Does it support gene knockout analysis?

Absolutely. It includes guidance for single and double gene/reaction deletions to study metabolic robustness and identify essential genes.

What is COBRApy used for in systems biology?

COBRApy is used for constraint-based modeling of metabolic networks, allowing researchers to simulate cellular metabolism and predict how changes, such as nutrient availability or genetic modifications, affect growth and flux.

Can this skill help with SBML files?

Yes, the skill provides specific implementation patterns for loading, reading, and writing SBML (Systems Biology Markup Language) models, which is the industry standard for metabolic reconstructions.

What optimization methods are supported through this skill?

It provides best practices for FBA, pFBA, geometric FBA, and Flux Variability Analysis (FVA), and is compatible with major solvers like GLPK, CPLEX, and Gurobi.

COBRApy Metabolic Modeling

Name: COBRApy Metabolic Modeling
Author: Microck

byMicrock

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Data Science & ML

Simulates and analyzes genome-scale metabolic models using constraint-based reconstruction and analysis (COBRA) techniques.

About

COBRApy is a specialized Python library designed for the quantitative analysis of metabolic networks. It enables researchers and bioinformaticians to perform advanced simulations like Flux Balance Analysis (FBA) and Gene Deletions to predict cellular phenotypes and optimize metabolic engineering strategies. This skill integrates seamlessly into Claude's workflow, providing domain-specific guidance for loading SBML models, managing metabolic components, and conducting complex flux analyses for systems biology research.

Key Features

81 GitHub stars
Flux Balance Analysis (FBA) and Parsimonious FBA (pFBA)
Gene and reaction knockout simulations for essentiality studies
Flux Variability Analysis (FVA) for uncertainty assessment
Model management for SBML, JSON, and YAML formats
Advanced flux sampling and production envelope calculation

Use Cases

Optimizing metabolic pathways for biofuel or chemical production
Predicting bacterial growth rates under varying nutrient conditions
Identifying essential genes for drug target discovery in pathogens

About

Key Features

81 GitHub stars
Flux Balance Analysis (FBA) and Parsimonious FBA (pFBA)
Gene and reaction knockout simulations for essentiality studies
Flux Variability Analysis (FVA) for uncertainty assessment
Model management for SBML, JSON, and YAML formats
Advanced flux sampling and production envelope calculation

Use Cases

Optimizing metabolic pathways for biofuel or chemical production
Predicting bacterial growth rates under varying nutrient conditions
Identifying essential genes for drug target discovery in pathogens