--- name: bio-systems-biology-model-curation description: Validate, gap-fill, and curate genome-scale metabolic models using memote for quality scores and COBRApy for manual curation. Ensure models meet SBML standards and produce biologically meaningful predictions. Use when improving draft models or preparing models for publication. tool_type: python primary_tool: memote measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- # Model Curation ## Memote Quality Assessment ```bash # 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 ```python 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 ```python 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]) ``` ## Identify Dead-End Metabolites ```python def find_dead_end_metabolites(model): '''Find metabolites that cannot be produced or consumed Dead-end metabolites indicate: - Missing reactions in the network - Incorrect reaction stoichiometry - Incomplete pathways ''' dead_ends = [] for met in model.metabolites: producing = [r for r in met.reactions if r.get_coefficient(met) > 0] consuming = [r for r in met.reactions if r.get_coefficient(met) < 0] if not producing or not consuming: dead_ends.append({ 'metabolite': met.id, 'name': met.name, 'producers': len(producing), 'consumers': len(consuming) }) return dead_ends dead_ends = find_dead_end_metabolites(model) print(f'Found {len(dead_ends)} dead-end metabolites') ``` ## Check Mass and Charge Balance ```python def check_reaction_balance(reaction): '''Check if reaction is mass and charge balanced Unbalanced reactions indicate: - Missing metabolites - Wrong stoichiometry - Proton accounting issues ''' mass_balance = {} charge_balance = 0 for met, coef in reaction.metabolites.items(): # Check mass if met.formula: for element, count in met.elements.items(): mass_balance[element] = mass_balance.get(element, 0) + coef * count # Check charge if met.charge is not None: charge_balance += coef * met.charge is_balanced = all(abs(v) < 1e-6 for v in mass_balance.values()) is_charge_balanced = abs(charge_balance) < 1e-6 return { 'mass_balanced': is_balanced, 'charge_balanced': is_charge_balanced, 'mass_imbalance': {k: v for k, v in mass_balance.items() if abs(v) > 1e-6} } # Check all reactions unbalanced = [] for rxn in model.reactions: result = check_reaction_balance(rxn) if not result['mass_balanced']: unbalanced.append((rxn.id, result['mass_imbalance'])) ``` ## Fix Gene-Protein-Reaction Rules ```python def standardize_gpr(model): '''Standardize gene-protein-reaction rules GPR format: (gene1 and gene2) or gene3 - 'and' = protein complex (all genes required) - 'or' = isozymes (any gene sufficient) ''' for rxn in model.reactions: if rxn.gene_reaction_rule: # Standardize formatting rule = rxn.gene_reaction_rule rule = rule.replace(' AND ', ' and ') rule = rule.replace(' OR ', ' or ') rxn.gene_reaction_rule = rule def identify_orphan_reactions(model): '''Find reactions without gene associations Orphan reactions may be: - Spontaneous reactions - Unannotated genes - Transport reactions (often orphan) ''' orphans = [r for r in model.reactions if not r.genes] # Classify orphans exchange = [r for r in orphans if r in model.exchanges] transport = [r for r in orphans if 'transport' in r.name.lower() or 't_' in r.id.lower()] other = [r for r in orphans if r not in exchange and r not in transport] return { 'exchange': len(exchange), 'transport': len(transport), 'other': len(other), 'total': len(orphans) } ``` ## Annotation Standards ```python def add_standard_annotations(model): '''Add standard database annotations Required annotations for SBML compliance: - KEGG IDs for reactions and metabolites - ChEBI IDs for metabolites - BiGG IDs if applicable ''' for met in model.metabolites: if not hasattr(met, 'annotation'): met.annotation = {} # Add SBO term for metabolite met.annotation['sbo'] = 'SBO:0000247' # Simple chemical for rxn in model.reactions: if not hasattr(rxn, 'annotation'): rxn.annotation = {} # Add SBO term based on reaction type if rxn in model.exchanges: rxn.annotation['sbo'] = 'SBO:0000627' # Exchange else: rxn.annotation['sbo'] = 'SBO:0000176' # Biochemical reaction ``` ## Related Skills - systems-biology/metabolic-reconstruction - Generate draft models - systems-biology/flux-balance-analysis - Test curated models - pathway-analysis/kegg-pathways - Add KEGG annotations --- Source attribution: GPTomics Original project: https://github.com/GPTomics/bioSkills License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 FridrichMethod Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. --- NOTE ON BUNDLED CONTENT This repository aggregates skills from multiple upstream projects (see the README for the source list). Each `skills//` directory may carry its own license inherited from its upstream source. The MIT license above applies to this repository's curation and the scripts/workflows in `/`, `/scripts/`, and `/.github/` — not to the upstream skill content itself. Consult the upstream repository linked in the README and any LICENSE file inside a skill directory before reusing skill content commercially. # Mega AI Hub resource metadata Creator / publisher: GPTomics Original project: https://github.com/GPTomics/bioSkills Attribution basis: Exact package in biomedical source index and publisher source table Fields of work: Data & Analytics, Education & Research Roles: Business Intelligence Analyst, Data Scientist, Researcher Specialties: API, Backend, Database, Frontend, React, Testing Tools: Cobra, Memote Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 760bcce8d6023e828e3d1742ee472dcd65a0135c5b225fa865d8322115ad49dc Metadata reviewed: 2026-10-27 This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.