lib-cobrapy

Perform constraint-based metabolic modeling and analysis with COBRApy.

Updated Mar 13, 2026
One-click install
npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-cobrapy
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: lib-cobrapy
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/lib-cobrapy
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-cobrapy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill empowers users to analyze and simulate complex biological metabolic networks, enabling predictions about cellular behavior and guiding metabolic engineering efforts.

Core Features & Use Cases

  • Model Loading & Management: Load, save, and manipulate metabolic models in various formats (SBML, JSON, YAML).
  • Simulation & Analysis: Perform Flux Balance Analysis (FBA), Flux Variability Analysis (FVA), gene/reaction knockouts, and flux sampling.
  • Use Case: A researcher can use this Skill to simulate the effect of deleting a specific gene on a bacterium's growth rate or to identify the optimal conditions for producing a valuable metabolite.

Quick Start

Use the lib-cobrapy skill to load the 'ecoli' model and predict its growth rate.

Frequently Asked Questions about lib-cobrapy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform flux balance analysis on a metabolic model?

Flux balance analysis (FBA) is supported natively to simulate metabolic networks and predict cellular phenotypes like growth rates. You can load a model from SBML, JSON, or YAML, then run FBA to calculate optimal reaction fluxes.

How do I simulate gene knockouts to predict their effect on cellular growth?

Gene knockouts can be simulated to predict their effect on cellular growth rates by constraining the associated reaction flux to zero. This identifies essential genes and guides metabolic engineering targets.

Can I load and save metabolic models in SBML format?

Yes, metabolic models can be loaded and saved in SBML, JSON, and YAML formats. This allows you to manipulate existing constraint-based models or build new ones from scratch for flux analysis.

What is flux variability analysis and when do I need it for metabolic modeling?

Flux variability analysis (FVA) determines the range of possible fluxes for each reaction while maintaining optimal growth. Use FVA to identify metabolic engineering targets and understand network flexibility.

Can I build a constraint-based metabolic model from scratch?

Yes, you can build constraint-based metabolic models from scratch using the COBRApy Python library. This enables custom metabolic network simulation and cellular phenotype prediction for non-standard organisms.

Does this tool support flux sampling for analyzing metabolic networks?

Yes, flux sampling is supported to analyze the solution space of metabolic networks. This generates feasible flux distributions, complementing FBA and FVA by providing a broader view of cellular behavior.