List of publications using AMICI. Total number is 35.
If you applied AMICI in your work and your publication is missing, please let us know via a new Github issue.
Dharmarajan, Lekshmi, Hans-Michael Kaltenbach, Fabian Rudolf, and Joerg Stelling. 2019. “A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics.” Cell Systems 8 (1). Elsevier: 15–26.e11. https://doi.org/10.1016/j.cels.2018.12.007.
Fischer, David S., Anna K. Fiedler, Eric Kernfeld, Ryan M. J. Genga, Aimée Bastidas-Ponce, Mostafa Bakhti, Heiko Lickert, Jan Hasenauer, Rene Maehr, and Fabian J. Theis. 2019. “Inferring Population Dynamics from Single-Cell Rna-Sequencing Time Series Data.” Nature Biotechnology 37: 461–68. https://doi.org/10.1038/s41587-019-0088-0.
Gregg, Robert W, Saumendra N Sarkar, and Jason E Shoemaker. 2019. “Mathematical Modeling of the cGAS Pathway Reveals Robustness of Dna Sensing to Trex1 Feedback.” Journal of Theoretical Biology 462 (February): 148–57. https://doi.org/10.1016/j.jtbi.2018.11.001.
Kapfer, Eva-Maria, Paul Stapor, and Jan Hasenauer. 2019. “Challenges in the Calibration of Large-Scale Ordinary Differential Equation Models.” Accepted for Publication in Proc. Of the Foundations of Syst. Biol. In Engin. (FOSBE).
Lines, Glenn Terje, Lukasz Paszkowski, Leonard Schmiester, Daniel Weindl, Paul Stapor, and Jan Hasenauer. 2019. “Efficient Computation of Steady States in Large-Scale Ode Models of Biochemical Reaction Networks.” Accepted for Publication in Proc. Of the Foundations of Syst. Biol. In Engin. (FOSBE).
Nousiainen, Kari, Jukka Intosalmi, and Harri Lähdesmäki. 2019. “A Mathematical Model for Enhancer Activation Kinetics During Cell Differentiation.” In Algorithms for Computational Biology, edited by Ian Holmes, Carlos Martín-Vide, and Miguel A. Vega-Rodríguez, 191–202. Cham: Springer International Publishing.
Pedretscher, B., B. Kaltenbacher, and O. Pfeiler. 2019. “Parameter Identification and Uncertainty Quantification in Stochastic State Space Models and Its Application to Texture Analysis.” Applied Numerical Mathematics 146: 38–54. https://doi.org/https://doi.org/10.1016/j.apnum.2019.06.020.
Pitt, Jake Alan, and Julio R Banga. 2019. “Parameter Estimation in Models of Biological Oscillators: An Automated Regularised Estimation Approach.” BMC Bioinformatics 20 (1): 82. https://doi.org/10.1186/s12859-019-2630-y.
Schmiester, Leonard, Yannik Schälte, Fabian Fröhlich, Jan Hasenauer, and Daniel Weindl. 2019. “Efficient parameterization of large-scale dynamic models based on relative measurements.” Bioinformatics, July. https://doi.org/10.1093/bioinformatics/btz581.
Schmiester, Leonard, Daniel Weindl, and Jan Hasenauer. 2019. “Statistical Inference of Mechanistic Models from Qualitative Data Using an Efficient Optimal Scaling Approach.” bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/848648.
Stapor, Paul, Leonard Schmiester, Christoph Wierling, Bodo Lange, Daniel Weindl, and Jan Hasenauer. 2019. “Mini-Batch Optimization Enables Training of Ode Models on Large-Scale Datasets.” bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/859884.
Villaverde, Alejandro F., Elba Raimúndez, Jan Hasenauer, and Julio R. Banga. 2019. “A Comparison of Methods for Quantifying Prediction Uncertainty in Systems Biology.” Accepted for Publication in Proc. Of the Foundations of Syst. Biol. In Engin. (FOSBE).
Wang, Dantong, Paul Stapor, and Jan Hasenauer. 2019. “Dirac Mixture Distributions for the Approximation of Mixed Effects Models.” Accepted for Publication in Proc. Of the Foundations of Syst. Biol. In Engin. (FOSBE).
Ballnus, Benjamin, Steffen Schaper, Fabian J Theis, and Jan Hasenauer. 2018. “Bayesian Parameter Estimation for Biochemical Reaction Networks Using Region-Based Adaptive Parallel Tempering.” Bioinformatics 34 (13): i494–i501. https://doi.org/10.1093/bioinformatics/bty229.
Bast, Lisa, Filippo Calzolari, Michael Strasser, Jan Hasenauer, Fabian J. Theis, Jovica Ninkovic, and Carsten Marr. 2018. “Subtle Changes in Clonal Dynamics Underlie the Age-Related Decline in Neurogenesis.” Cell Reports.
Fröhlich, Fabian, Thomas Kessler, Daniel Weindl, Alexey Shadrin, Leonard Schmiester, Hendrik Hache, Artur Muradyan, et al. 2018. “Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model.” Cell Systems 7 (6). Elsevier: 567–579.e6. https://doi.org/10.1016/j.cels.2018.10.013.
Fröhlich, Fabian, Anita Reiser, Laura Fink, Daniel Woschée, Thomas Ligon, Fabian Joachim Theis, Joachim Oskar Rädler, and Jan Hasenauer. 2018. “Multi-Experiment Nonlinear Mixed Effect Modeling of Single-Cell Translation Kinetics After Transfection.” Npj Systems Biology and Applications 5 (1): 1. https://doi.org/10.1038/s41540-018-0079-7.
Kaltenbacher, Barbara, and Barbara Pedretscher. 2018. “Parameter Estimation in Sdes via the Fokker–Planck Equation: Likelihood Function and Adjoint Based Gradient Computation.” Journal of Mathematical Analysis and Applications 465 (2): 872–84. https://doi.org/https://doi.org/10.1016/j.jmaa.2018.05.048.
Loos, Carolin, Sabrina Krause, and Jan Hasenauer. 2018. “Hierarchical Optimization for the Efficient Parametrization of ODE Models.” Bioinformatics 34 (24): 4266–73. https://doi.org/10.1093/bioinformatics/bty514.
Loos, Carolin, Katharina Moeller, Fabian Fröhlich, Tim Hucho, and Jan Hasenauer. 2018. “A Hierarchical, Data-Driven Approach to Modeling Single-Cell Populations Predicts Latent Causes of Cell-to-Cell Variability.” Cell Systems 6 (5). Elsevier: 593–603. https://doi.org/10.1016/j.cels.2018.04.008.
Schälte, Y., P. Stapor, and J. Hasenauer. 2018. “Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology.” FAC-PapersOnLine 51 (19): 98–101.
Stapor, Paul, Fabian Fröhlich, and Jan Hasenauer. 2018. “Optimization and Profile Calculation of ODE Models Using Second Order Adjoint Sensitivity Analysis.” Bioinformatics 34 (13). Oxford University Press: i151–i159.
Villaverde, Alejandro F., Fabian Froehlich, Daniel Weindl, Jan Hasenauer, and Julio R Banga. 2018. “Benchmarking Optimization Methods for Parameter Estimation in Large Kinetic Models.” Bioinformatics, bty736.
Ballnus, B., S. Hug, K. Hatz, L. Görlitz, J. Hasenauer, and F. J. Theis. 2017. “Comprehensive Benchmarking of Markov Chain Monte Carlo Methods for Dynamical Systems.” BMC Syst. Biol. 11 (63). https://doi.org/10.1186/s12918-017-0433-1.
Fröhlich, F., B. Kaltenbacher, F. J. Theis, and J. Hasenauer. 2017. “Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks.” PLoS Comput. Biol. 13 (1): e1005331. https://doi.org/10.1371/journal.pcbi.1005331.
Fröhlich, F., F. J. Theis, J. O. Rädler, and J. Hasenauer. 2017. “Parameter Estimation for Dynamical Systems with Discrete Events and Logical Operations.” Bioinformatics 33 (7): 1049–56. https://doi.org/10.1093/bioinformatics/btw764.
Kazeroonian, A., F. J. Theis, and J. Hasenauer. 2017. “A Scalable Moment-Closure Approximation for Large-Scale Biochemical Reaction Networks.” Bioinformatics 33 (14): i293–i300. https://doi.org/10.1093/bioinformatics/btx249.
Maier, C., C. Loos, and J. Hasenauer. 2017. “Robust Parameter Estimation for Dynamical Systems from Outlier-Corrupted Data.” Bioinformatics 33 (5): 718–25. https://doi.org/10.1093/bioinformatics/btw703.
Boiger, R., J. Hasenauer, S. Hross, and B. Kaltenbacher. 2016. “Integration Based Profile Likelihood Calculation for PDE Constrained Parameter Estimation Problems.” Inverse Prob. 32 (12): 125009. https://doi.org/10.1088/0266-5611/32/12/125009.
Fiedler, A., S. Raeth, F. J. Theis, A. Hausser, and J. Hasenauer. 2016. “Tailored Parameter Optimization Methods for Ordinary Differential Equation Models with Steady-State Constraints.” BMC Syst. Biol. 10 (80). https://doi.org/10.1186/s12918-016-0319-7.
Fröhlich, F., P. Thomas, A. Kazeroonian, F. J. Theis, R. Grima, and J. Hasenauer. 2016. “Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion.” PLoS Comput. Biol. 12 (7): e1005030. https://doi.org/10.1371/journal.pcbi.1005030.
Hross, S., A. Fiedler, F. J. Theis, and J. Hasenauer. 2016. “Quantitative Comparison of Competing PDE Models for Pom1p Dynamics in Fission Yeast.” In Proc. 6th IFAC Conf. Found. Syst. Biol. Eng., edited by R. Findeisen, E. Bullinger, E. Balsa-Canto, and K. Bernaerts, 49:264–69. 26. IFAC-PapersOnLine. https://doi.org/10.1016/j.ifacol.2016.12.136.
Kazeroonian, A., F. Fröhlich, A. Raue, F. J. Theis, and J. Hasenauer. 2016. “CERENA: Chemical REaction Network Analyzer – A Toolbox for the Simulation and Analysis of Stochastic Chemical Kinetics.” PLoS ONE 11 (1): e0146732. https://doi.org/10.1371/journal.pone.0146732.
Loos, C., A. Fiedler, and J. Hasenauer. 2016. “Parameter Estimation for Reaction Rate Equation Constrained Mixture Models.” In Proc. 13th Int. Conf. Comp. Meth. Syst. Biol., edited by E. Bartocci, P. Lio, and N. Paoletti, 186–200. Lecture Notes in Bioinformatics. Springer International Publishing. https://doi.org/10.1007/978-3-319-45177-0.
Loos, C., C. Marr, F. J. Theis, and J. Hasenauer. 2015. “Computational Methods in Systems Biology.” In, edited by O. Roux and J. Bourdon, 9308:52–63. Lecture Notes in Computer Science. Springer International Publishing.