bio-systems-biology-gene-essentiality

Version Compatibility

Reference examples tested with: COBRApy 0.29+

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.

Gene Essentiality Analysis

"Predict which genes are essential for growth in my organism" → Perform in silico single and double gene knockouts on a metabolic model, identifying genes whose deletion abolishes growth and synthetic lethal pairs for drug target discovery.

• Python: cobra.flux_analysis.single_gene_deletion(), cobra.flux_analysis.double_gene_deletion() (COBRApy)

Single Gene Knockouts

Goal: Identify essential genes by simulating single gene deletions and measuring growth impact on a metabolic model.

Approach: Perform in silico single gene deletions across all model genes, compare post-deletion growth to wild-type, and classify genes as essential (lethal), growth-reducing, or non-essential based on growth ratio thresholds.

import cobra
from cobra.flux_analysis import single_gene_deletion

model = cobra.io.load_model('textbook')

# Perform all single gene deletions
# Returns growth rate with each gene knocked out
deletion_results = single_gene_deletion(model)

# deletion_results is a DataFrame with:
# - ids: gene IDs (frozenset)
# - growth: growth rate after deletion
# - status: solver status

# Find essential genes (no growth when deleted)
# Essential: growth < 0.01 (allowing for numerical tolerance)
essential = deletion_results[deletion_results['growth'] < 0.01]
print(f'Essential genes: {len(essential)}')

Classify Gene Essentiality

def classify_gene_essentiality(model, growth_threshold=0.1):
    '''Classify genes by their impact on growth

    Categories:
    - Essential: Growth < 1% of WT (lethal)
    - Growth-reducing: 1-50% of WT
    - Non-essential: >50% of WT
    '''
    from cobra.flux_analysis import single_gene_deletion

    # Get wild-type growth
    wt_growth = model.optimize().objective_value

    # Run deletions
    results = single_gene_deletion(model)
    results['relative_growth'] = results['growth'] / wt_growth

    # Classify
    results['classification'] = 'non-essential'
    results.loc[results['relative_growth'] < 0.5, 'classification'] = 'growth-reducing'
    results.loc[results['relative_growth'] < 0.01, 'classification'] = 'essential'

    classification_counts = results['classification'].value_counts()
    return results, classification_counts