Can I use this skill to perform gene knockouts?

Yes, the skill provides specific functions for conducting single and double gene or reaction deletions to observe their impact on growth and flux.

Does it support standard SBML models?

Absolutely. COBRApy supports reading and writing SBML (Systems Biology Markup Language), as well as JSON and YAML formats for model portability.

How does it handle temporary model modifications?

It utilizes Python context managers, allowing you to make temporary changes to objectives or bounds that automatically revert once the analysis is complete.

Is COBRApy efficient for genome-scale models?

Yes, it is specifically designed to handle genome-scale metabolic models (GEMs) and supports parallel processing for computationally intensive tasks like sampling.

What is COBRApy used for?

COBRApy is used for constraint-based reconstruction and analysis of metabolic networks, allowing researchers to simulate and predict the metabolic behavior of organisms.

COBRApy Metabolic Modeling

Name: COBRApy Metabolic Modeling
Author: Activer007

byActiver007

Data Science & ML

Performs constraint-based reconstruction and analysis of metabolic models for systems biology and metabolic engineering.

About

COBRApy is a specialized skill for Claude Code that enables sophisticated metabolic modeling using the Python COBRA library. It allows researchers and bioinformaticians to simulate cellular metabolism, perform genome-scale metabolic reconstructions, and predict phenotypic behaviors through Flux Balance Analysis (FBA) and Flux Variability Analysis (FVA). By providing a structured framework for managing metabolic components, gene-reaction rules, and environmental constraints, this skill facilitates complex tasks like gene knockout studies, minimal media optimization, and flux sampling, making it an essential tool for advanced metabolic engineering and systems biology workflows.

Key Features

0 GitHub stars
Flux Balance Analysis (FBA) and Flux Variability Analysis (FVA) for metabolic simulation.
Gene and reaction deletion studies to identify essential metabolic components.
Advanced flux sampling and production envelope analysis for phenotypic prediction.
Comprehensive model management including SBML, JSON, and YAML support.
Growth media optimization and minimal media calculation.

Use Cases

Identifying essential genes for drug target discovery via computational knockouts.
Predicting bacterial growth rates and metabolic fluxes under various environmental conditions.
Designing optimal growth media and metabolic pathways for industrial fermentation.

About

Key Features

0 GitHub stars
Flux Balance Analysis (FBA) and Flux Variability Analysis (FVA) for metabolic simulation.
Gene and reaction deletion studies to identify essential metabolic components.
Advanced flux sampling and production envelope analysis for phenotypic prediction.
Comprehensive model management including SBML, JSON, and YAML support.
Growth media optimization and minimal media calculation.

Use Cases

Identifying essential genes for drug target discovery via computational knockouts.
Predicting bacterial growth rates and metabolic fluxes under various environmental conditions.
Designing optimal growth media and metabolic pathways for industrial fermentation.