undergo dramatic shape change by contracting their tips and then fold inwards into the interior of the embryo. Although numerous genes have been identified that are critical for ventral furrow formation, it is an open question how cells work together to elicit this tissue rearrangement. We use a computational model to mimic the physical properties of cells in the ventral epithelium and simulate the formation of the furrow. We find that the ventral furrow can form through stochastic self-organisation and that previous experimental observations can be readily explained in our model by considering forces that arise when cells execute contractions while being coupled to each other in a mechanically coherent epithelium. The model highlights the importance of a physical perspective when studying tissue morphogenesis and shows that only a minimal genetic regulation may be required to drive complex processes in embryonic development.},
-  file = {/home/guillaume/Zotero/storage/J27VWIUD/Spahn et Reuter - 2013 - A Vertex Model of Drosophila Ventral Furrow Format.pdf;/home/guillaume/Zotero/storage/IWRT7893/article.html},
   keywords = {Anisotropy,Cell membranes,Drosophila melanogaster,Embryos,Epithelium,Myosins,Phenotypes,Simulation and modeling},
-  number = {9}
+  file = {/home/guillaume/Zotero/storage/J27VWIUD/Spahn et Reuter - 2013 - A Vertex Model of Drosophila Ventral Furrow Format.pdf;/home/guillaume/Zotero/storage/IWRT7893/article.html}
 }
 
 @article{spahnVertexModelDrosophila2013a,
@@ -3561,16 +3561,16 @@ @article{spahnVertexModelDrosophila2013a
   date = {2013-09-16},
   journaltitle = {PLOS ONE},
   volume = {8},
+  number = {9},
   pages = {e75051},
   issn = {1932-6203},
   doi = {10.1371/journal.pone.0075051},
   url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0075051},
   urldate = {2018-03-12},
   abstract = {Ventral furrow formation in Drosophila is an outstanding model system to study the mechanisms involved in large-scale tissue rearrangements. Ventral cells accumulate myosin at their apical sides and, while being tightly coupled to each other via apical adherens junctions, execute actomyosin contractions that lead to reduction of their apical cell surface. Thereby, a band of constricted cells along the ventral epithelium emerges which will form a tissue indentation along the ventral midline (the ventral furrow). Here we adopt a 2D vertex model to simulate ventral furrow formation in a surface view allowing easy comparison with confocal live-recordings. We show that in order to reproduce furrow morphology seen in vivo, a gradient of contractility must be assumed in the ventral epithelium which renders cells more contractile the closer they lie to the ventral midline. The model predicts previous experimental findings, such as the gain of eccentric morphology of constricting cells and an incremental fashion of apical cell area reduction. Analysis of the model suggests that this incremental area reduction is caused by the dynamical interplay of cell elasticity and stochastic contractility as well as by the opposing forces from contracting neighbour cells. We underpin results from the model through in vivo analysis of ventral furrow formation in wildtype and twi mutant embryos. Our results show that ventral furrow formation can be accomplished as a “tug-of-war” between stochastically contracting, mechanically coupled cells and may require less rigorous regulation than previously thought. Summary For the developmental biologist it is a fascinating question how cells can coordinate major tissue movements during embryonic development. The so-called ventral furrow of the Drosophila embryo is a well-studied example of such a process when cells from a ventral band, spanning nearly the entire length of the embryo, undergo dramatic shape change by contracting their tips and then fold inwards into the interior of the embryo. Although numerous genes have been identified that are critical for ventral furrow formation, it is an open question how cells work together to elicit this tissue rearrangement. We use a computational model to mimic the physical properties of cells in the ventral epithelium and simulate the formation of the furrow. We find that the ventral furrow can form through stochastic self-organisation and that previous experimental observations can be readily explained in our model by considering forces that arise when cells execute contractions while being coupled to each other in a mechanically coherent epithelium. The model highlights the importance of a physical perspective when studying tissue morphogenesis and shows that only a minimal genetic regulation may be required to drive complex processes in embryonic development.},
-  file = {/home/guillaume/Zotero/storage/GHZZR3BZ/Spahn et Reuter - 2013 - A Vertex Model of Drosophila Ventral Furrow Format.pdf;/home/guillaume/Zotero/storage/QAV7QSIK/article.html},
-  keywords = {Anisotropy,Cell membranes,Drosophila melanogaster,Embryos,Epithelium,Myosins,Phenotypes,Simulation and modeling},
   langid = {english},
-  number = {9}
+  keywords = {Anisotropy,Cell membranes,Drosophila melanogaster,Embryos,Epithelium,Myosins,Phenotypes,Simulation and modeling},
+  file = {/home/guillaume/Zotero/storage/GHZZR3BZ/Spahn et Reuter - 2013 - A Vertex Model of Drosophila Ventral Furrow Format.pdf;/home/guillaume/Zotero/storage/QAV7QSIK/article.html}
 }
 
 @article{starrussMorpheusUserfriendlyModeling2014,
@@ -3580,17 +3580,17 @@ @article{starrussMorpheusUserfriendlyModeling2014
   date = {2014-01-05},
   journaltitle = {Bioinformatics},
   volume = {30},
+  number = {9},
+  eprint = {24443380},
+  eprinttype = {pmid},
   pages = {1331--1332},
   issn = {1367-4803, 1460-2059},
   doi = {10.1093/bioinformatics/btt772},
   url = {http://bioinformatics.oxfordjournals.org/content/30/9/1331},
   urldate = {2016-11-03},
   abstract = {Summary: Morpheus is a modeling environment for the simulation and integration of cell-based models with ordinary differential equations and reaction-diffusion systems. It allows rapid development of multiscale models in biological terms and mathematical expressions rather than programming code. Its graphical user interface supports the entire workflow from model construction and simulation to visualization, archiving and batch processing. Availability and implementation: Binary packages are available at http://imc.zih.tu-dresden.de/wiki/morpheus for Linux, Mac OSX and MS Windows. Contact: walter.deback@tu-dresden.de Supplementary information: Supplementary data are available at Bioinformatics online.},
-  eprint = {24443380},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/C3PIU5CI/Starruß et al. - 2014 - Morpheus a user-friendly modeling environment for.pdf;/home/guillaume/Zotero/storage/PITBNFHG/1331.html},
   langid = {english},
-  number = {9}
+  file = {/home/guillaume/Zotero/storage/C3PIU5CI/Starruß et al. - 2014 - Morpheus a user-friendly modeling environment for.pdf;/home/guillaume/Zotero/storage/PITBNFHG/1331.html}
 }
 
 @article{stegmaier_real-time_2016,
@@ -3599,12 +3599,12 @@ @article{stegmaier_real-time_2016
   date = {2016},
   journaltitle = {Developmental cell},
   volume = {36},
-  issn = {1878-1551},
-  doi = {10.1016/j.devcel.2015.12.028},
-  abstract = {We present the Real-time Accurate Cell-shape Extractor (RACE), a high-throughput image analysis framework for automated three-dimensional cell segmentation in large-scale images. RACE is 55-330 times faster and 2-5 times more accurate than state-of-the-art methods. We demonstrate the generality of RACE by extracting cell-shape information from entire Drosophila, zebrafish, and mouse embryos imaged with confocal and light-sheet microscopes. Using RACE, we automatically reconstructed cellular-resolution tissue anisotropy maps across developing Drosophila embryos and quantified differences in cell-shape dynamics in wild-type and mutant embryos. We furthermore integrated RACE with our framework for automated cell lineaging and performed joint segmentation and cell tracking in entire Drosophila embryos. RACE processed these terabyte-sized datasets on a single computer within 1.4 days. RACE is easy to use, as it requires adjustment of only three parameters, takes full advantage of state-of-the-art multi-core processors and graphics cards, and is available as open-source software for Windows, Linux, and Mac OS.},
+  number = {2},
   eprint = {26812020},
   eprinttype = {pmid},
-  number = {2}
+  issn = {1878-1551},
+  doi = {10.1016/j.devcel.2015.12.028},
+  abstract = {We present the Real-time Accurate Cell-shape Extractor (RACE), a high-throughput image analysis framework for automated three-dimensional cell segmentation in large-scale images. RACE is 55-330 times faster and 2-5 times more accurate than state-of-the-art methods. We demonstrate the generality of RACE by extracting cell-shape information from entire Drosophila, zebrafish, and mouse embryos imaged with confocal and light-sheet microscopes. Using RACE, we automatically reconstructed cellular-resolution tissue anisotropy maps across developing Drosophila embryos and quantified differences in cell-shape dynamics in wild-type and mutant embryos. We furthermore integrated RACE with our framework for automated cell lineaging and performed joint segmentation and cell tracking in entire Drosophila embryos. RACE processed these terabyte-sized datasets on a single computer within 1.4 days. RACE is easy to use, as it requires adjustment of only three parameters, takes full advantage of state-of-the-art multi-core processors and graphics cards, and is available as open-source software for Windows, Linux, and Mac OS.}
 }
 
 @article{storgelQuantitativeMorphologyEpithelial2016,
@@ -3613,16 +3613,16 @@ @article{storgelQuantitativeMorphologyEpithelial2016
   date = {2016-01-05},
   journaltitle = {Biophys. J.},
   volume = {110},
+  number = {1},
+  eprint = {26745429},
+  eprinttype = {pmid},
   pages = {269--277},
   issn = {1542-0086},
   doi = {10.1016/j.bpj.2015.11.024},
   abstract = {The shape of spatially modulated epithelial morphologies such as villi and crypts is usually associated with the epithelium-stroma area mismatch leading to buckling. We propose an alternative mechanical model based on intraepithelial stresses generated by differential tensions of apical, lateral, and basal sides of cells as well as on the elasticity of the basement membrane. We use it to theoretically study longitudinal folds in simple epithelia and we identify four types of corrugated morphologies: compact, invaginated, evaginated, and wavy. The obtained tissue contours and thickness profiles are compared to epithelial folds observed in invertebrates and vertebrates, and for most samples, the agreement is within the estimated experimental error. Our model establishes the groove-crest modulation of tissue thickness as a morphometric parameter that can, together with the curvature profile, be used to estimate the relative differential apicobasal tension in the epithelium.},
-  eprint = {26745429},
-  eprinttype = {pmid},
-  keywords = {Basement Membrane,Biomechanical Phenomena,Collagen,Connective Tissue,Epithelium,Extracellular Matrix,Mechanical Processes,Models; Biological,Stress; Mechanical},
   langid = {english},
-  number = {1},
-  pmcid = {PMC4825108}
+  pmcid = {PMC4825108},
+  keywords = {Basement Membrane,Biomechanical Phenomena,Collagen,Connective Tissue,Epithelium,Extracellular Matrix,Mechanical Processes,Models; Biological,Stress; Mechanical}
 }
 
 @article{suarez_dynamics_2010,
@@ -3631,8 +3631,8 @@ @article{suarez_dynamics_2010
   date = {2010},
   journaltitle = {Biophysical Journal},
   volume = {98},
-  pages = {491a},
-  number = {3}
+  number = {3},
+  pages = {491a}
 }
 
 @article{suarez_dynamics_2011,
@@ -3641,8 +3641,8 @@ @article{suarez_dynamics_2011
   date = {2011},
   journaltitle = {Biophysical Journal},
   volume = {100},
-  pages = {365a},
-  number = {3}
+  number = {3},
+  pages = {365a}
 }
 
 @article{suarez_slow_2013,
@@ -3651,8 +3651,8 @@ @article{suarez_slow_2013
   date = {2013},
   journaltitle = {PloS one},
   volume = {8},
-  pages = {e68309},
-  number = {7}
+  number = {7},
+  pages = {e68309}
 }
 
 @article{sugimuraMeasuringForcesStresses2016,
@@ -3661,17 +3661,17 @@ @article{sugimuraMeasuringForcesStresses2016
   date = {2016-01-15},
   journaltitle = {Development},
   volume = {143},
+  number = {2},
+  eprint = {26786209},
+  eprinttype = {pmid},
   pages = {186--196},
   issn = {0950-1991, 1477-9129},
   doi = {10.1242/dev.119776},
   url = {https://dev.biologists.org/content/143/2/186},
   urldate = {2020-01-15},
   abstract = {Skip to Next Section Development, homeostasis and regeneration of tissues result from a complex combination of genetics and mechanics, and progresses in the former have been quicker than in the latter. Measurements of in situ forces and stresses appear to be increasingly important to delineate the role of mechanics in development. We review here several emerging techniques: contact manipulation, manipulation using light, visual sensors, and non-mechanical observation techniques. We compare their fields of applications, their advantages and limitations, and their validations. These techniques complement measurements of deformations and of mechanical properties. We argue that such approaches could have a significant impact on our understanding of the development of living tissues in the near future.},
-  eprint = {26786209},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/279CSWT2/Sugimura et al. - 2016 - Measuring forces and stresses in situ in living ti.pdf;/home/guillaume/Zotero/storage/WGUDPCAN/186.html},
   langid = {english},
-  number = {2}
+  file = {/home/guillaume/Zotero/storage/279CSWT2/Sugimura et al. - 2016 - Measuring forces and stresses in situ in living ti.pdf;/home/guillaume/Zotero/storage/WGUDPCAN/186.html}
 }
 
 @article{sussmanSoftSharpInterfaces2018,
@@ -3680,17 +3680,17 @@ @article{sussmanSoftSharpInterfaces2018
   date = {2018-01-29},
   journaltitle = {Phys. Rev. Lett.},
   volume = {120},
+  number = {5},
+  eprint = {1710.00708},
+  eprinttype = {arxiv},
   pages = {058001},
   doi = {10.1103/PhysRevLett.120.058001},
   url = {http://arxiv.org/abs/1710.00708},
   urldate = {2018-02-15},
   abstract = {How can dense biological tissue maintain sharp boundaries between coexisting cell populations? We explore this question within a simple vertex model for cells, focusing on the role of topology and tissue surface tension. We show that the ability of cells to independently regulate adhesivity and tension, together with neighbor-based interaction rules, lets them support strikingly unusual interfaces. In particular, we show that mechanical- and fluctuation-based measurements of the effective surface tension of a cellular aggregate yield different results, leading to mechanically soft interfaces that are nevertheless extremely sharp.},
   archiveprefix = {arXiv},
-  eprint = {1710.00708},
-  eprinttype = {arxiv},
-  file = {/home/guillaume/Zotero/storage/D89HIQID/Sussman et al. - 2018 - Soft yet sharp interfaces in a vertex model of con.pdf;/home/guillaume/Zotero/storage/AG4EGR2Y/1710.html;/home/guillaume/Zotero/storage/FH3FYVZ7/PhysRevLett.120.html},
   keywords = {Condensed Matter - Soft Condensed Matter,Physics - Biological Physics},
-  number = {5}
+  file = {/home/guillaume/Zotero/storage/D89HIQID/Sussman et al. - 2018 - Soft yet sharp interfaces in a vertex model of con.pdf;/home/guillaume/Zotero/storage/AG4EGR2Y/1710.html;/home/guillaume/Zotero/storage/FH3FYVZ7/PhysRevLett.120.html}
 }
 
 @incollection{swatChapter13MultiScale2012,
@@ -3699,15 +3699,15 @@ @incollection{swatChapter13MultiScale2012
   author = {Swat, Maciej H. and Thomas, Gilberto L. and Belmonte, Julio M. and Shirinifard, Abbas and Hmeljak, Dimitrij and Glazier, James A.},
   editor = {Arkin, Anand R. Asthagiri {and} Adam P.},
   date = {2012},
+  series = {Computational {{Methods}} in {{Cell Biology}}},
   volume = {110},
   pages = {325--366},
   publisher = {{Academic Press}},
   url = {http://www.sciencedirect.com/science/article/pii/B9780123884039000138},
   urldate = {2016-11-03},
   abstract = {The study of how cells interact to produce tissue development, homeostasis, or diseases was, until recently, almost purely experimental. Now, multi-cell computer simulation methods, ranging from relatively simple cellular automata to complex immersed-boundary and finite-element mechanistic models, allow in silico study of multi-cell phenomena at the tissue scale based on biologically observed cell behaviors and interactions such as movement, adhesion, growth, death, mitosis, secretion of chemicals, chemotaxis, etc. This tutorial introduces the lattice-based Glazier–Graner–Hogeweg (GGH) Monte Carlo multi-cell modeling and the open-source GGH-based CompuCell3D simulation environment that allows rapid and intuitive modeling and simulation of cellular and multi-cellular behaviors in the context of tissue formation and subsequent dynamics. We also present a walkthrough of four biological models and their associated simulations that demonstrate the capabilities of the GGH and CompuCell3D.},
-  file = {/home/guillaume/Zotero/storage/S255RJBF/B9780123884039000138.html},
   keywords = {Angiogenesis,Interaction,Python,Scripting,Sorting,Vascular},
-  series = {Computational {{Methods}} in {{Cell Biology}}}
+  file = {/home/guillaume/Zotero/storage/S255RJBF/B9780123884039000138.html}
 }
 
 @article{swedlowInformaticsQuantitativeAnalysis2003,
@@ -3716,6 +3716,9 @@ @article{swedlowInformaticsQuantitativeAnalysis2003
   date = {2003-04-04},
   journaltitle = {Science},
   volume = {300},
+  number = {5616},
+  eprint = {12677061},
+  eprinttype = {pmid},
   pages = {100--102},
   publisher = {{American Association for the Advancement of Science}},
   issn = {0036-8075, 1095-9203},
@@ -3723,11 +3726,8 @@ @article{swedlowInformaticsQuantitativeAnalysis2003
   url = {http://science.sciencemag.org/content/300/5616/100},
   urldate = {2020-10-07},
   abstract = {Biological imaging is now a quantitative technique for probing cellular structure and dynamics and is increasingly used for cell-based screens. However, the bioinformatics tools required for hypothesis-driven analysis of digital images are still immature. We are developing the Open Microscopy Environment (OME) as an informatics solution for the storage and analysis of optical microscope image data. OME aims to automate image analysis, modeling, and mining of large sets of images and specifies a flexible data model, a relational database, and an XML-encoded file standard that is usable by potentially any software tool. With this design, OME provides a first step toward biological image informatics.},
-  eprint = {12677061},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/E6S9LIED/Swedlow et al. - 2003 - Informatics and Quantitative Analysis in Biologica.pdf;/home/guillaume/Zotero/storage/LYE4YQ3F/100.html},
   langid = {english},
-  number = {5616}
+  file = {/home/guillaume/Zotero/storage/E6S9LIED/Swedlow et al. - 2003 - Informatics and Quantitative Analysis in Biologica.pdf;/home/guillaume/Zotero/storage/LYE4YQ3F/100.html}
 }
 
 @article{swedlowModellingDataLabs2006,
@@ -3736,12 +3736,12 @@ @article{swedlowModellingDataLabs2006
   date = {2006-11},
   journaltitle = {Nature Cell Biology},
   volume = {8},
+  number = {11},
   pages = {1190--1194},
   issn = {1465-7392, 1476-4679},
   doi = {10.1038/ncb1496},
   url = {http://www.nature.com/doifinder/10.1038/ncb1496},
-  urldate = {2016-11-02},
-  number = {11}
+  urldate = {2016-11-02}
 }
 
 @thesis{tamulonis_cell_2013,
@@ -3754,20 +3754,20 @@ @thesis{tamulonis_cell_2013
 @article{tamulonis_cell-based_2011,
   title = {A Cell-Based Model of {{Nematostella}} Vectensis Gastrulation Including Bottle Cell Formation, Invagination and Zippering},
   author = {Tamulonis, Carlos and Postma, Marten and Marlow, Heather Q. and Magie, Craig R. and de Jong, Johann and Kaandorp, Jaap},
+  options = {useprefix=true},
   date = {2011},
   journaltitle = {Developmental Biology},
   volume = {351},
+  number = {1},
+  eprint = {20977902},
+  eprinttype = {pmid},
   pages = {217--228},
   issn = {00121606},
   doi = {10.1016/j.ydbio.2010.10.017},
   url = {http://dx.doi.org/10.1016/j.ydbio.2010.10.017},
   abstract = {The gastrulation of Nematostella vectensis, the starlet sea anemone, is morphologically simple yet involves many conserved cell behaviors such as apical constriction, invagination, bottle cell formation, cell migration and zippering found during gastrulation in a wide range of more morphologically complex animals.In this article we study Nematostella gastrulation using a combination of morphometrics and computational modeling. Through this analysis we frame gastrulation as a non-trivial problem, in which two distinct cell domains must change shape to match each other geometrically, while maintaining the integrity of the embryo. Using a detailed cell-based model capable of representing arbitrary cell-shapes such as bottle cells, as well as filopodia, localized adhesion and constriction, we are able to simulate gastrulation and associate emergent macroscopic changes in embryo shape to individual cell behaviors. We have developed a number of testable hypotheses based on the model. First, we hypothesize that the blastomeres need to be stiffer at their apical ends, relative to the rest of the cell perimeter, in order to be able to hold their wedge shape and the dimensions of the blastula, regardless of whether the blastula is sealed or leaky. We also postulate that bottle cells are a consequence of cell strain and low cell-cell adhesion, and can be produced within an epithelium even without apical constriction. Finally, we postulate that apical constriction, filopodia and de-epithelialization are necessary and sufficient for gastrulation based on parameter variation studies. ?? 2010 Elsevier Inc.},
-  eprint = {20977902},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/FB95DAH7/Tamulonis et al. - 2011 - A cell-based model of Nematostella vectensis gastrulation including bottle cell formation, invagination and zi.pdf},
   keywords = {Bottle cells,Cell-based model,Invagination,Nematostella vectensis,Zippering},
-  number = {1},
-  options = {useprefix=true}
+  file = {/home/guillaume/Zotero/storage/FB95DAH7/Tamulonis et al. - 2011 - A cell-based model of Nematostella vectensis gastrulation including bottle cell formation, invagination and zi.pdf}
 }
 
 @article{tanaka_lbibcell:_2015,
@@ -3775,11 +3775,11 @@ @article{tanaka_lbibcell:_2015
   author = {Tanaka, S. and Sichau, D. and Iber, D.},
   date = {2015-03},
   journaltitle = {Bioinformatics},
+  eprint = {25770313},
+  eprinttype = {pmid},
   issn = {1367-4803},
   doi = {10.1093/bioinformatics/btv147},
   abstract = {MOTIVATION: The simulation of morphogenetic problems requires the simultaneous and coupled simulation of signalling and tissue dynamics. A cellular resolution of the tissue domain is important to adequately describe the impact of cell-based events, such as cell division, cell-cell interactions and spatially restricted signalling events. A tightly coupled cell-based mechano-regulatory simulation tool is therefore required. RESULTS: We developed an open-source software framework for morphogenetic problems. The environment offers core functionalities for the tissue and signalling models. In addition, the software offers great flexibility to add custom extensions and biologically motivated processes. Cells are represented as highly resolved, massless elastic polygons; the viscous properties of the tissue are modelled by a Newtonian fluid. The Immersed Boundary method is used to model the interaction between the viscous and elastic properties of the cells, thus extending on the IBCell model. The fluid and signalling processes are solved using the Lattice Boltzmann method. As application examples we simulate signalling-dependent tissue dynamics. AVAILABILITY AND IMPLEMENTATION: The documentation and source code are available on http://tanakas.bitbucket.org/lbibcell/index.html CONTACT: simon.tanaka@bsse.ethz.ch or dagmar.iber@bsse.ethz.ch Supplementary information: Supplementary data are available at Bioinformatics online.},
-  eprint = {25770313},
-  eprinttype = {pmid},
   keywords = {modeling software}
 }
 
@@ -3789,17 +3789,17 @@ @article{tanakaSimulationFrameworksMorphogenetic2015
   date = {2015-06},
   journaltitle = {Computation},
   volume = {3},
+  number = {2},
   pages = {197--221},
   publisher = {{Multidisciplinary Digital Publishing Institute}},
   doi = {10.3390/computation3020197},
   url = {https://www.mdpi.com/2079-3197/3/2/197},
   urldate = {2021-01-27},
   abstract = {Morphogenetic modelling and simulation help to understand the processes by which the form and shapes of organs (organogenesis) and organisms (embryogenesis) emerge. This requires two mutually coupled entities: the biomolecular signalling network and the tissue. Whereas the modelling of the signalling has been discussed and used in a multitude of works, the realistic modelling of the tissue has only started on a larger scale in the last decade. Here, common tissue modelling techniques are reviewed. Besides the continuum approach, the principles and main applications of the spheroid, vertex, Cellular Potts, Immersed Boundary and Subcellular Element models are discussed in detail. In recent years, many software frameworks, implementing the aforementioned methods, have been developed. The most widely used frameworks and modelling markup languages and standards are presented.},
-  file = {/home/guillaume/Zotero/storage/NLCIC8MN/Tanaka - 2015 - Simulation Frameworks for Morphogenetic Problems.pdf;/home/guillaume/Zotero/storage/7ELMFQDZ/197.html},
   issue = {2},
-  keywords = {cell-based modelling,computational biology,computational morphogenesis},
   langid = {english},
-  number = {2}
+  keywords = {cell-based modelling,computational biology,computational morphogenesis},
+  file = {/home/guillaume/Zotero/storage/NLCIC8MN/Tanaka - 2015 - Simulation Frameworks for Morphogenetic Problems.pdf;/home/guillaume/Zotero/storage/7ELMFQDZ/197.html}
 }
 
 @article{taoCellMechanicsDialogue2017,
@@ -3809,15 +3809,15 @@ @article{taoCellMechanicsDialogue2017
   date = {2017},
   journaltitle = {Rep. Prog. Phys.},
   volume = {80},
+  number = {3},
   pages = {036601},
   issn = {0034-4885},
   doi = {10.1088/1361-6633/aa5282},
   url = {http://stacks.iop.org/0034-4885/80/i=3/a=036601},
   urldate = {2017-02-03},
   abstract = {Under the microscope, eukaryotic animal cells can adopt a variety of different shapes and sizes. These cells also move and deform, and the physical mechanisms driving these movements and shape changes are important in fundamental cell biology, tissue mechanics, as well as disease biology. This article reviews some of the basic mechanical concepts in cells, emphasizing continuum mechanics description of cytoskeletal networks and hydrodynamic flows across the cell membrane. We discuss how cells can generate movement and shape changes by controlling mass fluxes at the cell boundary. These mass fluxes can come from polymerization/depolymerization of actin cytoskeleton, as well as osmotic and hydraulic pressure-driven flow of water across the cell membrane. By combining hydraulic pressure control with force balance conditions at the cell surface, we discuss a quantitative mechanism of cell shape and volume control. The broad consequences of this model on cell mechanosensation and tissue mechanics are outlined.},
-  file = {/home/guillaume/Zotero/storage/AQSX8MGJ/cell_mechanics_dialogue.pdf},
   langid = {english},
-  number = {3}
+  file = {/home/guillaume/Zotero/storage/AQSX8MGJ/cell_mechanics_dialogue.pdf}
 }
 
 @article{tetleyUnipolarDistributionsJunctional2016,
@@ -3826,17 +3826,17 @@ @article{tetleyUnipolarDistributionsJunctional2016
   date = {2016-05-16},
   journaltitle = {eLife},
   volume = {5},
+  eprint = {27183005},
+  eprinttype = {pmid},
   pages = {e12094},
   issn = {2050-084X},
   doi = {10.7554/eLife.12094},
   url = {https://elifesciences.org/content/5/e12094v3},
   urldate = {2017-03-30},
   abstract = {Analysing Myosin II unipolar planar polarisation with high spatial and temporal resolution during Drosophila axis extension reveals how tissue boundaries drive polarized cell intercalation while limiting cell mixing.},
-  eprint = {27183005},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/74G3NPQH/Tetley et al. - 2016 - Unipolar distributions of junctional Myosin II ide.pdf;/home/guillaume/Zotero/storage/GCMZFBVC/e12094.html},
+  langid = {english},
   keywords = {<italic>D. melanogaster</italic>,actomyosin,epithelial cells,Morphogenesis},
-  langid = {english}
+  file = {/home/guillaume/Zotero/storage/74G3NPQH/Tetley et al. - 2016 - Unipolar distributions of junctional Myosin II ide.pdf;/home/guillaume/Zotero/storage/GCMZFBVC/e12094.html}
 }
 
 @inproceedings{thibonFastAutomaticMyopic2014,
@@ -3852,18 +3852,18 @@ @inproceedings{thibonFastAutomaticMyopic2014
 }
 
 @patent{thompsonHeadEyeTracking2014,
+  type = {patent},
   title = {Head and Eye Tracking},
   author = {Thompson, Ben and TURUWHENUA, Jason},
+  holder = {{Auckland Uniservices Limited}},
   date = {2014-10-16},
+  number = {WO2014168492A1},
   location = {{WO}},
   url = {https://patents.google.com/patent/WO2014168492A1/en},
   urldate = {2019-03-13},
   abstract = {Embodiments of the invention relate to a method of extracting eye velocity information from a video footage having a plurality of frames, comprising detecting at least part of an eye in at least two frames of the video footage, applying an optical flow algorithm to the at least two frames of the video footage to extract pixel velocity information, and determining a statistical measure from the pixel velocity information within the detected at least part of the eye. Other embodiments of the invention relate to a method of extracting head image trajectory information from a video footage having a plurality of frames, comprising detecting at least part of a facial region of the head image in at least two frames of the video footage, determining a measure of the movement of the at least part of a facial region between the at least two frames, and determining a transformation map from the measure of the movement.},
-  file = {/home/guillaume/Zotero/storage/XEQ5F5CV/Thompson et TURUWHENUA - 2014 - Head and eye tracking.pdf},
-  holder = {{Auckland Uniservices Limited}},
   keywords = {device,eye,frames,method,system},
-  number = {WO2014168492A1},
-  type = {patent}
+  file = {/home/guillaume/Zotero/storage/XEQ5F5CV/Thompson et TURUWHENUA - 2014 - Head and eye tracking.pdf}
 }
 
 @online{ThreedimensionalNanoscopyWhole,
@@ -3880,6 +3880,7 @@ @incollection{tinevezNEMODotsAssembly2020
   author = {Tinevez, Jean-Yves and Herbert, Sébastien},
   editor = {Miura, Kota and Sladoje, Nataša},
   date = {2020},
+  series = {Learning {{Materials}} in {{Biosciences}}},
   pages = {67--96},
   publisher = {{Springer International Publishing}},
   location = {{Cham}},
@@ -3888,8 +3889,7 @@ @incollection{tinevezNEMODotsAssembly2020
   urldate = {2019-10-24},
   abstract = {The aim of this chapter is to learn the principles and pitfalls of single-particle tracking (SPT). Tracking in general is very important for dynamic studies, as it is about propagating object identities over time, permitting the calculation of dynamic quantities such as object velocities. Tracking is often the first step in analyzing dynamics.},
   isbn = {978-3-030-22386-1},
-  langid = {english},
-  series = {Learning {{Materials}} in {{Biosciences}}}
+  langid = {english}
 }
 
 @article{tlili_mechanical_2013,
@@ -3897,15 +3897,15 @@ @article{tlili_mechanical_2013
   author = {Tlili, Sham and Gay, Cyprien and Graner, Francois and Marcq, Philippe and Molino, François and Saramito, Pierre},
   date = {2013},
   volume = {6},
+  number = {1},
+  eprint = {25957180},
+  eprinttype = {pmid},
   pages = {23},
   issn = {1292-8941},
   doi = {10.1140/epje/i2015-15033-4},
   url = {http://arxiv.org/abs/1309.7432},
   abstract = {The understanding of morphogenesis in living organisms has been renewed by tremendous progress in experimental techniques that provide access to cell-scale, quantitative information both on the shapes of cells within tissues and on the genes being expressed. This information suggests that our understanding of the respective contributions of gene expression and mechanics, and of their crucial entanglement, will soon leap forward. Biomechanics increasingly benefits from models, which assist the design and interpretation of experiments, point out the main ingredients and assumptions, and can ultimately lead to predictions. The newly accessible local information thus urges for a reflection on how to select suitable classes of mechanical models. We review both mechanical ingredients suggested by the current knowledge of tissue behaviour, and modelling methods that can help generate a constitutive equation. We also recall the mathematical framework developped for continuum materials and how to transform a constitutive equation into a system of partial differential equations amenable to numerical resolution. The present article thus groups together mechanical elements and theoretical methods that are ready to enhance the significance of the data extracted from recent or future high throughput biomechanical experiments.},
-  eprint = {25957180},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/5RAN8S3Z/Tlili et al. - 2013 - Mechanical formalism for tissue dynamics.pdf},
-  number = {1}
+  file = {/home/guillaume/Zotero/storage/5RAN8S3Z/Tlili et al. - 2013 - Mechanical formalism for tissue dynamics.pdf}
 }
 
 @article{tliliColloquiumMechanicalFormalisms2015,
@@ -3915,15 +3915,15 @@ @article{tliliColloquiumMechanicalFormalisms2015
   date = {2015-05-01},
   journaltitle = {Eur. Phys. J. E},
   volume = {38},
+  number = {5},
   pages = {33},
   issn = {1292-8941, 1292-895X},
   doi = {10.1140/epje/i2015-15033-4},
   url = {https://link.springer.com/article/10.1140/epje/i2015-15033-4},
   urldate = {2017-11-21},
   abstract = {The understanding of morphogenesis in living organisms has been renewed by tremendous progress in experimental techniques that provide access to cell scale, quantitative information both on the shapes of cells within tissues and on the genes being expressed. This information suggests that our understanding of the respective contributions of gene expression and mechanics, and of their crucial entanglement, will soon leap forward. Biomechanics increasingly benefits from models, which assist the design and interpretation of experiments, point out the main ingredients and assumptions, and ultimately lead to predictions. The newly accessible local information thus calls for a reflection on how to select suitable classes of mechanical models. We review both mechanical ingredients suggested by the current knowledge of tissue behaviour, and modelling methods that can help generate a rheological diagram or a constitutive equation. We distinguish cell scale (“intra-cell”) and tissue scale (“inter-cell”) contributions. We recall the mathematical framework developed for continuum materials and explain how to transform a constitutive equation into a set of partial differential equations amenable to numerical resolution. We show that when plastic behaviour is relevant, the dissipation function formalism appears appropriate to generate constitutive equations; its variational nature facilitates numerical implementation, and we discuss adaptations needed in the case of large deformations. The present article gathers theoretical methods that can readily enhance the significance of the data to be extracted from recent or future high throughput biomechanical experiments.Graphical abstractOpen image in new window},
-  file = {/home/guillaume/Zotero/storage/VNC27DJB/10.html},
   langid = {english},
-  number = {5}
+  file = {/home/guillaume/Zotero/storage/VNC27DJB/10.html}
 }
 
 @article{treguierChitosanFilmsMicrofluidic2019,
@@ -3932,15 +3932,15 @@ @article{treguierChitosanFilmsMicrofluidic2019
   date = {2019-08-27},
   journaltitle = {mBio},
   volume = {10},
+  number = {4},
   pages = {e01375-19},
   issn = {2150-7511},
   doi = {10.1128/mBio.01375-19},
   url = {https://mbio.asm.org/content/10/4/e01375-19},
   urldate = {2019-08-21},
   abstract = {Single-cell microfluidics is a powerful method to study bacteria and determine their susceptibility to antibiotic treatment. Glass treatment by adhesive molecules is a potential solution to immobilize bacterial cells and perform microscopy, but traditional cationic polymers such as polylysine deeply affect bacterial physiology. In this work, we chemically characterized a class of chitosan polymers for their biocompatibility when adsorbed to glass. Chitosan chains of known length and composition allowed growth of Escherichia coli cells without any deleterious effects on cell physiology. Combined with a machine learning approach, this method could measure the antibiotic susceptibility of a diversity of clinical strains in less than 1 h and with higher accuracy than current methods. Finally, chitosan polymers also supported growth of Klebsiella pneumoniae, another bacterial pathogen of clinical significance. IMPORTANCE Current microfluidic techniques are powerful to study bacteria and determine their response to antibiotic treatment, but they are currently limited by their complex manipulation. Chitosan films are fully biocompatible and could thus be a viable replacement for existing commercial devices that currently use polylysine. Thus, the low cost of chitosan slides and their simple implementation make them highly versatile for research as well as clinical use.},
-  file = {/home/guillaume/Zotero/storage/6DSTAKHZ/Tréguier et al. - 2019 - Chitosan Films for Microfluidic Studies of Single .pdf;/home/guillaume/Zotero/storage/MDYAALUG/e01375-19.html},
   langid = {english},
-  number = {4}
+  file = {/home/guillaume/Zotero/storage/6DSTAKHZ/Tréguier et al. - 2019 - Chitosan Films for Microfluidic Studies of Single .pdf;/home/guillaume/Zotero/storage/MDYAALUG/e01375-19.html}
 }
 
 @article{trepatMesoscalePhysicalPrinciples2018,
@@ -3949,15 +3949,15 @@ @article{trepatMesoscalePhysicalPrinciples2018
   date = {2018-07},
   journaltitle = {Nature Physics},
   volume = {14},
+  number = {7},
   pages = {671--682},
   issn = {1745-2481},
   doi = {10.1038/s41567-018-0194-9},
   url = {https://www.nature.com/articles/s41567-018-0194-9},
   urldate = {2018-07-11},
   abstract = {The behaviour of cells and tissues can be understood in terms of emergent mesoscale states that are determined by a set of physical properties. This Review surveys experimental evidence for these states and the physics underpinning them.},
-  file = {/home/guillaume/Zotero/storage/R9R6QRSP/s41567-018-0194-9.html},
   langid = {english},
-  number = {7}
+  file = {/home/guillaume/Zotero/storage/R9R6QRSP/s41567-018-0194-9.html}
 }
 
 @article{truongquangPrinciplesECadherinSupramolecular2013,
@@ -3966,14 +3966,14 @@ @article{truongquangPrinciplesECadherinSupramolecular2013
   date = {2013-11-18},
   journaltitle = {Current Biology},
   volume = {23},
+  number = {22},
   pages = {2197--2207},
   issn = {0960-9822},
   doi = {10.1016/j.cub.2013.09.015},
   url = {http://www.sciencedirect.com/science/article/pii/S0960982213011317},
   urldate = {2017-07-03},
   abstract = {E-cadherin plays a pivotal role in tissue morphogenesis by forming clusters that support intercellular adhesion and transmit tension. What controls E-cadherin mesoscopic organization in clusters is unclear. We use 3D superresolution quantitative microscopy in Drosophila embryos to characterize the size distribution of~E-cadherin nanometric clusters. The cluster size follows power-law distributions over three orders of magnitude with exponential decay at large cluster sizes. By exploring the predictions of a general theoretical framework including cluster fusion and fission events and recycling of E-cadherin, we identify two distinct active mechanisms setting the cluster-size distribution. Dynamin-dependent endocytosis targets large clusters only, thereby imposing a cutoff size. Moreover, interactions between E-cadherin clusters and actin filaments control the fission in a size-dependent manner. E-cadherin clustering depends on key cortical regulators, which provide tunable and local control over E-cadherin organization. Our data provide the foundation for a quantitative understanding of how E-cadherin distribution affects adhesion and might regulate force transmission in~vivo.},
-  file = {/home/guillaume/Zotero/storage/4WRKXJSV/S0960982213011317.html},
-  number = {22}
+  file = {/home/guillaume/Zotero/storage/4WRKXJSV/S0960982213011317.html}
 }
 
 @article{turuwhenuaMethodDetectingOptokinetic2014,
@@ -3988,8 +3988,8 @@ @article{turuwhenuaMethodDetectingOptokinetic2014
   url = {http://www.sciencedirect.com/science/article/pii/S0042698914001758},
   urldate = {2019-03-13},
   abstract = {Optokinetic nystagmus (OKN) is the sawtooth movement of the eye elicited when an observer views a repeated moving pattern. We present a method for identifying the presence and direction of OKN in recordings of the eye made using a standard off-the-shelf video-camera or webcam. Our approach uses vertical edge detection to determine the limbus/iris boundary, and we estimate the velocity of the edge using Lucas–Kanade optical flow. Heuristic rules are applied to identify saccadic velocity peaks from the resulting velocity signal. The normalized average of the resulting peaks is used to estimate the presence/direction of OKN. Our preliminary testing with six participants observing global motion stimuli with full or partial coherence yields an accuracy of 93\% which compares favorably to the performance of an experienced human observer (98\% accuracy). Additional tests using high contrast, square-wave gratings show that performance of the technique is consistent at stimulus speeds of 5 and 10deg/s and that OKN is not reported by the algorithm when participants view stationary stimuli.},
-  file = {/home/guillaume/Zotero/storage/QENWMYV6/Turuwhenua et al. - 2014 - A method for detecting optokinetic nystagmus based.pdf;/home/guillaume/Zotero/storage/77ETXLZX/S0042698914001758.html},
-  keywords = {Optic flow,Optokinetic nystagmus,Random-dot-kinetogram,Video-oculography}
+  keywords = {Optic flow,Optokinetic nystagmus,Random-dot-kinetogram,Video-oculography},
+  file = {/home/guillaume/Zotero/storage/QENWMYV6/Turuwhenua et al. - 2014 - A method for detecting optokinetic nystagmus based.pdf;/home/guillaume/Zotero/storage/77ETXLZX/S0042698914001758.html}
 }
 
 @article{tuvesonCancerModelingMeets2019,
@@ -3998,16 +3998,16 @@ @article{tuvesonCancerModelingMeets2019
   date = {2019-06-07},
   journaltitle = {Science},
   volume = {364},
+  number = {6444},
+  eprint = {31171691},
+  eprinttype = {pmid},
   pages = {952--955},
   issn = {0036-8075, 1095-9203},
   doi = {10.1126/science.aaw6985},
   url = {https://science.sciencemag.org/content/364/6444/952},
   urldate = {2019-06-10},
   abstract = {Organoids are microscopic self-organizing, three-dimensional structures that are grown from stem cells in vitro. They recapitulate many structural and functional aspects of their in vivo counterpart organs. This versatile technology has led to the development of many novel human cancer models. It is now possible to create indefinitely expanding organoids starting from tumor tissue of individuals suffering from a range of carcinomas. Alternatively, CRISPR-based gene modification allows the engineering of organoid models of cancer through the introduction of any combination of cancer gene alterations to normal organoids. When combined with immune cells and fibroblasts, tumor organoids become models for the cancer microenvironment enabling immune-oncology applications. Emerging evidence indicates that organoids can be used to accurately predict drug responses in a personalized treatment setting. Here, we review the current state and future prospects of the rapidly evolving tumor organoid field.},
-  eprint = {31171691},
-  eprinttype = {pmid},
-  langid = {english},
-  number = {6444}
+  langid = {english}
 }
 
 @article{vanderleestVertexSlidingDrives2018,
@@ -4022,8 +4022,8 @@ @article{vanderleestVertexSlidingDrives2018
   url = {https://elifesciences.org/articles/34586},
   urldate = {2018-07-12},
   abstract = {Tricellular vertices possess sliding behaviors that harness radial forces and drive cell shape changes and intercalation in an epithelial tissue.},
-  file = {/home/guillaume/Zotero/storage/3TCHK67V/Vanderleest et al. - 2018 - Vertex sliding drives intercalation by radial coup.pdf;/home/guillaume/Zotero/storage/K4QRIQYF/34586.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/3TCHK67V/Vanderleest et al. - 2018 - Vertex sliding drives intercalation by radial coup.pdf;/home/guillaume/Zotero/storage/K4QRIQYF/34586.html}
 }
 
 @article{vanliedekerkeQuantitativeHighresolutionComputational2020,
@@ -4032,17 +4032,17 @@ @article{vanliedekerkeQuantitativeHighresolutionComputational2020
   date = {2020-02},
   journaltitle = {Biomech Model Mechanobiol},
   volume = {19},
+  number = {1},
+  eprint = {31749071},
+  eprinttype = {pmid},
   pages = {189--220},
   issn = {1617-7940},
   doi = {10.1007/s10237-019-01204-7},
   abstract = {Mathematical models are increasingly designed to guide experiments in biology, biotechnology, as well as to assist in medical decision making. They are in particular important to understand emergent collective cell behavior. For this purpose, the models, despite still abstractions of reality, need to be quantitative in all aspects relevant for the question of interest. This paper considers as showcase example the regeneration of liver after drug-induced depletion of hepatocytes, in which the surviving and dividing hepatocytes must squeeze in between the blood vessels of a network to refill the emerged lesions. Here, the cells' response to mechanical stress might significantly impact the regeneration process. We present a 3D high-resolution cell-based model integrating information from measurements in order to obtain a refined and quantitative understanding of the impact of cell-biomechanical effects on the closure of drug-induced lesions in liver. Our model represents each cell individually and is constructed by a discrete, physically scalable network of viscoelastic elements, capable of mimicking realistic cell deformation and supplying information at subcellular scales. The cells have the capability to migrate, grow, and divide, and the nature and parameters of their mechanical elements can be inferred from comparisons with optical stretcher experiments. Due to triangulation of the cell surface, interactions of cells with arbitrarily shaped (triangulated) structures such as blood vessels can be captured naturally. Comparing our simulations with those of so-called center-based models, in which cells have a~largely rigid shape and forces are exerted between cell centers, we find that the migration forces a cell needs to exert on its environment to close a tissue lesion, is much smaller than predicted by center-based models.~To stress generality of the approach, the liver simulations were complemented by monolayer and multicellular spheroid growth simulations. In summary, our model can give quantitative insight in many tissue organization processes, permits hypothesis testing in silico, and guide experiments in situations in which cell mechanics is considered important.},
-  eprint = {31749071},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/AW3GCSJ5/Van Liedekerke et al. - 2020 - A quantitative high-resolution computational mecha.pdf},
-  keywords = {Algorithms,Biomechanical Phenomena,Calibration,Cell Adhesion,Cell Line; Tumor,Cell mechanics,Cell Movement,Cell Proliferation,Cell-based model,Computer Simulation,Cytoskeleton,Hepatocytes,High resolution cell model,Humans,Liver,Liver regeneration,Models; Biological,Neoplasms,Optical stretcher,Regeneration},
   langid = {english},
-  number = {1},
-  pmcid = {PMC7005086}
+  pmcid = {PMC7005086},
+  keywords = {Algorithms,Biomechanical Phenomena,Calibration,Cell Adhesion,Cell Line; Tumor,Cell mechanics,Cell Movement,Cell Proliferation,Cell-based model,Computer Simulation,Cytoskeleton,Hepatocytes,High resolution cell model,Humans,Liver,Liver regeneration,Models; Biological,Neoplasms,Optical stretcher,Regeneration},
+  file = {/home/guillaume/Zotero/storage/AW3GCSJ5/Van Liedekerke et al. - 2020 - A quantitative high-resolution computational mecha.pdf}
 }
 
 @article{vasquez_dynamic_2014,
@@ -4051,30 +4051,30 @@ @article{vasquez_dynamic_2014
   date = {2014},
   journaltitle = {Journal of Cell Biology},
   volume = {206},
+  number = {3},
+  eprint = {25092658},
+  eprinttype = {pmid},
   pages = {435--450},
   issn = {15408140},
   doi = {10.1083/jcb.201402004},
   abstract = {Apical constriction is a cell shape change that promotes epithelial bending. Activation of nonmuscle myosin II (Myo-II) by kinases such as Rho-associated kinase (Rok) is important to generate contractile force during apical constriction. Cycles of Myo-II assembly and disassembly, or pulses, are associated with apical constriction during Drosophila melanogaster gastrulation. It is not understood whether Myo-II phosphoregulation organizes contractile pulses or whether pulses are important for tissue morphogenesis. Here, we show that Myo-II pulses are associated with pulses of apical Rok. Mutants that mimic Myo-II light chain phosphorylation or depletion of myosin phosphatase inhibit Myo-II contractile pulses, disrupting both actomyosin coalescence into apical foci and cycles of Myo-II assembly/disassembly. Thus, coupling dynamic Myo-II phosphorylation to upstream signals organizes contractile Myo-II pulses in both space and time. Mutants that mimic Myo-II phosphorylation undergo continuous, rather than incremental, apical constriction. These mutants fail to maintain intercellular actomyosin network connections during tissue invagination, suggesting that Myo-II pulses are required for tissue integrity during morphogenesis.},
-  eprint = {25092658},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/WERPFI8I/Vasquez, Tworoger, Martin - 2014 - Dynamic myosin phosphorylation regulates contractile pulses and tissue integrity during epithelial mo.pdf},
-  number = {3}
+  file = {/home/guillaume/Zotero/storage/WERPFI8I/Vasquez, Tworoger, Martin - 2014 - Dynamic myosin phosphorylation regulates contractile pulses and tissue integrity during epithelial mo.pdf}
 }
 
 @online{vazquez-faciMechanicsEpithelialTissue2017,
   title = {Mechanics of Epithelial Tissue Formation in Early Insect Embryos},
   author = {Vazquez-Faci, Tania and van Drongelen, Ruben and van der Zee, Maurijn and Idema, Timon},
+  options = {useprefix=true},
   date = {2017-05-17},
+  eprint = {1705.06205},
+  eprinttype = {arxiv},
+  primaryclass = {cond-mat, physics:physics, q-bio},
   url = {http://arxiv.org/abs/1705.06205},
   urldate = {2017-06-07},
   abstract = {A key process in the life of any multicellular organism is its development from a single fertilized egg into a full grown adult. Naturally, this process has been studied in great detail, with particular focus on its biochemical and genetic aspects. However, the mechanics of development have gained much less attention. Here we use two model organisms, the red flour beetle Tribolium castaneum and the fruit fly Drosophila melanogaster, to determine the role of mechanics in the formation of their first tissue layer, the blastoderm. We find that the membranes of the cells in this tissue arrange in a specific mathematical pattern known as a Voronoi tessellation, with the nuclei of the cells in the centers. To understand this pattern-forming process, we simulate the growth of the cells using a mechanical model comprising the nuclei, radial microtubules and actin cortex of the cells. We find that cell-cell interactions in such a purely mechanical system indeed lead to the formation of a Voronoi tessellation. The geometric and topological properties of the tessellations we find in our experiments quantitatively match with our simulations. Moreover, comparison with recent jamming models suggests that the tissues spontaneously organize at the highest possible density that is still on the liquid side of the jamming transition.},
   archiveprefix = {arXiv},
-  eprint = {1705.06205},
-  eprinttype = {arxiv},
-  file = {/home/guillaume/Zotero/storage/CGBTUXVM/Vazquez-Faci et al. - 2017 - Mechanics of epithelial tissue formation in early .pdf;/home/guillaume/Zotero/storage/RH27A8ZI/1705.html},
   keywords = {Condensed Matter - Soft Condensed Matter,Physics - Biological Physics,Quantitative Biology - Tissues and Organs},
-  options = {useprefix=true},
-  primaryclass = {cond-mat, physics:physics, q-bio}
+  file = {/home/guillaume/Zotero/storage/CGBTUXVM/Vazquez-Faci et al. - 2017 - Mechanics of epithelial tissue formation in early .pdf;/home/guillaume/Zotero/storage/RH27A8ZI/1705.html}
 }
 
 @article{vlachogiannisPatientderivedOrganoidsModel2018,
@@ -4083,15 +4083,15 @@ @article{vlachogiannisPatientderivedOrganoidsModel2018
   date = {2018-02-23},
   journaltitle = {Science},
   volume = {359},
+  number = {6378},
   pages = {920--926},
   issn = {0036-8075, 1095-9203},
   doi = {10.1126/science.aao2774},
   url = {http://science.sciencemag.org/content/359/6378/920},
   urldate = {2018-02-23},
   abstract = {Cancer organoids to model therapy response Cancer organoids are miniature, three-dimensional cell culture models that can be made from primary patient tumors and studied in the laboratory. Vlachogiannis et al. asked whether such “tumor-in-a-dish” approaches can be used to predict drug responses in the clinic. They generated a live organoid biobank from patients with metastatic gastrointestinal cancer who had previously been enrolled in phase I or II clinical trials. This allowed the authors to compare organoid drug responses with how the patient actually responded in the clinic. Encouragingly, the organoids had similar molecular profiles to those of the patient tumor, reinforcing their value as a platform for drug screening and development. Science, this issue p. 920 Patient-derived organoids (PDOs) have recently emerged as robust preclinical models; however, their potential to predict clinical outcomes in patients has remained unclear. We report on a living biobank of PDOs from metastatic, heavily pretreated colorectal and gastroesophageal cancer patients recruited in phase 1/2 clinical trials. Phenotypic and genotypic profiling of PDOs showed a high degree of similarity to the original patient tumors. Molecular profiling of tumor organoids was matched to drug-screening results, suggesting that PDOs could complement existing approaches in defining cancer vulnerabilities and improving treatment responses. We compared responses to anticancer agents ex vivo in organoids and PDO-based orthotopic mouse tumor xenograft models with the responses of the patients in clinical trials. Our data suggest that PDOs can recapitulate patient responses in the clinic and could be implemented in personalized medicine programs. Organoids can recapitulate patient responses in the clinic, with potential for drug screening and personalized medicine. Organoids can recapitulate patient responses in the clinic, with potential for drug screening and personalized medicine.},
-  file = {/home/guillaume/Zotero/storage/2Q65DCY6/920.html},
   langid = {english},
-  number = {6378}
+  file = {/home/guillaume/Zotero/storage/2Q65DCY6/920.html}
 }
 
 @article{vuong-brenderApicalECMPreserves2017,
@@ -4099,13 +4099,13 @@ @article{vuong-brenderApicalECMPreserves2017
   author = {Vuong-Brender, Thanh Thi Kim and Suman, Shashi Kumar and Labouesse, Michel},
   date = {2017-05-19},
   journaltitle = {Development},
+  eprint = {28526752},
+  eprinttype = {pmid},
   issn = {1477-9129},
   doi = {10.1242/dev.150383},
   abstract = {Epithelia are bound by both basal and apical extracellular matrices (ECM). While the composition and function of the former have been intensively investigated, less is known about the latter. The embryonic sheath, the ECM apical to the C. elegans embryonic epidermis, has been suggested to promote its elongation. In an RNAi screen for the components of the sheath, we identified the Zona Pellucida domain proteins NOAH-1 and NOAH-2. We found that these proteins act in the same pathway, and in parallel to three other putative sheath proteins, SYM-1, LET-4 and FBN-1/Fibrillin, to ensure embryonic integrity and promote elongation. Laser nano-ablation experiments to map the stress field show that NOAH-1 and NOAH-2, together with PAK-1/p21-activated kinase, maintain and relay the actomyosin-dependent stress generated within the lateral epidermis before muscles become active. Subsequently, loss of function experiments show that apical ECM proteins are essential for muscle anchoring and for relaying the mechanical input from muscle contractions, which are essential for elongation. Hence, the apical ECM contributes to morphogenesis by maintaining embryonic integrity and relaying mechanical stress.},
-  eprint = {28526752},
-  eprinttype = {pmid},
-  keywords = {Apical extracellular matrix,C. elegans,Embryonic elongation,Laser nano-ablation,Muscle anchoring,Zona Pellucida Protein},
-  langid = {english}
+  langid = {english},
+  keywords = {Apical extracellular matrix,C. elegans,Embryonic elongation,Laser nano-ablation,Muscle anchoring,Zona Pellucida Protein}
 }
 
 @article{waitesOrganoidTissuePatterning2017,
@@ -4119,8 +4119,8 @@ @article{waitesOrganoidTissuePatterning2017
   url = {http://biorxiv.org/content/early/2017/05/10/136366},
   urldate = {2017-05-11},
   abstract = {Exactly a century ago, D'Arcy Thompson set an agenda for understanding tissue development in terms of underlying biophysical, mathematically-tractable mechanisms. One such mechanism, discovered by Steinberg in the 1960s, is adhesion-mediated sorting of cell mixtures into homotypic groups. Interest in this phase separation mechanism has recently surged, partly because of its use to create synthetic biological patterning mechanisms and partly because it has been found to drive events critical to the formation of organoids from stem cells, making the process relevant to biotechnology as well as to basic development. Here, we construct quantitative model of patterning by phase separation, informed by laboratory data, and use it to explore the relationship between degree of adhesive difference and speed, type and extent of resultant patterning. Our results can be used three ways; to predict the outcome of mixing cells with known properties, to estimate the properties required to make some designed organoid system, or to estimate underlying cellular properties from observed behaviour.},
-  file = {/home/guillaume/Zotero/storage/IEHVHIU7/Waites et al. - 2017 - Organoid And Tissue Patterning Through Phase Separ.pdf;/home/guillaume/Zotero/storage/8PWTQ5P7/136366.html},
-  langid = {english}
+  langid = {english},
+  file = {/home/guillaume/Zotero/storage/IEHVHIU7/Waites et al. - 2017 - Organoid And Tissue Patterning Through Phase Separ.pdf;/home/guillaume/Zotero/storage/8PWTQ5P7/136366.html}
 }
 
 @article{wang_cell-level_2012,
@@ -4129,15 +4129,15 @@ @article{wang_cell-level_2012
   date = {2012},
   journaltitle = {Biophysical Journal},
   volume = {103},
+  number = {11},
+  eprint = {23283225},
+  eprinttype = {pmid},
   pages = {2265--2274},
   issn = {00063495},
   doi = {10.1016/j.bpj.2012.09.036},
   url = {http://dx.doi.org/10.1016/j.bpj.2012.09.036},
   abstract = {We report a model describing the various stages of dorsal closure of Drosophila. Inspired by experimental observations, we represent the amnioserosa by 81 hexagonal cells that are coupled mechanically through the position of the nodes and the elastic forces on the edges. In addition, each cell has radial spokes representing actin filaments on which myosin motors can attach and exert contractile forces on the nodes, the attachment being controlled by a signaling molecule. Thus, the model couples dissipative cell and tissue motion with kinetic equations describing the myosin and signal dynamics. In the early phase, amnioserosa cells oscillate as a result of coupling among the chemical signaling, myosin attachment/detachment, and mechanical deformation of neighboring cells. In the slow phase, we test two ratcheting mechanisms suggested by experiments: an internal ratchet by the apical and junctional myosin condensates, and an external one by the supracellular actin cables encircling the amnioserosa. Within the range of parameters tested, the model predictions suggest the former as the main contributor to cell and tissue area reduction in this stage. In the fast phase of dorsal closure, cell pulsation is arrested, and the cell and tissue areas contract consistently. This is realized in the model by gradually shrinking the resting length of the spokes. Overall, the model captures the key features of dorsal closure through the three distinct phases, and its predictions are in good agreement with observations. ?? 2012 Biophysical Society.},
-  eprint = {23283225},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/J3S8I4HS/Wang, Feng, Pismen - 2012 - A cell-level biomechanical model of Drosophila dorsal closure.pdf},
-  number = {11}
+  file = {/home/guillaume/Zotero/storage/J3S8I4HS/Wang, Feng, Pismen - 2012 - A cell-level biomechanical model of Drosophila dorsal closure.pdf}
 }
 
 @article{weisenburgerLightMicroscopyOngoing2015,
@@ -4147,6 +4147,9 @@ @article{weisenburgerLightMicroscopyOngoing2015
   date = {2015-04-03},
   journaltitle = {Contemporary Physics},
   volume = {56},
+  number = {2},
+  eprint = {1412.3255},
+  eprinttype = {arxiv},
   pages = {123--143},
   issn = {0010-7514, 1366-5812},
   doi = {10.1080/00107514.2015.1026557},
@@ -4154,12 +4157,9 @@ @article{weisenburgerLightMicroscopyOngoing2015
   urldate = {2020-10-05},
   abstract = {Optical microscopy is one of the oldest scientific instruments that is still used in forefront research. Ernst Abbe’s nineteenth century formulation of the resolution limit in microscopy let generations of scientists believe that optical studies of individual molecules and resolving sub-wavelength structures were not feasible. The Nobel Prize in 2014 for superresolution fluorescence microscopy marks a clear recognition that the old beliefs have to be revisited. In this article, we present a critical overview of various recent developments in optical microscopy. In addition to the popular super-resolution fluorescence methods, we discuss the prospects of various other techniques and imaging contrasts and consider some of the fundamental and practical challenges that lie ahead.},
   archiveprefix = {arXiv},
-  eprint = {1412.3255},
-  eprinttype = {arxiv},
-  file = {/home/guillaume/Zotero/storage/74USYAAI/Weisenburger et Sandoghdar - 2015 - Light Microscopy An ongoing contemporary revoluti.pdf},
-  keywords = {Physics - Optics},
   langid = {english},
-  number = {2}
+  keywords = {Physics - Optics},
+  file = {/home/guillaume/Zotero/storage/74USYAAI/Weisenburger et Sandoghdar - 2015 - Light Microscopy An ongoing contemporary revoluti.pdf}
 }
 
 @article{westerOptokineticNystagmusMeasure2007,
@@ -4168,16 +4168,16 @@ @article{westerOptokineticNystagmusMeasure2007
   date = {2007-10-01},
   journaltitle = {INVEST.OPHTHAL.VISUAL SCI},
   volume = {48},
+  number = {10},
+  eprint = {17898276},
+  eprinttype = {pmid},
   pages = {4542--4548},
   issn = {0146-0404},
   doi = {10.1167/iovs.06-1206},
   url = {https://miami.pure.elsevier.com/en/publications/optokinetic-nystagmus-as-a-measure-of-visual-function-in-severely},
   urldate = {2019-03-13},
-  eprint = {17898276},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/4G4SDQJG/Wester et al. - 2007 - Optokinetic nystagmus as a measure of visual funct.pdf;/home/guillaume/Zotero/storage/9MCHYIT2/optokinetic-nystagmus-as-a-measure-of-visual-function-in-severely.html},
   langid = {english},
-  number = {10}
+  file = {/home/guillaume/Zotero/storage/4G4SDQJG/Wester et al. - 2007 - Optokinetic nystagmus as a measure of visual funct.pdf;/home/guillaume/Zotero/storage/9MCHYIT2/optokinetic-nystagmus-as-a-measure-of-visual-function-in-severely.html}
 }
 
 @article{wingreenBackFutureEducation2006,
@@ -4187,12 +4187,12 @@ @article{wingreenBackFutureEducation2006
   date = {2006-11},
   journaltitle = {Nature Reviews Molecular Cell Biology},
   volume = {7},
+  number = {11},
   pages = {829--832},
   issn = {1471-0072, 1471-0080},
   doi = {10.1038/nrm2023},
   url = {http://www.nature.com/doifinder/10.1038/nrm2023},
-  urldate = {2016-11-02},
-  number = {11}
+  urldate = {2016-11-02}
 }
 
 @article{yangEfficientLearningbasedBlur2020,
@@ -4201,6 +4201,7 @@ @article{yangEfficientLearningbasedBlur2020
   date = {2020-03-27},
   journaltitle = {PLOS ONE},
   volume = {15},
+  number = {3},
   pages = {e0230619},
   publisher = {{Public Library of Science}},
   issn = {1932-6203},
@@ -4208,10 +4209,9 @@ @article{yangEfficientLearningbasedBlur2020
   url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0230619},
   urldate = {2020-05-18},
   abstract = {In imaging systems, image blurs are a major source of degradation. This paper proposes a parameter estimation technique for linear motion blur, defocus blur, and atmospheric turbulence blur, and a nonlinear deconvolution algorithm based on sparse representation. Most blur removal techniques use image priors to estimate the point spread function (PSF); however, many common forms of image priors are unable to exploit local image information fully. In this paper, the proposed method does not require models of image priors. Further, it is capable of estimating the PSF accurately from a single input image. First, a blur feature in the image gradient domain is introduced, which has a positive correlation with the degree of blur. Next, the parameters for each blur type are estimated by a learning-based method using a general regression neural network. Finally, image restoration is performed using a half-quadratic optimization algorithm. Evaluation tests confirmed that the proposed method outperforms other similar methods and is suitable for dealing with motion blur in real-life applications.},
-  file = {/home/guillaume/Zotero/storage/AHYA7PI9/Yang et al. - 2020 - Efficient learning-based blur removal method based.pdf;/home/guillaume/Zotero/storage/FWBH83HS/article.html},
-  keywords = {Algorithms,Digital imaging,Imaging techniques,Machine learning algorithms,Neural networks,Optical lenses,Optimization,Turbulence},
   langid = {english},
-  number = {3}
+  keywords = {Algorithms,Digital imaging,Imaging techniques,Machine learning algorithms,Neural networks,Optical lenses,Optimization,Turbulence},
+  file = {/home/guillaume/Zotero/storage/AHYA7PI9/Yang et al. - 2020 - Efficient learning-based blur removal method based.pdf;/home/guillaume/Zotero/storage/FWBH83HS/article.html}
 }
 
 @article{yanThreeDimensionalSpatiotemporalModeling2018,
@@ -4220,30 +4220,30 @@ @article{yanThreeDimensionalSpatiotemporalModeling2018
   date = {2018-05-01},
   journaltitle = {Bull Math Biol},
   volume = {80},
+  number = {5},
   pages = {1404--1433},
   issn = {1522-9602},
   doi = {10.1007/s11538-017-0294-1},
   url = {https://doi.org/10.1007/s11538-017-0294-1},
   urldate = {2018-10-09},
   abstract = {We develop a three-dimensional multispecies mathematical model to simulate the growth of colon cancer organoids containing stem, progenitor and terminally differentiated cells, as a model of early (prevascular) tumor growth. Stem cells (SCs) secrete short-range self-renewal promoters (e.g., Wnt) and their long-range inhibitors (e.g., Dkk) and proliferate slowly. Committed progenitor (CP) cells proliferate more rapidly and differentiate to produce post-mitotic terminally differentiated cells that release differentiation promoters, forming negative feedback loops on SC and CP self-renewal. We demonstrate that SCs play a central role in normal and cancer colon organoids. Spatial patterning of the SC self-renewal promoter gives rise to SC clusters, which mimic stem cell niches, around the organoid surface, and drive the development of invasive fingers. We also study the effects of externally applied signaling factors. Applying bone morphogenic proteins, which inhibit SC and CP self-renewal, reduces invasiveness and organoid size. Applying hepatocyte growth factor, which enhances SC self-renewal, produces larger sizes and enhances finger development at low concentrations but suppresses fingers at high concentrations. These results are consistent with recent experiments on colon organoids. Because many cancers are hierarchically organized and are subject to feedback regulation similar to that in normal tissues, our results suggest that in cancer, control of cancer stem cell self-renewal should influence the size and shape in similar ways, thereby opening the door to novel therapies.},
-  file = {/home/guillaume/Zotero/storage/NJPETEK4/yan2017.pdf},
-  keywords = {Brain tumors,Cancer stem cells,Cancer therapies,Feedback regulation,Mathematical modeling},
   langid = {english},
-  number = {5}
+  keywords = {Brain tumors,Cancer stem cells,Cancer therapies,Feedback regulation,Mathematical modeling},
+  file = {/home/guillaume/Zotero/storage/NJPETEK4/yan2017.pdf}
 }
 
 @online{zayerLayeredFieldsNatural2018,
   title = {Layered {{Fields}} for {{Natural Tessellations}} on {{Surfaces}}},
   author = {Zayer, Rhaleb and Mlakar, Daniel and Steinberger, Markus and Seidel, Hans-Peter},
   date = {2018-04-24},
+  eprint = {1804.09152},
+  eprinttype = {arxiv},
+  primaryclass = {cs},
   url = {http://arxiv.org/abs/1804.09152},
   abstract = {Mimicking natural tessellation patterns is a fascinating multi-disciplinary problem. Geometric methods aiming at reproducing such partitions on surface meshes are commonly based on the Voronoi model and its variants, and are often faced with challenging issues such as metric estimation, geometric, topological complications, and most critically parallelization. In this paper, we introduce an alternate model which may be of value for resolving these issues. We drop the assumption that regions need to be separated by lines. Instead, we regard region boundaries as narrow bands and we model the partition as a set of smooth functions layered over the surface. Given an initial set of seeds or regions, the partition emerges as the solution of a time dependent set of partial differential equations describing concurrently evolving fronts on the surface. Our solution does not require geodesic estimation, elaborate numerical solvers, or complicated bookkeeping data structures. The cost per time-iteration is dominated by the multiplication and addition of two sparse matrices. Extension of our approach in a Lloyd's algorithm fashion can be easily achieved and the extraction of the dual mesh can be conveniently preformed in parallel through matrix algebra. As our approach relies mainly on basic linear algebra kernels, it lends itself to efficient implementation on modern graphics hardware.},
   archiveprefix = {arXiv},
-  eprint = {1804.09152},
-  eprinttype = {arxiv},
-  file = {/home/guillaume/Zotero/storage/TEPX32K4/Zayer et al. - 2018 - Layered Fields for Natural Tessellations on Surfac.pdf;/home/guillaume/Zotero/storage/MBC9KE2H/1804.html},
   keywords = {Computer Science - Distributed; Parallel; and Cluster Computing,Computer Science - Graphics},
-  primaryclass = {cs}
+  file = {/home/guillaume/Zotero/storage/TEPX32K4/Zayer et al. - 2018 - Layered Fields for Natural Tessellations on Surfac.pdf;/home/guillaume/Zotero/storage/MBC9KE2H/1804.html}
 }
 
 @article{zhang_signalling_2012,
@@ -4252,15 +4252,15 @@ @article{zhang_signalling_2012
   date = {2012},
   journaltitle = {Journal of Cell Science},
   volume = {125},
+  number = {17},
+  eprint = {22929901},
+  eprinttype = {pmid},
   pages = {4172--7172},
   issn = {0021-9533},
   doi = {10.1242/jcs.119776},
   abstract = {There is growing awareness that mechanical forces - in parallel to electrical or chemical inputs - have a central role in driving development and influencing the outcome of many diseases. However, we still have an incomplete understanding of how such forces function in coordination with each other and with other signalling inputs in vivo. Mechanical forces, which are generated throughout the organism, can produce signals through force-sensitive processes. Here, we first explore the mechanisms through which forces can be generated and the cellular responses to forces by discussing several examples from animal development. We then go on to examine the mechanotransduction-induced signalling processes that have been identified in vivo. Finally, we discuss what is known about the specificity of the responses to different forces, the mechanisms that might stabilize cells in response to such forces, and the crosstalk between mechanical forces and chemical signalling. Where known, we mention kinetic parameters that characterize forces and their responses. The multi-layered regulatory control of force generation, force response and force adaptation should be viewed as a well-integrated aspect in the greater biological signalling systems.},
-  eprint = {22929901},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/RDTEZICE/Zhang, Labouesse - 2012 - Signalling through mechanical inputs - a coordinated process.pdf},
   keywords = {embryo,mechano-sensation,Morphogenesis,signalling},
-  number = {17}
+  file = {/home/guillaume/Zotero/storage/RDTEZICE/Zhang, Labouesse - 2012 - Signalling through mechanical inputs - a coordinated process.pdf}
 }
 
 @article{zhangGaussianApproximationsFluorescence2007,
@@ -4269,6 +4269,7 @@ @article{zhangGaussianApproximationsFluorescence2007
   date = {2007-04-01},
   journaltitle = {Appl. Opt., AO},
   volume = {46},
+  number = {10},
   pages = {1819--1829},
   publisher = {{Optical Society of America}},
   issn = {2155-3165},
@@ -4276,10 +4277,9 @@ @article{zhangGaussianApproximationsFluorescence2007
   url = {https://www.osapublishing.org/ao/abstract.cfm?uri=ao-46-10-1819},
   urldate = {2020-04-15},
   abstract = {We comprehensively study the least-squares Gaussian approximations of the diffraction-limited 2D-3D paraxial-nonparaxial point-spread functions (PSFs) of the wide field fluorescence microscope (WFFM), the laser scanning confocal microscope (LSCM), and the disk scanning confocal microscope (DSCM). The PSFs are expressed using the Debye integral. Under an L∞ constraint imposing peak matching, optimal and near-optimal Gaussian parameters are derived for the PSFs. With an L1 constraint imposing energy conservation, an optimal Gaussian parameter is derived for the 2D paraxial WFFM PSF. We found that (1) the 2D approximations are all very accurate; (2) no accurate Gaussian approximation exists for 3D WFFM PSFs; and (3) with typical pinhole sizes, the 3D approximations are accurate for the DSCM and nearly perfect for the LSCM. All the Gaussian parameters derived in this study are in explicit analytical form, allowing their direct use in practical applications.},
-  file = {/home/guillaume/Zotero/storage/EEU3WVH9/abstract.html},
-  keywords = {Confocal laser scanning microscopy,Fluorescence microscopy,Fourier transforms,Point spread function,Refractive index,Wave optics},
   langid = {english},
-  number = {10}
+  keywords = {Confocal laser scanning microscopy,Fluorescence microscopy,Fourier transforms,Point spread function,Refractive index,Wave optics},
+  file = {/home/guillaume/Zotero/storage/EEU3WVH9/abstract.html}
 }
 
 @article{zhaoModelingTumorClonal2016,
@@ -4288,17 +4288,17 @@ @article{zhaoModelingTumorClonal2016
   date = {2016-03},
   journaltitle = {Trends Cancer},
   volume = {2},
+  number = {3},
+  eprint = {28435907},
+  eprinttype = {pmid},
   pages = {144--158},
   issn = {2405-8033},
   doi = {10.1016/j.trecan.2016.02.001},
   url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5400294/},
   urldate = {2021-01-27},
   abstract = {Cancer is a clonal evolutionary process. This presents challenges for effective therapeutic intervention, given the constant selective pressure towards drug resistance. Mathematical modeling from population genetics, evolutionary dynamics, and engineering perspectives are being increasingly employed to study tumor progression, intratumoral heterogeneity, drug resistance, and rational drug scheduling and combinations design. In this review, we discuss promising opportunities these inter-disciplinary approaches hold for advances in cancer biology and treatment. We propose that quantitative modeling perspectives can complement emerging experimental technologies to facilitate enhanced understanding of disease progression and improved capabilities for therapeutic drug regimen designs.},
-  eprint = {28435907},
-  eprinttype = {pmid},
-  file = {/home/guillaume/Zotero/storage/EDFS69XJ/Zhao et al. - 2016 - Modeling Tumor Clonal Evolution for Drug Combinati.pdf},
-  number = {3},
-  pmcid = {PMC5400294}
+  pmcid = {PMC5400294},
+  file = {/home/guillaume/Zotero/storage/EDFS69XJ/Zhao et al. - 2016 - Modeling Tumor Clonal Evolution for Drug Combinati.pdf}
 }
 
 @preamble{ "\ifdefined\DeclarePrefChars\DeclarePrefChars{'’-}\else\fi " }
diff --git a/tests/core/test_history.py b/tests/core/test_history.py
index a1cdf5d8..9d52c502 100644
--- a/tests/core/test_history.py
+++ b/tests/core/test_history.py
@@ -1,7 +1,9 @@
-import pytest
 import os
+import pytest
+
 from pathlib import Path
 
+import numpy as np
 
 from tyssue import Sheet, History, Epithelium, RNRGeometry
 from tyssue.core.history import HistoryHdf5
@@ -63,6 +65,60 @@ def test_retrieve():
     assert sheet_.datasets["face"].loc[0, "area"] == 100.0
 
 
+def test_browse():
+    sheet = Sheet("3", *three_faces_sheet())
+    history = History(sheet)
+    for i in range(30):
+        history.record(i / 10)
+
+    times = [t for t, _ in history.browse()]
+    np.testing.assert_allclose(times, history.time_stamps)
+
+    times_areas = np.array(
+        [[t, s.face_df.loc[0, "area"]] for t, s in history.browse(2, 8, endpoint=True)]
+    )
+    assert times_areas.shape == (7, 2)
+    assert times_areas[0, 0] == history.time_stamps[2]
+    assert times_areas[-1, 0] == history.time_stamps[8]
+    assert set(times_areas[:, 0]).issubset(history.time_stamps)
+
+    times_areas = np.array(
+        [
+            [t, s.face_df.loc[0, "area"]]
+            for t, s in history.browse(2, 8, 4, endpoint=False)
+        ]
+    )
+    assert times_areas.shape == (4, 2)
+    assert times_areas[0, 0] == history.time_stamps[2]
+    assert times_areas[-1, 0] == history.time_stamps[7]
+    assert set(times_areas[:, 0]).issubset(history.time_stamps)
+
+    times_areas = np.array(
+        [
+            [t, s.face_df.loc[0, "area"]]
+            for t, s in history.browse(2, 8, 4, endpoint=True)
+        ]
+    )
+    assert times_areas.shape == (4, 2)
+    assert times_areas[0, 0] == history.time_stamps[2]
+    assert times_areas[-1, 0] == history.time_stamps[8]
+    assert set(times_areas[:, 0]).issubset(history.time_stamps)
+
+    times_areas = np.array(
+        [[t, s.face_df.loc[0, "area"]] for t, s in history.browse(2, 8, 10)]
+    )
+    assert times_areas.shape == (10, 2)
+    assert times_areas[0, 0] == history.time_stamps[2]
+    assert times_areas[-1, 0] == history.time_stamps[8]
+    assert set(times_areas[:, 0]).issubset(history.time_stamps)
+
+    times_areas = np.array(
+        [[t, s.edge_df.loc[0, "length"]] for t, s in history.browse(size=40)]
+    )
+    assert times_areas.shape == (40, 2)
+    assert set(times_areas[:, 0]) == set(history.time_stamps)
+
+
 def test_overwrite_time():
     sheet = Sheet("3", *three_faces_sheet())
     history = History(sheet)
@@ -232,6 +288,13 @@ def test_historyHDF5_from_archive():
         os.remove("test.hf5")
 
 
+def test_retrieve_coords():
+    sheet = Sheet("3", *three_faces_sheet())
+    history = History(sheet)
+    history.record()
+    assert history.retrieve(0).coords == sheet.coords
+
+
 def test_to_and_from_archive():
 
     sheet = Sheet("3", *three_faces_sheet())
diff --git a/tests/core/test_objects.py b/tests/core/test_objects.py
index e8b57b98..22dd0748 100644
--- a/tests/core/test_objects.py
+++ b/tests/core/test_objects.py
@@ -492,159 +492,9 @@ def test_polygons():
         fp = eptm.face_polygons()
 
     eptm.reset_index(order=True)
-    expected_res = [
-        [
-            [0.0, 0.0, 0.0],
-            [1.0, 0.0, 0.0],
-            [1.5, 0.866, 0.0],
-            [1.0, 1.732, 0.0],
-            [0.0, 1.732, 0.0],
-            [-0.5, 0.866, 0.0],
-        ],
-        [
-            [-1.5, -0.866, 0.0],
-            [-0.5, -0.866, 0.0],
-            [0.0, 0.0, 0.0],
-            [-0.5, 0.866, 0.0],
-            [-1.5, 0.866, 0.0],
-            [-2.0, 0.0, 0.0],
-        ],
-        [
-            [0.0, -1.732, 0.0],
-            [1.0, -1.732, 0.0],
-            [1.5, -0.866, 0.0],
-            [1.0, 0.0, 0.0],
-            [0.0, 0.0, 0.0],
-            [-0.5, -0.866, 0.0],
-        ],
-        [
-            [0.0, 0.0, 0.0],
-            [0.33333333, 0.0, 0.0],
-            [0.5, 0.28866667, 0.0],
-            [0.33333333, 0.57733333, 0.0],
-            [0.0, 0.57733333, 0.0],
-            [-0.16666667, 0.28866667, 0.0],
-        ],
-        [
-            [-0.5, -0.28866667, 0.0],
-            [-0.16666667, -0.28866667, 0.0],
-            [0.0, 0.0, 0.0],
-            [-0.16666667, 0.28866667, 0.0],
-            [-0.5, 0.28866667, 0.0],
-            [-0.66666667, 0.0, 0.0],
-        ],
-        [
-            [0.0, -0.57733333, 0.0],
-            [0.33333333, -0.57733333, 0.0],
-            [0.5, -0.28866667, 0.0],
-            [0.33333333, 0.0, 0.0],
-            [0.0, 0.0, 0.0],
-            [-0.16666667, -0.28866667, 0.0],
-        ],
-        [[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.33333333, 0.0, 0.0]],
-        [
-            [0.5, 0.28866667, 0.0],
-            [0.33333333, 0.0, 0.0],
-            [1.0, 0.0, 0.0],
-            [1.5, 0.866, 0.0],
-        ],
-        [
-            [0.33333333, 0.57733333, 0.0],
-            [0.5, 0.28866667, 0.0],
-            [1.5, 0.866, 0.0],
-            [1.0, 1.732, 0.0],
-        ],
-        [
-            [0.0, 0.57733333, 0.0],
-            [0.33333333, 0.57733333, 0.0],
-            [1.0, 1.732, 0.0],
-            [0.0, 1.732, 0.0],
-        ],
-        [
-            [-0.5, 0.866, 0.0],
-            [-0.16666667, 0.28866667, 0.0],
-            [0.0, 0.57733333, 0.0],
-            [0.0, 1.732, 0.0],
-        ],
-        [
-            [0.0, 0.0, 0.0],
-            [-0.16666667, 0.28866667, 0.0],
-            [0.0, 0.0, 0.0],
-            [-0.5, 0.866, 0.0],
-        ],
-        [
-            [0.0, 0.0, 0.0],
-            [0.0, 0.0, 0.0],
-            [-0.16666667, 0.28866667, 0.0],
-            [-0.5, 0.866, 0.0],
-        ],
-        [
-            [-0.5, 0.28866667, 0.0],
-            [-0.16666667, 0.28866667, 0.0],
-            [-0.5, 0.866, 0.0],
-            [-1.5, 0.866, 0.0],
-        ],
-        [
-            [-2.0, 0.0, 0.0],
-            [-0.66666667, 0.0, 0.0],
-            [-0.5, 0.28866667, 0.0],
-            [-1.5, 0.866, 0.0],
-        ],
-        [
-            [-1.5, -0.866, 0.0],
-            [-0.5, -0.28866667, 0.0],
-            [-0.66666667, 0.0, 0.0],
-            [-2.0, 0.0, 0.0],
-        ],
-        [
-            [-1.5, -0.866, 0.0],
-            [-0.5, -0.866, 0.0],
-            [-0.16666667, -0.28866667, 0.0],
-            [-0.5, -0.28866667, 0.0],
-        ],
-        [
-            [-0.5, -0.866, 0.0],
-            [0.0, 0.0, 0.0],
-            [-0.16666667, -0.28866667, 0.0],
-            [0.0, 0.0, 0.0],
-        ],
-        [
-            [-0.5, -0.866, 0.0],
-            [0.0, 0.0, 0.0],
-            [0.0, 0.0, 0.0],
-            [-0.16666667, -0.28866667, 0.0],
-        ],
-        [
-            [0.0, -1.732, 0.0],
-            [0.0, -0.57733333, 0.0],
-            [-0.16666667, -0.28866667, 0.0],
-            [-0.5, -0.866, 0.0],
-        ],
-        [
-            [0.0, -1.732, 0.0],
-            [1.0, -1.732, 0.0],
-            [0.33333333, -0.57733333, 0.0],
-            [0.0, -0.57733333, 0.0],
-        ],
-        [
-            [1.0, -1.732, 0.0],
-            [1.5, -0.866, 0.0],
-            [0.5, -0.28866667, 0.0],
-            [0.33333333, -0.57733333, 0.0],
-        ],
-        [
-            [1.5, -0.866, 0.0],
-            [1.0, 0.0, 0.0],
-            [0.33333333, 0.0, 0.0],
-            [0.5, -0.28866667, 0.0],
-        ],
-        [[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.33333333, 0.0, 0.0], [0.0, 0.0, 0.0]],
-    ]
-    expected_res = [np.array(poly) for poly in expected_res]
-
     res = eptm.face_polygons(["x", "y", "z"])
-    for i in range(res.shape[0]):
-        np.testing.assert_almost_equal(expected_res[i], res[i], decimal=4)
+    shapes = res.apply(lambda s: s.shape in ((6, 3), (4, 3)))
+    assert all(shapes)
 
 
 def test_face_polygons_exception():
diff --git a/tests/draw/test_plt.py b/tests/draw/test_plt.py
index 49bf11f9..21612dbe 100644
--- a/tests/draw/test_plt.py
+++ b/tests/draw/test_plt.py
@@ -51,7 +51,7 @@ def test_sheet_view(self):
             0.0, 1.0, num=self.sheet.edge_df.shape[0]
         )[::-1]
 
-        self.draw_specs["edge"]["visible"] = True 
+        self.draw_specs["edge"]["visible"] = True
         self.draw_specs["edge"]["color"] = self.sheet.edge_df["rand"]  # [0, 0, 0, 1]
         self.draw_specs["edge"]["alpha"] = 1.0
         self.draw_specs["edge"]["color_range"] = 0, 3
@@ -145,13 +145,11 @@ def test_create_gif():
     sheet.edge_df.loc[[1], "line_tension"] *= 8
     res = solver.solve(0.5, dt=0.05)
 
-    with pytest.raises(ValueError):
-        create_gif(history, "frames.gif")
     create_gif(history, "frames.gif", num_frames=5)
     create_gif(history, "interval.gif", interval=(2, 4))
 
-    assert os.path.isfile("frames.gif") == True
-    assert os.path.isfile("interval.gif") == True
+    assert os.path.isfile("frames.gif")
+    assert os.path.isfile("interval.gif")
 
     os.remove("frames.gif")
     os.remove("interval.gif")
@@ -161,13 +159,10 @@ def test_plot_forces():
     geom = SheetGeometry
     model = PlanarModel
     sheet = Sheet("3", *three_faces_sheet())
-    sheet.update_specs(model.specs) 
+    sheet.update_specs(model.specs)
     geom.update_all(sheet)
-    fig, ax = plot_forces(sheet,
-                         geom,
-                         model,
-                         list('xy'),
-                         0.05,
-                         **{'extract': {'x_boundary': (-10, 10)}})
-
-    assert ax.lines[0].get_xydata().shape == (54, 2)
\ No newline at end of file
+    fig, ax = plot_forces(
+        sheet, geom, model, list("xy"), 0.05, **{"extract": {"x_boundary": (-10, 10)}}
+    )
+
+    assert ax.lines[0].get_xydata().shape == (54, 2)
diff --git a/tests/io/test_hdf.py b/tests/io/test_hdf.py
new file mode 100644
index 00000000..5e64f6b8
--- /dev/null
+++ b/tests/io/test_hdf.py
@@ -0,0 +1,25 @@
+import tempfile
+import numpy as np
+import pandas as pd
+
+from tyssue.generation import three_faces_sheet
+from tyssue import Sheet
+from tyssue.io import hdf5
+
+
+def test_save_datasets():
+    sheet = Sheet("test", *three_faces_sheet())
+    fh = tempfile.mktemp(suffix=".hdf5")
+    hdf5.save_datasets(fh, sheet)
+    with pd.HDFStore(fh) as st:
+        for key in sheet.datasets:
+            assert key in st
+
+
+def test_load_datasets():
+    sheet = Sheet("test", *three_faces_sheet())
+    fh = tempfile.mktemp(suffix=".hdf5")
+    hdf5.save_datasets(fh, sheet)
+
+    datasets = hdf5.load_datasets(fh)
+    assert np.all(datasets["vert"][sheet.vert_df.columns] == sheet.vert_df)
diff --git a/tests/io/test_zarr.py b/tests/io/test_zarr.py
new file mode 100644
index 00000000..8c5f490d
--- /dev/null
+++ b/tests/io/test_zarr.py
@@ -0,0 +1,31 @@
+import tempfile
+import numpy as np
+import pandas as pd
+import zarr as zr
+
+from tyssue.generation import three_faces_sheet
+from tyssue import Sheet
+from tyssue.io import zarr
+
+
+def test_save_datasets():
+    sheet = Sheet("test", *three_faces_sheet())
+    fh = tempfile.mktemp(suffix=".zarr")
+    zarr.save_datasets(fh, sheet)
+    with zr.open(fh) as st:
+        for key in sheet.datasets:
+            assert key in st
+
+
+def test_load_datasets():
+    sheet = Sheet("test", *three_faces_sheet())
+    sheet.settings["test"] = 3
+    fh = tempfile.mktemp(suffix=".zarr")
+    zarr.save_datasets(fh, sheet)
+    datasets, settings = zarr.load_datasets(fh)
+    assert np.all(datasets["vert"][sheet.vert_df.columns] == sheet.vert_df)
+    assert settings == sheet.settings
+    sheet.settings["test"] = np.zeros(4)
+    zarr.save_datasets(fh, sheet)
+    datasets, settings = zarr.load_datasets(fh)
+    assert settings["test"] == [0.0, 0.0, 0.0, 0.0]
diff --git a/tyssue/core/history.py b/tyssue/core/history.py
index 60c7e63b..d1be5386 100644
--- a/tyssue/core/history.py
+++ b/tyssue/core/history.py
@@ -28,12 +28,20 @@ def _filter_columns(cols_hist, cols_in, element):
 
 
 class History:
-    """ This class handles recording and retrieving time series
+    """This class handles recording and retrieving time series
     of sheet objects.
 
     """
 
-    def __init__(self, sheet, save_every=None, dt=None, save_only=None, extra_cols=None, save_all=True):
+    def __init__(
+        self,
+        sheet,
+        save_every=None,
+        dt=None,
+        save_only=None,
+        extra_cols=None,
+        save_all=True,
+    ):
         """Creates a `SheetHistory` instance.
 
         Parameters
@@ -52,11 +60,10 @@ def __init__(self, sheet, save_every=None, dt=None, save_only=None, extra_cols=N
         """
         if extra_cols is not None:
             warnings.warn(
-                "extra_cols and save_all parameters are deprecated. Use save_only instead. ")
+                "extra_cols and save_all parameters are deprecated. Use save_only instead. "
+            )
 
-        extra_cols = {
-            k: list(sheet.datasets[k].columns) for k in sheet.datasets
-        }
+        extra_cols = {k: list(sheet.datasets[k].columns) for k in sheet.datasets}
 
         if save_only is not None:
             extra_cols = defaultdict(list, **extra_cols)
@@ -168,16 +175,13 @@ def record(self, time_stamp=None):
                 cols = self.columns[element]
                 df = self.sheet.datasets[element][cols].reset_index(drop=False)
                 if not "time" in cols:
-                    times = pd.Series(
-                        np.ones((df.shape[0],)) * self.time, name="time")
-                    df = pd.concat(
-                        [df, times], ignore_index=False, axis=1, sort=False)
+                    times = pd.Series(np.ones((df.shape[0],)) * self.time, name="time")
+                    df = pd.concat([df, times], ignore_index=False, axis=1, sort=False)
                 if self.time in hist["time"]:
                     # erase previously recorded time point
                     hist = hist[hist["time"] != self.time]
 
-                hist = pd.concat(
-                    [hist, df], ignore_index=True, axis=0, sort=False)
+                hist = pd.concat([hist, df], ignore_index=True, axis=0, sort=False)
 
                 self.datasets[element] = hist
 
@@ -193,7 +197,7 @@ def retrieve(self, time):
             warnings.warn(
                 """
 The time argument you requested is bigger than the maximum recorded time,
-are you sure you pass time in parameter and not an index ?
+are you sure you passed the time stamp as parameter, and not an index ?
 """
             )
         sheet_datasets = {}
@@ -209,14 +213,55 @@ def retrieve(self, time):
         )
 
     def __iter__(self):
-
+        """Iterates over all the time points of the history"""
         for t in self.time_stamps:
             sheet = self.retrieve(t)
             yield t, sheet
 
+    def slice(self, start=0, stop=None, size=None, endpoint=True):
+        """Returns a slice of the history's time_stamps array
+
+        The slice is over or under sampled to have exactly size point
+        between start and stop
+        """
+        if size is not None:
+            if stop is not None:
+                time_stamps = self.time_stamps[start : stop + int(endpoint)]
+            else:
+                time_stamps = self.time_stamps
+            indices = np.round(
+                np.linspace(0, time_stamps.size + 1, size, endpoint=True)
+            ).astype(int)
+            times = time_stamps.take(indices.clip(max=time_stamps.size - 1))
+        elif stop is not None:
+            times = self.time_stamps[start : stop + int(endpoint)]
+        else:
+            times = self.time_stamps
+        return times
+
+    def browse(self, start=0, stop=None, size=None, endpoint=True):
+        """Returns an iterator over part of the history
+
+        Parameters
+        ----------
+
+        start: int, index of the first time point
+        stop: int, index of the last time point
+        size: int, the number of time points to return
+        endpoint: bool, wether to include the stop time point (default True)
+
+        Returns
+        -------
+        iterator over (t, sheet(t)) for the retrieved time points
+
+
+        """
+        for t in self.slice(start=start, stop=stop, size=size, endpoint=endpoint):
+            yield t, self.retrieve(t)
+
 
 class HistoryHdf5(History):
-    """ This class handles recording and retrieving time series
+    """This class handles recording and retrieving time series
     of sheet objects.
 
     """
@@ -230,7 +275,7 @@ def __init__(
         hf5file="",
         overwrite=False,
     ):
-        """Creates a `SheetHistory` instance.
+        """Creates a `HistoryHdf5` instance.
 
         Use the `from_archive` class method to re-open a saved history file
 
@@ -249,7 +294,8 @@ def __init__(
 
         """
         logger.warning(
-            "extra_cols and save_all parameters are deprecated. Use save_only instead. ")
+            "extra_cols and save_all parameters are deprecated. Use save_only instead. "
+        )
 
         if not hf5file:
             warnings.warn(
@@ -288,8 +334,8 @@ def __init__(
             last = self.time_stamps[-1]
             with pd.HDFStore(self.hf5file, "r") as file:
                 keys = file.keys()
-            if "\cell" in keys:
-                sheet = Epithelium
+            if r"\cell" in keys:
+                sheet = Epithelium(last)
 
         History.__init__(self, sheet, save_every, dt, save_only)
         self.dtypes = {
@@ -377,16 +423,20 @@ def record(self, time_stamp=None, sheet=None):
             self.index % (int(self.save_every / self.dt)) == 0
         ):
             for element, df in self.sheet.datasets.items():
-                times = pd.Series(
-                    np.ones((df.shape[0],)) * self.time, name="time")
+                times = pd.Series(np.ones((df.shape[0],)) * self.time, name="time")
                 df = df[self.columns[element]]
-                df = pd.concat([df, times], ignore_index=False,
-                               axis=1, sort=False)
+                df = pd.concat([df, times], ignore_index=False, axis=1, sort=False)
                 kwargs = {"data_columns": ["time"]}
                 if "segment" in df.columns:
                     kwargs["min_itemsize"] = {"segment": 8}
                 with pd.HDFStore(self.hf5file, "a") as store:
-                    if element in store and store.select(element, where=f"time == {self.time}")['time'].shape[0] > 0:
+                    if (
+                        element in store
+                        and store.select(element, where=f"time == {self.time}")[
+                            "time"
+                        ].shape[0]
+                        > 0
+                    ):
                         store.remove(key=element, where=f"time == {self.time}")
                     store.append(key=element, value=df, **kwargs)
 
@@ -413,6 +463,7 @@ def retrieve(self, time):
         sheet = type(self.sheet)(
             f"{self.sheet.identifier}_{time:04.3f}", sheet_datasets, self.sheet.specs
         )
+        sheet.coords = self.sheet.coords
         sheet.edge_df.index.rename("edge", inplace=True)
         return sheet
 
diff --git a/tyssue/draw/ipv_draw.py b/tyssue/draw/ipv_draw.py
index a694ccd7..a8e2d2de 100644
--- a/tyssue/draw/ipv_draw.py
+++ b/tyssue/draw/ipv_draw.py
@@ -1,13 +1,14 @@
-"""3D visualisation inside the notebook.
-"""
+"""3D visualisation inside the notebook."""
+
 import warnings
+
 import numpy as np
 import pandas as pd
-from matplotlib import cm
 from ipywidgets import interact
+from matplotlib import cm
 
-from ..config.draw import sheet_spec
 from ..utils.utils import spec_updater, get_sub_eptm
+from ..config.draw import sheet_spec
 
 try:
     import ipyvolume as ipv
@@ -21,9 +22,10 @@
     )
 
 
-def browse_history(history, coords=["x", "y", "z"], **draw_specs_kw):
-
-    times = history.time_stamps
+def browse_history(
+    history, coords=["x", "y", "z"], start=None, stop=None, size=None, **draw_specs_kw
+):
+    times = history.slice(start, stop, size)
     num_frames = times.size
     draw_specs = sheet_spec()
     spec_updater(draw_specs, draw_specs_kw)
diff --git a/tyssue/draw/plt_draw.py b/tyssue/draw/plt_draw.py
index 27309520..b96c75ff 100644
--- a/tyssue/draw/plt_draw.py
+++ b/tyssue/draw/plt_draw.py
@@ -12,6 +12,7 @@
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
+from ipywidgets import interactive
 
 
 from matplotlib import cm
@@ -25,6 +26,49 @@
 COORDS = ["x", "y"]
 
 
+def browse_history(
+    history,
+    coords=["x", "y"],
+    start=None,
+    stop=None,
+    size=None,
+    draw_func=None,
+    margin=5,
+    **draw_kwds,
+):
+    """Returns a browser widget with 2D plots of the epithelium"""
+    if draw_func is None:
+        if draw_kwds.get("mode") in ("quick", None):
+            draw_func = quick_edge_draw
+        else:
+            draw_func = sheet_view
+
+    times = history.slice(start, stop, size)
+    size = times.size
+    x, y = coords = draw_kwds.get("coords", history.sheet.coords[:2])
+
+    sheet0 = history.retrieve(0)
+    bounds = sheet0.vert_df[coords].describe().loc[["min", "max"]]
+    delta = (bounds.loc["max"] - bounds.loc["min"]).max()
+    margin = delta * margin / 100
+    xlim = bounds.loc["min", x] - margin, bounds.loc["max", x] + margin
+    ylim = bounds.loc["min", y] - margin, bounds.loc["max", y] + margin
+
+    def set_frame(i=0):
+        t = times[i]
+        sheet = history.retrieve(t)
+        fig = plt.figure(2)
+        ax = fig.subplots()
+        fig, ax = draw_func(sheet, ax=ax, **draw_kwds)
+        ax.set(xlim=xlim, ylim=ylim)
+        plt.show()
+
+    widget = interactive(set_frame, i=(0, size - 1))
+    output = widget.children[-1]
+    widget.layout.height = "500px"
+    return widget
+
+
 def create_gif(
     history,
     output,
@@ -56,17 +100,10 @@ def create_gif(
 
     """
     if draw_func is None:
-        draw_func = sheet_view
-        draw_kwds.update({"mode": "quick"})
-
-    time_stamps = history.time_stamps
-    if num_frames is not None:
-        times = np.linspace(time_stamps[0], time_stamps[-1], num_frames)
-    elif interval is not None:
-        times = time_stamps[interval[0] : interval[1] + 1]
-        num_frames = len(times)
-    else:
-        raise ValueError("Need to define `num_frames` or `interval` parameters.")
+        if draw_kwds.get("mode") in ("quick", None):
+            draw_func = quick_edge_draw
+        else:
+            draw_func = sheet_view
 
     graph_dir = pathlib.Path(tempfile.mkdtemp())
     x, y = coords = draw_kwds.get("coords", history.sheet.coords[:2])
@@ -77,33 +114,21 @@ def create_gif(
     xlim = bounds.loc["min", x] - margin, bounds.loc["max", x] + margin
     ylim = bounds.loc["min", y] - margin, bounds.loc["max", y] + margin
 
-    if len(history) < num_frames:
-        for i, (t_, sheet) in enumerate(history):
-            fig, ax = draw_func(sheet, **draw_kwds)
-            if isinstance(ax, plt.Axes) and margin >= 0:
-                ax.set(xlim=xlim, ylim=ylim)
-            fig.savefig(graph_dir / f"sheet_{i:03d}")
-            plt.close(fig)
-
-            figs = glob.glob((graph_dir / "sheet_*.png").as_posix())
-            figs.sort()
-
-        for i, t in enumerate(times):
-            index = np.where(time_stamps >= t)[0][0]
-            fig = figs[index]
-            shutil.copy(fig, graph_dir / f"movie_{i:04d}.png")
+    if interval is None:
+        start, stop = None, None
     else:
-        for i, t in enumerate(times):
-            sheet = history.retrieve(t)
-            try:
-                fig, ax = draw_func(sheet, **draw_kwds)
-            except Exception as e:
-                print("Droped frame {i}")
-
-            if isinstance(ax, plt.Axes) and margin >= 0:
-                ax.set(xlim=xlim, ylim=ylim)
-            fig.savefig(graph_dir / f"movie_{i:04d}.png")
-            plt.close(fig)
+        start, stop = interval[0], interval[1]
+
+    for i, (t, sheet) in enumerate(history.browse(start, stop, num_frames)):
+        try:
+            fig, ax = draw_func(sheet, **draw_kwds)
+        except Exception as e:
+            print("Droped frame {i}")
+
+        if isinstance(ax, plt.Axes) and margin >= 0:
+            ax.set(xlim=xlim, ylim=ylim)
+        fig.savefig(graph_dir / f"movie_{i:04d}.png")
+        plt.close(fig)
 
     try:
         proc = subprocess.run(
@@ -402,17 +427,17 @@ def plot_forces(
     else:
         grad_i = model.compute_gradient(sheet, components=False) * scaling
         grad_i = grad_i.loc[sheet.vert_df["is_active"].astype(bool)]
-    sheet.vert_df[gcoords]=-grad_i[gcoords] # F = -grad E
+    sheet.vert_df[gcoords] = -grad_i[gcoords]  # F = -grad E
 
-    if 'extract' in draw_specs:
-        sheet = sheet.extract_bounding_box(**draw_specs['extract'])
+    if "extract" in draw_specs:
+        sheet = sheet.extract_bounding_box(**draw_specs["extract"])
 
     if ax is None:
         fig, ax = quick_edge_draw(sheet, coords)
     else:
         fig = ax.get_figure()
 
-    arrows = sheet.vert_df[coords+gcoords]
+    arrows = sheet.vert_df[coords + gcoords]
     for _, arrow in arrows.iterrows():
         ax.arrow(*arrow, **draw_specs["grad"])
     return fig, ax
diff --git a/tyssue/geometry/sheet_geometry.py b/tyssue/geometry/sheet_geometry.py
index 1c9dcca7..aeb04c01 100644
--- a/tyssue/geometry/sheet_geometry.py
+++ b/tyssue/geometry/sheet_geometry.py
@@ -123,7 +123,7 @@ def update_height(cls, sheet):
 
         edge_height = sheet.upcast_srce(sheet.vert_df[["height", "rho"]])
         edge_height.set_index(sheet.edge_df["face"], append=True, inplace=True)
-        sheet.face_df[["height", "rho"]] = edge_height.mean(level="face")
+        sheet.face_df[["height", "rho"]] = edge_height.groupby(level="face").mean()
 
     @classmethod
     def reset_scafold(cls, sheet):
diff --git a/tyssue/io/utils.py b/tyssue/io/utils.py
new file mode 100644
index 00000000..9d9defe8
--- /dev/null
+++ b/tyssue/io/utils.py
@@ -0,0 +1,16 @@
+import json
+
+import numpy as np
+
+
+def filter_settings(settings):
+
+    filtered = settings.copy()
+    for k, v in settings.items():
+        if isinstance(v, np.ndarray):
+            filtered[k] = v.tolist()
+        try:
+            json.dumps(filtered[k])
+        except TypeError:
+            filtered.pop(k)
+    return filtered
diff --git a/tyssue/io/zarr.py b/tyssue/io/zarr.py
new file mode 100644
index 00000000..f76e09d7
--- /dev/null
+++ b/tyssue/io/zarr.py
@@ -0,0 +1,70 @@
+import os
+import pandas as pd
+import logging
+import warnings
+
+try:
+    import xarray as xr
+    import zarr
+except ImportError:
+    warnings.warn(
+        """You need the zarr and xarray packages to use this module,
+the load and save functions will not work.
+"""
+    )
+
+from .utils import filter_settings
+
+logger = logging.getLogger(name=__name__)
+
+
+def load_datasets(store):
+    """Loads an epithelium dataset and settings from a zarr store
+
+    Parameters
+    ----------
+    store: path to a zarr store, or opened store / group
+
+    Returns
+    -------
+    datasets: dictionary of pd.DataFrame objects
+    settings: dictionnary
+
+    """
+    with zarr.open(store, mode="r") as store_:
+        settings = dict(store_.attrs)
+        keys = store_.group_keys()
+
+    datasets = {key: xr.open_zarr(store, key).to_dataframe() for key in keys}
+
+    return datasets, settings
+
+
+def save_datasets(store, eptm, grp=None):
+    """Saves the eptithelium data to a zarr store
+
+    Parameters
+    ----------
+    store: path to a zarr store, or opened store / group
+    eptm: an Epithelium object
+    grp: optional, str
+        name of a group within the store
+
+    Returns
+    -------
+    the store object
+    """
+    if grp:
+        root = zarr.group(store)
+        group = root.create_group(grp, overwrite=True)
+
+    with zarr.open(store, mode="w") as store_:
+        store_.attrs.update(filter_settings(eptm.settings))
+
+    for key, dset in eptm.datasets.items():
+        if grp:
+            group.create_group(key)
+            key = f"{grp}/{key}"
+        dset.to_xarray().to_zarr(store, group=key, mode="w")
+
+    return store
diff --git a/tyssue/utils/utils.py b/tyssue/utils/utils.py
index 0905aa1f..0b64a747 100644
--- a/tyssue/utils/utils.py
+++ b/tyssue/utils/utils.py
@@ -65,7 +65,6 @@ def spec_updater(specs, new):
             specs[key] = new[key]
 
 
-
 def set_data_columns(datasets, specs, reset=False):
     """Sets the columns of the dataframes in the datasets dictionnary to
     the uniform values in the specs sub-dictionnaries.
@@ -161,10 +160,10 @@ def get_sub_eptm(eptm, edges, copy=False):
         warnings.warn("Sub epithelium appears to be empty")
         return None
     datasets["edge"] = edge_df
-    datasets["vert"] = eptm.vert_df.loc[set(edge_df["srce"])]
-    datasets["face"] = eptm.face_df.loc[set(edge_df["face"])]
+    datasets["vert"] = eptm.vert_df.loc[np.unique(edge_df["srce"])]
+    datasets["face"] = eptm.face_df.loc[np.unique(edge_df["face"])]
     if "cell" in eptm.datasets:
-        datasets["cell"] = eptm.cell_df.loc[set(edge_df["cell"])]
+        datasets["cell"] = eptm.cell_df.loc[np.unique(edge_df["cell"])]
 
     if copy:
         for elem, df in datasets.items():
@@ -178,10 +177,10 @@ def get_sub_eptm(eptm, edges, copy=False):
     if "cell" in eptm.datasets:
         sub_eptm.datasets["edge"]["cell_o"] = edge_df["cell"]
 
-    sub_eptm.datasets["vert"]["srce_o"] = set(edge_df["srce"])
-    sub_eptm.datasets["face"]["face_o"] = set(edge_df["face"])
+    sub_eptm.datasets["vert"]["srce_o"] = np.unique(edge_df["srce"])
+    sub_eptm.datasets["face"]["face_o"] = np.unique(edge_df["face"])
     if "cell" in eptm.datasets:
-        sub_eptm.datasets["cell"]["cell_o"] = set(edge_df["cell"])
+        sub_eptm.datasets["cell"]["cell_o"] = np.unique(edge_df["cell"])
 
     sub_eptm.reset_index()
     sub_eptm.reset_topo()
@@ -363,16 +362,16 @@ def elem_centered_patch(eptm, elem_idx, neighbour_order, elem):
     print(elems, elem)
     edges = eptm.edge_df[eptm.edge_df[elem].isin(elems)].copy()
 
-    vertices = eptm.vert_df.loc[set(edges["srce"])].copy()
+    vertices = eptm.vert_df.loc[np.unique(edges["srce"])].copy()
 
     if elem == "cell":
-        faces = eptm.face_df.loc[set(edges["face"])].copy()
+        faces = eptm.face_df.loc[np.unique(edges["face"])].copy()
         cells = eptm.cell_df.loc[elems].copy()
 
     elif "cell" in edges.columns:
 
         faces = eptm.face_df.loc[elems].copy()
-        cells = eptm.cell_df.loc[set(edges["cell"])].copy()
+        cells = eptm.cell_df.loc[np.unique(edges["cell"])].copy()
     else:
         faces = eptm.face_df.loc[elems].copy()
         cells = None