bio-systems-biology-model-curation

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
• CLI: <tool> --version then <tool> --help to confirm flags

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

Model Curation

"Validate and improve the quality of my metabolic model" → Score a genome-scale model against SBML community standards using memote, then gap-fill blocked reactions and fix stoichiometric inconsistencies using COBRApy to ensure biologically meaningful predictions.

• CLI: memote report snapshot for quality scoring
• Python: cobra.flux_analysis.gapfilling.gapfill() for gap-filling

Memote Quality Assessment

Goal: Evaluate the quality and standards compliance of a genome-scale metabolic model to identify areas needing curation.

Approach: Run memote snapshot to score the model against SBML community standards, then use the Python API to inspect individual test failures and guide manual fixes.

# Install memote
pip install memote

# Run full quality report
memote report snapshot model.xml --filename report.html

# Quick score
memote run model.xml

# Continuous integration testing
memote run --pytest-args "--tb=short" model.xml

Memote Python API

import memote
import cobra

model = cobra.io.read_sbml_model('model.xml')

# Run all tests
result = memote.suite.api.run(model)

# Get score breakdown
scores = memote.suite.api.snapshot(model)
print(f"Total score: {scores['score']['total_score']:.2%}")

# Detailed test results
for test_name, test_result in scores['tests'].items():
    if not test_result['passed']:
        print(f"Failed: {test_name}")

Gap-Filling

import cobra
from cobra.flux_analysis import gapfill

model = cobra.io.read_sbml_model('model.xml')

# Load universal reaction database
universal = cobra.io.read_sbml_model('universal_model.xml')

# Find reactions to add for growth
# demand: reaction to optimize (usually biomass exchange)
# iterations: number of alternative solutions
solution = gapfill(model, universal,
                   demand=model.reactions.BIOMASS,
                   iterations=5)

# solution contains list of reaction sets to add
for i, rxn_set in enumerate(solution):
    print(f'Solution {i+1}: {[r.id for r in rxn_set]}')

# Add first solution
for rxn in solution[0]:
    model.add_reactions([rxn])