What is COBRApy used for?

COBRApy is a Python library used for constraint-based reconstruction and analysis of metabolic models, allowing researchers to simulate cellular metabolism and predict phenotypic behavior.

Can I use this skill to identify essential genes?

Yes, the skill includes specialized workflows for single and double gene deletion studies, which are used to determine which genes are essential for an organism's growth.

Does COBRApy support standard biological file formats?

Absolutely. It provides robust support for reading and writing SBML (Systems Biology Markup Language), as well as JSON and YAML for compatibility with tools like Escher.

How does this skill improve Claude's ability to help with metabolic modeling?

This skill provides Claude with domain-specific implementation patterns, best practices for solver management, and complex workflow templates specifically for the COBRApy library.

COBRApy Metabolic Modeling

Name: COBRApy Metabolic Modeling
Author: lifangda

bylifangda

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Data Science & ML

Facilitates constraint-based reconstruction and analysis (COBRA) of metabolic models for systems biology and metabolic engineering.

About

COBRApy is a specialized skill for Claude Code designed to streamline metabolic modeling and systems biology workflows. It enables researchers and bioinformaticians to perform genome-scale metabolic analysis, including Flux Balance Analysis (FBA), gene knockout studies, and growth media optimization directly within their development environment. By providing a structured framework for managing SBML models and simulating cellular metabolism, this skill bridges the gap between raw biological data and actionable metabolic engineering insights, making complex bioprocessing simulations more accessible.

Key Features

Automated single and double gene/reaction knockout studies
Flux Balance Analysis (FBA) and Parsimonious FBA simulations
Seamless integration with SBML, JSON, and YAML metabolic model formats
Growth media management and minimal media requirement calculations
16 GitHub stars
Flux Variability Analysis (FVA) including loopless and geometric variants

Use Cases

Optimizing industrial fermentation processes by identifying high-yield metabolic pathways
Identifying essential genes and potential drug targets through computational knockout screens
Predicting microbial growth rates and metabolic fluxes under various environmental conditions

About

Key Features

Automated single and double gene/reaction knockout studies
Flux Balance Analysis (FBA) and Parsimonious FBA simulations
Seamless integration with SBML, JSON, and YAML metabolic model formats
Growth media management and minimal media requirement calculations
16 GitHub stars
Flux Variability Analysis (FVA) including loopless and geometric variants

Use Cases

Optimizing industrial fermentation processes by identifying high-yield metabolic pathways
Identifying essential genes and potential drug targets through computational knockout screens
Predicting microbial growth rates and metabolic fluxes under various environmental conditions