COBRApy is a package for constraint-based modeling of metabolic networks.
python Parameter EStimation TOolbox
💎 An easy-to-use workflow for generating context specific genome-scale metabolic models and predicting metabolic interactions within microbial communities directly from metagenomic data
INDRA (Integrated Network and Dynamical Reasoning Assembler) is an automated model assembly system interfacing with NLP systems and databases to collect knowledge, and through a process of assembly, produce causal graphs and dynamical models.
🌶️ An ecosystem in Python for working with the Biological Expression Language (BEL)
A Python framework for structural systems biology
Python package to study microbial communities using metabolic modeling.
An integrated visual environment for metabolic modeling with common methods such as FBA, FVA and Elementary Flux Modes, and advanced features such as thermodynamic methods, extended Minimal Cut Sets, OptKnock, RobustKnock, OptCouple and more!
A python library for metabolic networks sampling and analysis
StrainDesign is a python package for the computational design of metabolic networks and based on COBRApy
StochPy is a versatile stochastic modeling package which is designed for stochastic simulation of molecular control networks
A COBRApy extension for genome-scale models of metabolism and expression (ME-models)
HGTphyloDetect is a versatile toolbox to identify horizontal gene transfer (HGT) events combined with phylogenetic analysis.
A collection of mathematical models with experimental data in the PEtab format as benchmark problems in order to evaluate new and existing methodologies for data-based modelling
A Python Framework for Modeling and Analysis of Signaling Systems
Code repository for the book "Python for the Life Sciences" by Lancaster & Webster
A Python package for drug discovery by analyzing causal paths on multiscale networks
Enzyme-constrained genome-scale models in python
An implementation of genome-scale model reconstruction using Cost Optimization Reaction Dependency Assessment by Schultz et. al
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