Run FBA, FVA, and knockout analyses on SBML metabolic models.
npx skills add https://github.com/shushuzn/Rairos --skill cobrapy-shushuzn
Please help me install this Agent Skill. Skill: cobrapy Source: https://github.com/shushuzn/Rairos/tree/main/skills/cobrapy Command: npx skills add https://github.com/shushuzn/Rairos --skill cobrapy-shushuzn
It solves the problem of analyzing constraint-based metabolic models without manually running repetitive, error-prone workflows for growth prediction, gene knockouts, and flux behavior.
Use the cobrapy skill to run flux balance analysis on an uploaded SBML model and report the predicted objective value and status.
High-intent search queries and answers about installing and using this skill.
To run flux balance analysis, load a genome-scale metabolic reconstruction in SBML, JSON, or YAML format and optimize reaction bounds to predict objective-driven phenotypes such as growth rates and ATP maintenance.
Flux Variability Analysis (FVA) quantifies minimum and maximum flux ranges for reactions, including loopless options, to identify which metabolic reactions are flexible versus tightly constrained within the network.
Yes, you can perform single and double gene or reaction deletions to identify essential components in a metabolic network, generating knockout impact tables that highlight candidate targets for metabolic engineering.
Yes, the workflow configures growth media and computes minimal media compositions for specified performance targets, while production envelope calculations map phenotype phase planes for product and reaction tradeoffs.
You can repair infeasible models by using gapfilling to add candidate reactions, or construct metabolic models from scratch to satisfy stoichiometric constraints and enable successful optimization.
You need a compatible genome-scale reconstruction, but you can load models from SBML, JSON, or YAML formats to explore the feasible flux space using flux sampling techniques.
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