Perform constraint-based modeling and analyses of metabolic networks in Python.
npx skills add https://github.com/JosephWoodall/noosphere --skill cobrapy-josephwoodall
Please help me install this Agent Skill. Skill: cobrapy Source: https://github.com/JosephWoodall/noosphere/tree/main/.agent/skills/cobrapy Command: npx skills add https://github.com/JosephWoodall/noosphere --skill cobrapy-josephwoodall
COBRApy provides a Python-based framework to perform constraint-based modeling and analysis of metabolic networks, enabling researchers to predict growth, test gene knockouts, and explore pathway capabilities in genome-scale models.
Load a COBRApy model and run FBA to predict growth and flux distributions.
High-intent search queries and answers about installing and using this skill.
Flux Balance Analysis (FBA) predicts microbial growth and flux distributions by loading a genome-scale metabolic model and applying constraint-based modeling to calculate optimal fluxes. COBRApy supports FBA and pFBA for analyzing metabolic networks.
Flux Variability Analysis (FVA) explores the range of possible flux values in metabolic networks while maintaining a defined objective. It helps researchers understand pathway flexibility and identify blocked reactions in genome-scale models.
Gene knockout and double knockout analysis identifies essential genes by systematically removing them from a metabolic model and evaluating growth. COBRApy enables these simulations to predict microbial viability under genetic perturbations.
SBML is a supported format for loading and building metabolic models, though other formats are also supported. Model handling requires Python, COBRApy, and standard scientific libraries to execute constraint-based modeling workflows.
Production envelope and phenotype phase plane analyses design production strains by evaluating metabolic capabilities across varying conditions. Flux sampling further characterizes pathway usage distributions in genome-scale models.
Flux sampling characterizes the full distribution of feasible metabolic states, whereas FBA identifies a single optimal solution. Both are supported for constraint-based modeling of genome-scale metabolic networks.
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