Context-Specific Models
GIMME Algorithm
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
iMAT Algorithm
def imat(model, expression_data, high_threshold=0.75, low_threshold=0.25):
'''Integrative Metabolic Analysis Tool (iMAT)
Maximizes agreement between flux activity and expression:
- Highly expressed reactions should carry flux
- Lowly expressed reactions should have zero flux
More sophisticated than GIMME - uses MILP optimization.
'''
from cobra import Reaction
# Classify reactions by expression
high_expr_rxns = []
low_expr_rxns = []
for rxn in model.reactions:
if rxn.genes:
# Aggregate gene expression (use max for OR, min for AND)
gene_expr = [expression_data.get(g.id, 0.5) for g in rxn.genes]
rxn_expr = max(gene_expr) # Simplified OR logic
if rxn_expr > np.percentile(list(expression_data.values()), high_threshold * 100):
high_expr_rxns.append(rxn.id)
elif rxn_expr < np.percentile(list(expression_data.values()), low_threshold * 100):
low_expr_rxns.append(rxn.id)
# Create MILP to maximize consistent reactions
# This is a simplified version - full iMAT uses binary variables
context_model = model.copy()
# Force flux through highly expressed reactions
for rxn_id in high_expr_rxns:
rxn = context_model.reactions.get_by_id(rxn_id)
rxn.lower_bound = max(rxn.lower_bound, 0.01)
# Constrain lowly expressed reactions
for rxn_id in low_expr_rxns:
rxn = context_model.reactions.get_by_id(rxn_id)
rxn.upper_bound = min(rxn.upper_bound, 0.1)
rxn.lower_bound = max(rxn.lower_bound, -0.1)
return context_model, high_expr_rxns, low_expr_rxns
Expression Data Integration
def load_expression_data(filepath, gene_col='gene_id', expr_col='TPM'):
'''Load and normalize expression data
Accepts:
- RNA-seq counts (TPM, FPKM)
- Microarray intensities
- Proteomics abundances
Returns dict mapping gene_id -> normalized expression
'''
import pandas as pd
df = pd.read_csv(filepath)
# Log-transform if needed (high dynamic range)
expr = df[expr_col].values
if expr.max() / expr.mean() > 100:
expr = np.log2(expr + 1)
# Normalize to 0-1 range
expr_norm = (expr - expr.min()) / (expr.max() - expr.min())
return dict(zip(df[gene_col], expr_norm))
def aggregate_gene_expression(model, expression_data, method='max'):
'''Map gene expression to reactions
Methods:
- 'max': Use maximum gene expression (OR logic)
- 'min': Use minimum gene expression (AND logic)
- 'mean': Average across genes
For GPR: (A and B) or C
- min(A, B) for the complex
- max(complex, C) for the alternatives
'''
rxn_expression = {}
for rxn in model.reactions:
if not rxn.genes:
rxn_expression[rxn.id] = 0.5 # Default for non-enzymatic
continue
gene_expr = [expression_data.get(g.id, 0.5) for g in rxn.genes]
if method == 'max':
rxn_expression[rxn.id] = max(gene_expr)
elif method == 'min':
rxn_expression[rxn.id] = min(gene_expr)
else:
rxn_expression[rxn.id] = np.mean(gene_expr)
return rxn_expression
Tissue-Specific Human Models
def create_tissue_model(generic_model, gtex_expression, tissue='liver'):
'''Create tissue-specific model from GTEx expression data
GTEx provides median TPM for 54 human tissues.
Download from: https://gtexportal.org/home/datasets
'''
import pandas as pd
# Load GTEx median expression
gtex = pd.read_csv(gtex_expression, sep='\t')
# Extract tissue column
tissue_col = [c for c in gtex.columns if tissue.lower() in c.lower()][0]
expression = dict(zip(gtex['gene_id'], gtex[tissue_col]))
# Apply GIMME
tissue_model = gimme(generic_model, expression, threshold=0.25)
return tissue_model
Validate Context Model
def validate_context_model(original, context, expression_data):
'''Compare original and context-specific models
Checks:
1. Growth capability maintained
2. Inactive reactions reduced
3. Active reactions maintained
'''
# Growth comparison
orig_growth = original.optimize().objective_value
context_growth = context.optimize().objective_value
# Count constrained reactions
constrained = 0
for rxn in context.reactions:
orig_rxn = original.reactions.get_by_id(rxn.id)
if rxn.upper_bound < orig_rxn.upper_bound:
constrained += 1
return {
'original_growth': orig_growth,
'context_growth': context_growth,
'growth_ratio': context_growth / orig_growth,
'constrained_reactions': constrained,
'total_reactions': len(context.reactions)
}
Related Skills
- systems-biology/flux-balance-analysis - Run FBA on context models
- differential-expression/de-results - Generate expression data
- single-cell/clustering - Cell-type specific expression
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