bio-systems-biology-context-specific-models

Version Compatibility

Reference examples tested with: COBRApy 0.29+, numpy 1.26+, pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Context-Specific Models

"Build a tissue-specific metabolic model from my expression data" → Constrain a generic genome-scale model using transcriptomics data to produce a context-specific model reflecting the active metabolism of a particular tissue or condition, using GIMME, iMAT, or INIT algorithms.

• Python: custom implementations with cobra model manipulation (COBRApy)

GIMME Algorithm

Goal: Build a tissue-specific metabolic model by integrating transcriptomics data with a generic genome-scale model, retaining only metabolically active reactions.

Approach: Map gene expression values to reactions, penalize flux through lowly-expressed reactions while maintaining minimum biomass production, and remove inactive reactions to produce a context-specific model.

import cobra
import numpy as np

def gimme(model, expression_data, threshold=0.25, required_growth=0.1):
    '''Gene Inactivity Moderated by Metabolism and Expression (GIMME)

    Creates context-specific model by:
    1. Penalizing flux through lowly-expressed reactions
    2. Requiring minimum biomass production

    Args:
        expression_data: dict mapping gene_id -> expression value
        threshold: Expression percentile below which genes are inactive
                  0.25 = bottom 25% considered inactive
        required_growth: Minimum growth rate to maintain

    Returns:
        Context-specific model with inactive reactions constrained
    '''
    # Calculate expression threshold
    values = list(expression_data.values())
    cutoff = np.percentile(values, threshold * 100)

    # Identify lowly-expressed genes
    low_expressed = {g for g, v in expression_data.items() if v < cutoff}

    # Create context model
    context_model = model.copy()

    # Set minimum growth constraint
    context_model.reactions.get_by_id('Biomass_Ecoli_core').lower_bound = required_growth

    # Minimize flux through reactions with low-expressed genes
    for rxn in context_model.reactions:
        genes = {g.id for g in rxn.genes}
        if genes and genes.issubset(low_expressed):
            # This reaction is likely inactive - constrain it
            rxn.upper_bound = min(rxn.upper_bound, 1.0)
            rxn.lower_bound = max(rxn.lower_bound, -1.0)

    return context_model