Can I perform gene knockout studies with COBRApy?

Yes, it includes functions for single and double gene/reaction deletions to evaluate the impact of genetic changes on metabolic flux and growth.

Which model formats are compatible with this skill?

It supports standard formats including SBML (.xml), JSON (.json), and YAML (.yml), making it compatible with major model repositories like BiGG.

How are temporary model changes handled?

The skill utilizes Python context managers, ensuring that any temporary modifications to bounds or objectives are automatically reverted after the analysis is complete.

What is the primary use of the COBRApy skill?

The COBRApy skill is used for constraint-based reconstruction and analysis of metabolic networks, allowing for simulations like Flux Balance Analysis (FBA) to predict cellular behavior.

Does this skill support parallel processing?

Yes, computationally intensive tasks such as flux sampling and double gene deletions support multi-processing to improve performance.

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 Claude Code skill designed for researchers and engineers working with genome-scale metabolic models. It enables sophisticated computational simulations of cellular metabolism, including Flux Balance Analysis (FBA), Flux Variability Analysis (FVA), and gene knockout studies. By providing a structured interface for managing SBML, JSON, and YAML models, this skill facilitates the prediction of phenotypic behaviors, growth media optimization, and flux sampling. It is an essential tool for anyone performing metabolic engineering or systems biology research within a Python-based computational environment.

Key Features

Detailed gene and reaction deletion studies to identify essential metabolic pathways.
Advanced Flux Variability Analysis (FVA) and flux sampling for uncertainty quantification.
0 GitHub stars
Growth media management and minimal media calculation for optimized cellular growth.
Support for SBML, JSON, and YAML model formats for seamless data exchange.
Comprehensive Flux Balance Analysis (FBA) and Parsimonious FBA (pFBA) simulations.

Use Cases

Designing and optimizing synthetic media for industrial microbial fermentation processes.
Conducting large-scale computational gene knockout screens to identify potential drug targets.
Predicting cellular growth rates and metabolic flux distributions in specific environmental conditions.

About

Key Features

Detailed gene and reaction deletion studies to identify essential metabolic pathways.
Advanced Flux Variability Analysis (FVA) and flux sampling for uncertainty quantification.
0 GitHub stars
Growth media management and minimal media calculation for optimized cellular growth.
Support for SBML, JSON, and YAML model formats for seamless data exchange.
Comprehensive Flux Balance Analysis (FBA) and Parsimonious FBA (pFBA) simulations.

Use Cases

Designing and optimizing synthetic media for industrial microbial fermentation processes.
Conducting large-scale computational gene knockout screens to identify potential drug targets.
Predicting cellular growth rates and metabolic flux distributions in specific environmental conditions.