MeneTools are Python (3.6 and higher) tools to explore the producibility potential in a metabolic network using the network expansion algorithm. The MeneTools can:
- assess whether targets are producible starting from nutrients (
Mene check) - get all compounds that are producible starting from nutrients (
Mene scope) - get all reactions that are activable from nutrients (
Mene acti) - get production paths of specific compounds (
Mene path) - obtain compounds that if added to the nutrients, would ensure the producibility of targets (
Mene cof) - identify metabolic deadends, i.e. metabolites that act as reactants of reactions but never as products, or metabolites that act as products of reactions but never as reactants. This is a purely structural analysis (
Mene dead) - identify exchanged compounds in metabolic networks based on exchange reactions, i.e. outputs of reactions that do not have reactants (
Mene seed). - identify the number of steps needed either to produce targets or all producible compounds (computed with menescope) starting from nutrients (
Mene scope_inc).
MeneTools follows the producibility in metabolic networks as defined by the network expansion algorithm. Mainly, two rules are followed:
- a recursive rule: the products of a reactions are producible if all reactants of this reaction are themselves producible
- an initiation rule: producibility is initiated by the presence of nutrients, called seeds.
A metabolite that is producible from a set of nutrients is described as being "in the scope of the seeds". The computation is made using logic solvers (Answer Set Programming). The present modelling ignores the stoichiometry of reactions (2A + B --> C is considered equivalent to A + B --> C), and is therefore suited to non-curated or draft metabolic networks.
Menescope computes the set of metabolites that are producible from a set of nutrients: its provides the scope of the seeds in a metabolic network. Menecheck assesses whether a list of target metabolites are producible from the nutrients in a metabolic model, following the network expansion algorithm.
Meneacti has a similar functioning as Menescope but focuses on activable reactions. It computes all reactions that can be activated from the nutritional environment (i.e. whose sets of reactants are in the scope).
Menepath (beta version) proposes a pathway (set of reactions) that explains the producibility of a given target metabolite from the seeds. The objective if to find a path of reactions for metabolites of interest.
Menecof (beta version) proposes compounds that would unblock the producibility of taregt metabolites if they were producible. It can therefore identify missing cofactor for the modelling or compounds that would need to be added to the growth medium of the modelled organism. Menedead (beta version) identifies deadends in a metabolic network. Deadends are compounds which are not produced or consumed (meaning that they are not reactant or product of a reaction).
Meneseed (beta version) returns metabolites produced by reaction without reactants. Menescope_inc (beta version) computes the number of steps needed by the expansion algorithm to reach either (1) targets or (2) all producible compounds.
If you use MeneTools, please cite:
Belcour* A, Frioux* C, Aite M, Bretaudeau A, Hildebrand F, Siegel A. Metage2Metabo, microbiota-scale metabolic complementarity for the identification of key species. eLife 2020;9:e61968 https://doi.org/10.7554/eLife.61968.
Aite* M, Chevallier* M, Frioux* C, Trottier* C, Got J, Cortés MP, et al. Traceability, reproducibility and wiki-exploration for “à-la-carte” reconstructions of genome-scale metabolic models. PLoS Comput Biol 2018;14:e1006146. https://doi.org/10.1371/journal.pcbi.1006146.
Requires Python >= 3.6
Required package (starting from version 2.0 of the package):
ClyngororClyngor_with_clingothat includes the solvers
python setup.py install
or
pip install menetools
usage: mene [-h] [-v] {acti,check,cof,dead,path,scope,seed,scope_inc} ...
Explore the producibility potential in a metabolic network using the network
expansion algorithm. For specific help on each subcommand use: mene {cmd}
--help
optional arguments:
-h, --help show this help message and exit
-v, --version show program's version number and exit
subcommands:
valid subcommands:
{acti,check,cof,dead,path,scope,seed,scope_inc}
acti Get activable reactions in a metabolic network,
starting from seeds.
check Check the producibility of targets from seeds in a
metabolic network.
cof Propose cofactor whose producibility could unblock the
producibility of targets.
dead Identification of dead-end reactions (reactions whose
reactants are never consumed or whose reactants are
never produced) in metabolic networks.
path Get production pathways of targets in metabolic
networks, started from seeds.
scope Get producible metabolites in a metabolic network,
starting from seeds.
seed Get metabolites from exchange reactions in a metabolic network.
scope_inc Get the steps of the network expansion to produce
either targets or all the producible compounds,
starting from seeds.
Requires Clingo and clyngor package: "pip install clyngor clyngor-with-clingo"
Menecheck is a python3 tool to get the topologically producibility status of target compounds
usage: mene check [-h] -d DRAFTNET -s SEEDS [-t TARGETS] [--output OUTPUT]
optional arguments:
-h, --help show this help message and exit
-d DRAFTNET, --draftnet DRAFTNET
metabolic network in SBML format
-s SEEDS, --seeds SEEDS
seeds in SBML format
-t TARGETS, --targets TARGETS
targets in SBML format
--output OUTPUT output file for instance
from menetools import run_menecheck
model = run_menecheck(draft_sbml='required',seeds_sbml='required',targets_sbml='required',output='optional')Menescope is a python3 tool to get the topologically reachable compounds from seeds in a metabolic network.
usage: mene scope [-h] -d DRAFTNET -s SEEDS [--output OUTPUT]
optional arguments:
-h, --help show this help message and exit
-d DRAFTNET, --draftnet DRAFTNET
metabolic network in SBML format
-s SEEDS, --seeds SEEDS
seeds in SBML format
--output OUTPUT output file for instance
from menetools import run_menescope
model = run_menescope(draft_sbml='required',seeds_sbml='required',output='optional')The outputs for menecope are
- As a control: seeds that were provided but do not appear in the metabolic network.
- The scope i.e. compounds that are in the seeds and those that are produced from the seeds
- Two subsets to distinguish seed status
- those that can be produced by the metabolic network. For such seeds, the organism would have the metabolic capability to renew the production in addition to what was available in the environement.
- those that cannot be produced by the metabolic network. For such seeds, there is no alternative other than relying on the environmental composition.
Meneacti is a python3 tool that retrieve the activable reactions from seeds in a metabolic network.
usage: mene acti [-h] -d DRAFTNET -s SEEDS [--output OUTPUT]
optional arguments:
-h, --help show this help message and exit
-d DRAFTNET, --draftnet DRAFTNET
metabolic network in SBML format
-s SEEDS, --seeds SEEDS
seeds in SBML format
--output OUTPUT output file for instance
from menetools import run_menescope
model = run_mene_acti(draft_sbml='required',seeds_sbml='required',output='optional')Menepath is a python3 tool to get the topologically essential reactions with respects to individual targets in metabolic networks.
usage: mene path [-h] -d DRAFTNET -s SEEDS [-t TARGETS] [--enumerate] [--min]
[--output OUTPUT]
optional arguments:
-h, --help show this help message and exit
-d DRAFTNET, --draftnet DRAFTNET
metabolic network in SBML format
-s SEEDS, --seeds SEEDS
seeds in SBML format
-t TARGETS, --targets TARGETS
targets in SBML format
--enumerate enumerates all cofactors solutions
--min call this option to obtain minimal-size paths
--output OUTPUT output file for instance
from menetools import run_menepath
model = run_menepath(draft_sbml='required',seeds_sbml='required',targets_sbml='required',min_size='optional',enumeration='optional',output='optional')Menecof is a python3 tool to get the minimal set of cofactors that enables to maximize the number f producible targets. Study of the metabolic network is made topologically using reachable compounds from seeds.
usage: mene cof [-h] -d DRAFTNET -s SEEDS [-t TARGETS] [-c COFACTORS]
[--weight] [--suffix SUFFIX] [--enumerate] [--output OUTPUT]
optional arguments:
-h, --help show this help message and exit
-d DRAFTNET, --draftnet DRAFTNET
metabolic network in SBML format
-s SEEDS, --seeds SEEDS
seeds in SBML format
-t TARGETS, --targets TARGETS
targets in SBML format
-c COFACTORS, --cofactors COFACTORS
cofactors, in one-per-line text file format
--weight call this option if cofactors are weighted according
to their occurrence frequency in database. If so,
cofactors file must be tabulated with per line
compound' 'occurrence
--suffix SUFFIX suffix to be added to the compounds of the database.
It can be the suffix for the cytosolic compartment or
external one. Cytosolic one is prefered to ensure the
impact of the added cofactors. Default = None
--enumerate enumerates all cofactors solutions
--output OUTPUT output file for instance
from menetools import run_menecof
model = run_menecof(draft_sbml='required',seeds_sbml='required',targets_sbml='required',cofactors_txt='optional',weights='optional',suffix='optional',enumeration='optional',output='optional')Menedead is a python3 tool to identify dead ends in a metabolic network, by searching non produced and non consumed metabolites.
usage: mene dead [-h] -d DRAFTNET [--output OUTPUT]
optional arguments:
-h, --help show this help message and exit
-d DRAFTNET, --draftnet DRAFTNET
metabolic network in SBML format
--output OUTPUT json output file
from menetools import run_menedead
model = run_menedead(draft_sbml='required',output='optional')Meneseed identifies metabolites produced by exchange reactions in a metabolic network. It does not consider the flux value of these exchange reactions, it solely considers the structure of the network.
e.g. Given the reactions <-> A, -> B, <- C, A and B would be reported by Meneseed.
usage: mene seed [-h] -d DRAFTNET [--output OUTPUT]
optional arguments:
-h, --help show this help message and exit
-d DRAFTNET, --draftnet DRAFTNET
metabolic network in SBML format
--output OUTPUT json output file
from menetools import run_meneseed
model = run_meneseed(draft_sbml='required',output='optional')Menescope_inc identifies the number of steps needed by the expansion algorithm to reach either (1) specific targets or (2) all producible compounds. The results are for each step, the producible metabolites.
usage: mene scope_inc [-h] -d DRAFTNET -s SEEDS [-t TARGETS] [--output OUTPUT]
optional arguments:
-h, --help show this help message and exit
-d DRAFTNET, --draftnet DRAFTNET
metabolic network in SBML format
-s SEEDS, --seeds SEEDS
seeds in SBML format
-t TARGETS, --targets TARGETS
targets in SBML format
--output OUTPUT json output file
from menetools import run_menescope_inc
model = run_menescope_inc(draft_sbml='required',seeds_sbml='required',targets_sbml='optional',output='optional')Many thanks to