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FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33602,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fcobrapy\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32],"engineering","analysis","sampling","models","flux","modeling","systems biology","systems","biology","metabolic engineering","cobra","for","gene","constraint","2026-11-10 10:12:30",false,"---\nname: cobrapy\ndescription: \"Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.\"\n---\n\n# COBRApy - Constraint-Based Reconstruction and Analysis\n\n## Overview\n\nCOBRApy is a Python library for constraint-based reconstruction and analysis (COBRA) of metabolic models, essential for systems biology research. Work with genome-scale metabolic models, perform computational simulations of cellular metabolism, conduct metabolic engineering analyses, and predict phenotypic behaviors.\n\n## Core Capabilities\n\nCOBRApy provides comprehensive tools organized into several key areas:\n\n### 1. Model Management\n\nLoad existing models from repositories or files:\n```python\nfrom cobra.io import load_model\n\n# Load bundled test models\nmodel = load_model(\"textbook\")  # E. coli core model\nmodel = load_model(\"ecoli\")     # Full E. coli model\nmodel = load_model(\"salmonella\")\n\n# Load from files\nfrom cobra.io import read_sbml_model, load_json_model, load_yaml_model\nmodel = read_sbml_model(\"path\u002Fto\u002Fmodel.xml\")\nmodel = load_json_model(\"path\u002Fto\u002Fmodel.json\")\nmodel = load_yaml_model(\"path\u002Fto\u002Fmodel.yml\")\n```\n\nSave models in various formats:\n```python\nfrom cobra.io import write_sbml_model, save_json_model, save_yaml_model\nwrite_sbml_model(model, \"output.xml\")  # Preferred format\nsave_json_model(model, \"output.json\")  # For Escher compatibility\nsave_yaml_model(model, \"output.yml\")   # Human-readable\n```\n\n### 2. Model Structure and Components\n\nAccess and inspect model components:\n```python\n# Access components\nmodel.reactions      # DictList of all reactions\nmodel.metabolites    # DictList of all metabolites\nmodel.genes          # DictList of all genes\n\n# Get specific items by ID or index\nreaction = model.reactions.get_by_id(\"PFK\")\nmetabolite = model.metabolites[0]\n\n# Inspect properties\nprint(reaction.reaction)        # Stoichiometric equation\nprint(reaction.bounds)          # Flux constraints\nprint(reaction.gene_reaction_rule)  # GPR logic\nprint(metabolite.formula)       # Chemical formula\nprint(metabolite.compartment)   # Cellular location\n```\n\n### 3. Flux Balance Analysis (FBA)\n\nPerform standard FBA simulation:\n```python\n# Basic optimization\nsolution = model.optimize()\nprint(f\"Objective value: {solution.objective_value}\")\nprint(f\"Status: {solution.status}\")\n\n# Access fluxes\nprint(solution.fluxes[\"PFK\"])\nprint(solution.fluxes.head())\n\n# Fast optimization (objective value only)\nobjective_value = model.slim_optimize()\n\n# Change objective\nmodel.objective = \"ATPM\"\nsolution = model.optimize()\n```\n\nParsimonious FBA (minimize total flux):\n```python\nfrom cobra.flux_analysis import pfba\nsolution = pfba(model)\n```\n\nGeometric FBA (find central solution):\n```python\nfrom cobra.flux_analysis import geometric_fba\nsolution = geometric_fba(model)\n```\n\n### 4. Flux Variability Analysis (FVA)\n\nDetermine flux ranges for all reactions:\n```python\nfrom cobra.flux_analysis import flux_variability_analysis\n\n# Standard FVA\nfva_result = flux_variability_analysis(model)\n\n# FVA at 90% optimality\nfva_result = flux_variability_analysis(model, fraction_of_optimum=0.9)\n\n# Loopless FVA (eliminates thermodynamically infeasible loops)\nfva_result = flux_variability_analysis(model, loopless=True)\n\n# FVA for specific reactions\nfva_result = flux_variability_analysis(\n    model,\n    reaction_list=[\"PFK\", \"FBA\", \"PGI\"]\n)\n```\n\n### 5. Gene and Reaction Deletion Studies\n\nPerform knockout analyses:\n```python\nfrom cobra.flux_analysis import (\n    single_gene_deletion,\n    single_reaction_deletion,\n    double_gene_deletion,\n    double_reaction_deletion\n)\n\n# Single deletions\ngene_results = single_gene_deletion(model)\nreaction_results = single_reaction_deletion(model)\n\n# Double deletions (uses multiprocessing)\ndouble_gene_results = double_gene_deletion(\n    model,\n    processes=4  # Number of CPU cores\n)\n\n# Manual knockout using context manager\nwith model:\n    model.genes.get_by_id(\"b0008\").knock_out()\n    solution = model.optimize()\n    print(f\"Growth after knockout: {solution.objective_value}\")\n# Model automatically reverts after context exit\n```\n\n### 6. Growth Media and Minimal Media\n\nManage growth medium:\n```python\n# View current medium\nprint(model.medium)\n\n# Modify medium (must reassign entire dict)\nmedium = model.medium\nmedium[\"EX_glc__D_e\"] = 10.0  # Set glucose uptake\nmedium[\"EX_o2_e\"] = 0.0       # Anaerobic conditions\nmodel.medium = medium\n\n# Calculate minimal media\nfrom cobra.medium import minimal_medium\n\n# Minimize total import flux\nmin_medium = minimal_medium(model, minimize_components=False)\n\n# Minimize number of components (uses MILP, slower)\nmin_medium = minimal_medium(\n    model,\n    minimize_components=True,\n    open_exchanges=True\n)\n```\n\n### 7. Flux Sampling\n\nSample the feasible flux space:\n```python\nfrom cobra.sampling import sample\n\n# Sample using OptGP (default, supports parallel processing)\nsamples = sample(model, n=1000, method=\"optgp\", processes=4)\n\n# Sample using ACHR\nsamples = sample(model, n=1000, method=\"achr\")\n\n# Validate samples\nfrom cobra.sampling import OptGPSampler\nsampler = OptGPSampler(model, processes=4)\nsampler.sample(1000)\nvalidation = sampler.validate(sampler.samples)\nprint(validation.value_counts())  # Should be all 'v' for valid\n```\n\n### 8. Production Envelopes\n\nCalculate phenotype phase planes:\n```python\nfrom cobra.flux_analysis import production_envelope\n\n# Standard production envelope\nenvelope = production_envelope(\n    model,\n    reactions=[\"EX_glc__D_e\", \"EX_o2_e\"],\n    objective=\"EX_ac_e\"  # Acetate production\n)\n\n# With carbon yield\nenvelope = production_envelope(\n    model,\n    reactions=[\"EX_glc__D_e\", \"EX_o2_e\"],\n    carbon_sources=\"EX_glc__D_e\"\n)\n\n# Visualize (use matplotlib or pandas plotting)\nimport matplotlib.pyplot as plt\nenvelope.plot(x=\"EX_glc__D_e\", y=\"EX_o2_e\", kind=\"scatter\")\nplt.show()\n```\n\n### 9. Gapfilling\n\nAdd reactions to make models feasible:\n```python\nfrom cobra.flux_analysis import gapfill\n\n# Prepare universal model with candidate reactions\nuniversal = load_model(\"universal\")\n\n# Perform gapfilling\nwith model:\n    # Remove reactions to create gaps for demonstration\n    model.remove_reactions([model.reactions.PGI])\n\n    # Find reactions needed\n    solution = gapfill(model, universal)\n    print(f\"Reactions to add: {solution}\")\n```\n\n### 10. Model Building\n\nBuild models from scratch:\n```python\nfrom cobra import Model, Reaction, Metabolite\n\n# Create model\nmodel = Model(\"my_model\")\n\n# Create metabolites\natp_c = Metabolite(\"atp_c\", formula=\"C10H12N5O13P3\",\n                   name=\"ATP\", compartment=\"c\")\nadp_c = Metabolite(\"adp_c\", formula=\"C10H12N5O10P2\",\n                   name=\"ADP\", compartment=\"c\")\npi_c = Metabolite(\"pi_c\", formula=\"HO4P\",\n                  name=\"Phosphate\", compartment=\"c\")\n\n# Create reaction\nreaction = Reaction(\"ATPASE\")\nreaction.name = \"ATP hydrolysis\"\nreaction.subsystem = \"Energy\"\nreaction.lower_bound = 0.0\nreaction.upper_bound = 1000.0\n\n# Add metabolites with stoichiometry\nreaction.add_metabolites({\n    atp_c: -1.0,\n    adp_c: 1.0,\n    pi_c: 1.0\n})\n\n# Add gene-reaction rule\nreaction.gene_reaction_rule = \"(gene1 and gene2) or gene3\"\n\n# Add to model\nmodel.add_reactions([reaction])\n\n# Add boundary reactions\nmodel.add_boundary(atp_c, type=\"exchange\")\nmodel.add_boundary(adp_c, type=\"demand\")\n\n# Set objective\nmodel.objective = \"ATPASE\"\n```\n\n## Common Workflows\n\n### Workflow 1: Load Model and Predict Growth\n\n```python\nfrom cobra.io import load_model\n\n# Load model\nmodel = load_model(\"ecoli\")\n\n# Run FBA\nsolution = model.optimize()\nprint(f\"Growth rate: {solution.objective_value:.3f} \u002Fh\")\n\n# Show active pathways\nprint(solution.fluxes[solution.fluxes.abs() > 1e-6])\n```\n\n### Workflow 2: Gene Knockout Screen\n\n```python\nfrom cobra.io import load_model\nfrom cobra.flux_analysis import single_gene_deletion\n\n# Load model\nmodel = load_model(\"ecoli\")\n\n# Perform single gene deletions\nresults = single_gene_deletion(model)\n\n# Find essential genes (growth \u003C threshold)\nessential_genes = results[results[\"growth\"] \u003C 0.01]\nprint(f\"Found {len(essential_genes)} essential genes\")\n\n# Find genes with minimal impact\nneutral_genes = results[results[\"growth\"] > 0.9 * solution.objective_value]\n```\n\n### Workflow 3: Media Optimization\n\n```python\nfrom cobra.io import load_model\nfrom cobra.medium import minimal_medium\n\n# Load model\nmodel = load_model(\"ecoli\")\n\n# Calculate minimal medium for 50% of max growth\ntarget_growth = model.slim_optimize() * 0.5\nmin_medium = minimal_medium(\n    model,\n    target_growth,\n    minimize_components=True\n)\n\nprint(f\"Minimal medium components: {len(min_medium)}\")\nprint(min_medium)\n```\n\n### Workflow 4: Flux Uncertainty Analysis\n\n```python\nfrom cobra.io import load_model\nfrom cobra.flux_analysis import flux_variability_analysis\nfrom cobra.sampling import sample\n\n# Load model\nmodel = load_model(\"ecoli\")\n\n# First check flux ranges at optimality\nfva = flux_variability_analysis(model, fraction_of_optimum=1.0)\n\n# For reactions with large ranges, sample to understand distribution\nsamples = sample(model, n=1000)\n\n# Analyze specific reaction\nreaction_id = \"PFK\"\nimport matplotlib.pyplot as plt\nsamples[reaction_id].hist(bins=50)\nplt.xlabel(f\"Flux through {reaction_id}\")\nplt.ylabel(\"Frequency\")\nplt.show()\n```\n\n### Workflow 5: Context Manager for Temporary Changes\n\nUse context managers to make temporary modifications:\n```python\n# Model remains unchanged outside context\nwith model:\n    # Temporarily change objective\n    model.objective = \"ATPM\"\n\n    # Temporarily modify bounds\n    model.reactions.EX_glc__D_e.lower_bound = -5.0\n\n    # Temporarily knock out genes\n    model.genes.b0008.knock_out()\n\n    # Optimize with changes\n    solution = model.optimize()\n    print(f\"Modified growth: {solution.objective_value}\")\n\n# All changes automatically reverted\nsolution = model.optimize()\nprint(f\"Original growth: {solution.objective_value}\")\n```\n\n## Key Concepts\n\n### DictList Objects\nModels use `DictList` objects for reactions, metabolites, and genes - behaving like both lists and dictionaries:\n```python\n# Access by index\nfirst_reaction = model.reactions[0]\n\n# Access by ID\npfk = model.reactions.get_by_id(\"PFK\")\n\n# Query methods\natp_reactions = model.reactions.query(\"atp\")\n```\n\n### Flux Constraints\nReaction bounds define feasible flux ranges:\n- **Irreversible**: `lower_bound = 0, upper_bound > 0`\n- **Reversible**: `lower_bound \u003C 0, upper_bound > 0`\n- Set both bounds simultaneously with `.bounds` to avoid inconsistencies\n\n### Gene-Reaction Rules (GPR)\nBoolean logic linking genes to reactions:\n```python\n# AND logic (both required)\nreaction.gene_reaction_rule = \"gene1 and gene2\"\n\n# OR logic (either sufficient)\nreaction.gene_reaction_rule = \"gene1 or gene2\"\n\n# Complex logic\nreaction.gene_reaction_rule = \"(gene1 and gene2) or (gene3 and gene4)\"\n```\n\n### Exchange Reactions\nSpecial reactions representing metabolite import\u002Fexport:\n- Named with prefix `EX_` by convention\n- Positive flux = secretion, negative flux = uptake\n- Managed through `model.medium` dictionary\n\n## Best Practices\n\n1. **Use context managers** for temporary modifications to avoid state management issues\n2. **Validate models** before analysis using `model.slim_optimize()` to ensure feasibility\n3. **Check solution status** after optimization - `optimal` indicates successful solve\n4. **Use loopless FVA** when thermodynamic feasibility matters\n5. **Set fraction_of_optimum** appropriately in FVA to explore suboptimal space\n6. **Parallelize** computationally expensive operations (sampling, double deletions)\n7. **Prefer SBML format** for model exchange and long-term storage\n8. **Use slim_optimize()** when only objective value needed for performance\n9. **Validate flux samples** to ensure numerical stability\n\n## Troubleshooting\n\n**Infeasible solutions**: Check medium constraints, reaction bounds, and model consistency\n**Slow optimization**: Try different solvers (GLPK, CPLEX, Gurobi) via `model.solver`\n**Unbounded solutions**: Verify exchange reactions have appropriate upper bounds\n**Import errors**: Ensure correct file format and valid SBML identifiers\n\n## References\n\nFor detailed workflows and API patterns, refer to:\n- `references\u002Fworkflows.md` - Comprehensive step-by-step workflow examples\n- `references\u002Fapi_quick_reference.md` - Common function signatures and patterns\n\nOfficial documentation: https:\u002F\u002Fcobrapy.readthedocs.io\u002Fen\u002Flatest\u002F\n","# COBRApy - Constraint-Based Reconstruction and Analysis\n\n## Overview\n\nCOBRApy is a Python library for constraint-based reconstruction and analysis (COBRA) of metabolic models, essential for systems biology research. Work with genome-scale metabolic models, perform computational simulations of cellular metabolism, conduct metabolic engineering analyses, and predict phenotypic behaviors.\n\n## Core Capabilities\n\nCOBRApy provides comprehensive tools organized into several key areas:\n\n### 1. Model Management\n\nLoad existing models from repositories or files:\n```python\nfrom cobra.io import load_model\n\n# Load bundled test models\nmodel = load_model(\"textbook\")  # E. coli core model\nmodel = load_model(\"ecoli\")     # Full E. coli model\nmodel = load_model(\"salmonella\")\n\n# Load from files\nfrom cobra.io import read_sbml_model, load_json_model, load_yaml_model\nmodel = read_sbml_model(\"path\u002Fto\u002Fmodel.xml\")\nmodel = load_json_model(\"path\u002Fto\u002Fmodel.json\")\nmodel = load_yaml_model(\"path\u002Fto\u002Fmodel.yml\")\n```\n\nSave models in various formats:\n```python\nfrom cobra.io import write_sbml_model, save_json_model, save_yaml_model\nwrite_sbml_model(model, \"output.xml\")  # Preferred format\nsave_json_model(model, \"output.json\")  # For Escher compatibility\nsave_yaml_model(model, \"output.yml\")   # Human-readable\n```\n\n### 2. Model Structure and Components\n\nAccess and inspect model components:\n```python\n# Access components\nmodel.reactions      # DictList of all reactions\nmodel.metabolites    # DictList of all metabolites\nmodel.genes          # DictList of all genes\n\n# Get specific items by ID or index\nreaction = model.reactions.get_by_id(\"PFK\")\nmetabolite = model.metabolites[0]\n\n# Inspect properties\nprint(reaction.reaction)        # Stoichiometric equation\nprint(reaction.bounds)          # Flux constraints\nprint(reaction.gene_reaction_rule)  # GPR logic\nprint(metabolite.formula)       # Chemical formula\nprint(metabolite.compartment)   # Cellular location\n```\n\n### 3. Flux Balance Analysis (FBA)\n\nPerform standard FBA simulation:\n```python\n# Basic optimization\nsolution = model.optimize()\nprint(f\"Objective value: {solution.objective_value}\")\nprint(f\"Status: {solution.status}\")\n\n# Access fluxes\nprint(solution.fluxes[\"PFK\"])\nprint(solution.fluxes.head())\n\n# Fast optimization (objective value only)\nobjective_value = model.slim_optimize()\n\n# Change objective\nmodel.objective = \"ATPM\"\nsolution = model.optimize()\n```\n\nParsimonious FBA (minimize total flux):\n```python\nfrom cobra.flux_analysis import pfba\nsolution = pfba(model)\n```\n\nGeometric FBA (find central solution):\n```python\nfrom cobra.flux_analysis import geometric_fba\nsolution = geometric_fba(model)\n```\n\n### 4. Flux Variability Analysis (FVA)\n\nDetermine flux ranges for all reactions:\n```python\nfrom cobra.flux_analysis import flux_variability_analysis\n\n# Standard FVA\nfva_result = flux_variability_analysis(model)\n\n# FVA at 90% optimality\nfva_result = flux_variability_analysis(model, fraction_of_optimum=0.9)\n\n# Loopless FVA (eliminates thermodynamically infeasible loops)\nfva_result = flux_variability_analysis(model, loopless=True)\n\n# FVA for specific reactions\nfva_result = flux_variability_analysis(\n    model,\n    reaction_list=[\"PFK\", \"FBA\", \"PGI\"]\n)\n```\n\n### 5. Gene and Reaction Deletion Studies\n\nPerform knockout analyses:\n```python\nfrom cobra.flux_analysis import (\n    single_gene_deletion,\n    single_reaction_deletion,\n    double_gene_deletion,\n    double_reaction_deletion\n)\n\n# Single deletions\ngene_results = single_gene_deletion(model)\nreaction_results = single_reaction_deletion(model)\n\n# Double deletions (uses multiprocessing)\ndouble_gene_results = double_gene_deletion(\n    model,\n    processes=4  # Number of CPU cores\n)\n\n# Manual knockout using context manager\nwith model:\n    model.genes.get_by_id(\"b0008\").knock_out()\n    solution = model.optimize()\n    print(f\"Growth after knockout: {solution.objective_value}\")\n# Model automatically reverts after context exit\n```\n\n### 6. Growth Media and Minimal Media\n\nManage growth medium:\n```python\n# View current medium\nprint(model.medium)\n\n# Modify medium (must reassign entire dict)\nmedium = model.medium\nmedium[\"EX_glc__D_e\"] = 10.0  # Set glucose uptake\nmedium[\"EX_o2_e\"] = 0.0       # Anaerobic conditions\nmodel.medium = medium\n\n# Calculate minimal media\nfrom cobra.medium import minimal_medium\n\n# Minimize total import flux\nmin_medium = minimal_medium(model, minimize_components=False)\n\n# Minimize number of components (uses MILP, slower)\nmin_medium = minimal_medium(\n    model,\n    minimize_components=True,\n    open_exchanges=True\n)\n```\n\n### 7. Flux Sampling\n\nSample the feasible flux space:\n```python\nfrom cobra.sampling import sample\n\n# Sample using OptGP (default, supports parallel processing)\nsamples = sample(model, n=1000, method=\"optgp\", processes=4)\n\n# Sample using ACHR\nsamples = sample(model, n=1000, method=\"achr\")\n\n# Validate samples\nfrom cobra.sampling import OptGPSampler\nsampler = OptGPSampler(model, processes=4)\nsampler.sample(1000)\nvalidation = sampler.validate(sampler.samples)\nprint(validation.value_counts())  # Should be all 'v' for valid\n```\n\n### 8. Production Envelopes\n\nCalculate phenotype phase planes:\n```python\nfrom cobra.flux_analysis import production_envelope\n\n# Standard production envelope\nenvelope = production_envelope(\n    model,\n    reactions=[\"EX_glc__D_e\", \"EX_o2_e\"],\n    objective=\"EX_ac_e\"  # Acetate production\n)\n\n# With carbon yield\nenvelope = production_envelope(\n    model,\n    reactions=[\"EX_glc__D_e\", \"EX_o2_e\"],\n    carbon_sources=\"EX_glc__D_e\"\n)\n\n# Visualize (use matplotlib or pandas plotting)\nimport matplotlib.pyplot as plt\nenvelope.plot(x=\"EX_glc__D_e\", y=\"EX_o2_e\", kind=\"scatter\")\nplt.show()\n```\n\n### 9. Gapfilling\n\nAdd reactions to make models feasible:\n```python\nfrom cobra.flux_analysis import gapfill\n\n# Prepare universal model with candidate reactions\nuniversal = load_model(\"universal\")\n\n# Perform gapfilling\nwith model:\n    # Remove reactions to create gaps for demonstration\n    model.remove_reactions([model.reactions.PGI])\n\n    # Find reactions needed\n    solution = gapfill(model, universal)\n    print(f\"Reactions to add: {solution}\")\n```\n\n### 10. Model Building\n\nBuild models from scratch:\n```python\nfrom cobra import Model, Reaction, Metabolite\n\n# Create model\nmodel = Model(\"my_model\")\n\n# Create metabolites\natp_c = Metabolite(\"atp_c\", formula=\"C10H12N5O13P3\",\n                   name=\"ATP\", compartment=\"c\")\nadp_c = Metabolite(\"adp_c\", formula=\"C10H12N5O10P2\",\n                   name=\"ADP\", compartment=\"c\")\npi_c = Metabolite(\"pi_c\", formula=\"HO4P\",\n                  name=\"Phosphate\", compartment=\"c\")\n\n# Create reaction\nreaction = Reaction(\"ATPASE\")\nreaction.name = \"ATP hydrolysis\"\nreaction.subsystem = \"Energy\"\nreaction.lower_bound = 0.0\nreaction.upper_bound = 1000.0\n\n# Add metabolites with stoichiometry\nreaction.add_metabolites({\n    atp_c: -1.0,\n    adp_c: 1.0,\n    pi_c: 1.0\n})\n\n# Add gene-reaction rule\nreaction.gene_reaction_rule = \"(gene1 and gene2) or gene3\"\n\n# Add to model\nmodel.add_reactions([reaction])\n\n# Add boundary reactions\nmodel.add_boundary(atp_c, type=\"exchange\")\nmodel.add_boundary(adp_c, type=\"demand\")\n\n# Set objective\nmodel.objective = \"ATPASE\"\n```\n\n## Common Workflows\n\n### Workflow 1: Load Model and Predict Growth\n\n```python\nfrom cobra.io import load_model\n\n# Load model\nmodel = load_model(\"ecoli\")\n\n# Run FBA\nsolution = model.optimize()\nprint(f\"Growth rate: {solution.objective_value:.3f} \u002Fh\")\n\n# Show active pathways\nprint(solution.fluxes[solution.fluxes.abs() > 1e-6])\n```\n\n### Workflow 2: Gene Knockout Screen\n\n```python\nfrom cobra.io import load_model\nfrom cobra.flux_analysis import single_gene_deletion\n\n# Load model\nmodel = load_model(\"ecoli\")\n\n# Perform single gene deletions\nresults = single_gene_deletion(model)\n\n# Find essential genes (growth \u003C threshold)\nessential_genes = results[results[\"growth\"] \u003C 0.01]\nprint(f\"Found {len(essential_genes)} essential genes\")\n\n# Find genes with minimal impact\nneutral_genes = results[results[\"growth\"] > 0.9 * solution.objective_value]\n```\n\n### Workflow 3: Media Optimization\n\n```python\nfrom cobra.io import load_model\nfrom cobra.medium import minimal_medium\n\n# Load model\nmodel = load_model(\"ecoli\")\n\n# Calculate minimal medium for 50% of max growth\ntarget_growth = model.slim_optimize() * 0.5\nmin_medium = minimal_medium(\n    model,\n    target_growth,\n    minimize_components=True\n)\n\nprint(f\"Minimal medium components: {len(min_medium)}\")\nprint(min_medium)\n```\n\n### Workflow 4: Flux Uncertainty Analysis\n\n```python\nfrom cobra.io import load_model\nfrom cobra.flux_analysis import flux_variability_analysis\nfrom cobra.sampling import sample\n\n# Load model\nmodel = load_model(\"ecoli\")\n\n# First check flux ranges at optimality\nfva = flux_variability_analysis(model, fraction_of_optimum=1.0)\n\n# For reactions with large ranges, sample to understand distribution\nsamples = sample(model, n=1000)\n\n# Analyze specific reaction\nreaction_id = \"PFK\"\nimport matplotlib.pyplot as plt\nsamples[reaction_id].hist(bins=50)\nplt.xlabel(f\"Flux through {reaction_id}\")\nplt.ylabel(\"Frequency\")\nplt.show()\n```\n\n### Workflow 5: Context Manager for Temporary Changes\n\nUse context managers to make temporary modifications:\n```python\n# Model remains unchanged outside context\nwith model:\n    # Temporarily change objective\n    model.objective = \"ATPM\"\n\n    # Temporarily modify bounds\n    model.reactions.EX_glc__D_e.lower_bound = -5.0\n\n    # Temporarily knock out genes\n    model.genes.b0008.knock_out()\n\n    # Optimize with changes\n    solution = model.optimize()\n    print(f\"Modified growth: {solution.objective_value}\")\n\n# All changes automatically reverted\nsolution = model.optimize()\nprint(f\"Original growth: {solution.objective_value}\")\n```\n\n## Key Concepts\n\n### DictList Objects\nModels use `DictList` objects for reactions, metabolites, and genes - behaving like both lists and dictionaries:\n```python\n# Access by index\nfirst_reaction = model.reactions[0]\n\n# Access by ID\npfk = model.reactions.get_by_id(\"PFK\")\n\n# Query methods\natp_reactions = model.reactions.query(\"atp\")\n```\n\n### Flux Constraints\nReaction bounds define feasible flux ranges:\n- **Irreversible**: `lower_bound = 0, upper_bound > 0`\n- **Reversible**: `lower_bound \u003C 0, upper_bound > 0`\n- Set both bounds simultaneously with `.bounds` to avoid inconsistencies\n\n### Gene-Reaction Rules (GPR)\nBoolean logic linking genes to reactions:\n```python\n# AND logic (both required)\nreaction.gene_reaction_rule = \"gene1 and gene2\"\n\n# OR logic (either sufficient)\nreaction.gene_reaction_rule = \"gene1 or gene2\"\n\n# Complex logic\nreaction.gene_reaction_rule = \"(gene1 and gene2) or (gene3 and gene4)\"\n```\n\n### Exchange Reactions\nSpecial reactions representing metabolite import\u002Fexport:\n- Named with prefix `EX_` by convention\n- Positive flux = secretion, negative flux = uptake\n- Managed through `model.medium` dictionary\n\n## Best Practices\n\n1. **Use context managers** for temporary modifications to avoid state management issues\n2. **Validate models** before analysis using `model.slim_optimize()` to ensure feasibility\n3. **Check solution status** after optimization - `optimal` indicates successful solve\n4. **Use loopless FVA** when thermodynamic feasibility matters\n5. **Set fraction_of_optimum** appropriately in FVA to explore suboptimal space\n6. **Parallelize** computationally expensive operations (sampling, double deletions)\n7. **Prefer SBML format** for model exchange and long-term storage\n8. **Use slim_optimize()** when only objective value needed for performance\n9. **Validate flux samples** to ensure numerical stability\n\n## Troubleshooting\n\n**Infeasible solutions**: Check medium constraints, reaction bounds, and model consistency\n**Slow optimization**: Try different solvers (GLPK, CPLEX, Gurobi) via `model.solver`\n**Unbounded solutions**: Verify exchange reactions have appropriate upper bounds\n**Import errors**: Ensure correct file format and valid SBML identifiers\n\n## References\n\nFor detailed workflows and API patterns, refer to:\n- `references\u002Fworkflows.md` - Comprehensive step-by-step workflow examples\n- `references\u002Fapi_quick_reference.md` - Common function signatures and patterns\n\nOfficial documentation: https:\u002F\u002Fcobrapy.readthedocs.io\u002Fen\u002Flatest\u002F\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fcobrapy",3963,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-15 01:32:48",{"source":8,"skillName":5,"commands":49,"prompt":59,"zipAvailable":60},[50,53,56],{"tool":51,"command":52},"npx","npx skills add davila7\u002Fclaude-code-templates --skill cobrapy",{"tool":54,"command":55},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill cobrapy",{"tool":57,"command":58},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill cobrapy","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fcobrapy\u002FSKILL.md",true,[62,68],{"path":63,"resourceType":64,"resourceTypeDesc":65,"sizeBytes":66,"sourceHtmlUrl":67},"references\u002Fapi_quick_reference.md","REFERENCE","参考文档",16036,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fcobrapy\u002Freferences\u002Fapi_quick_reference.md",{"path":69,"resourceType":64,"resourceTypeDesc":65,"sizeBytes":70,"sourceHtmlUrl":71},"references\u002Fworkflows.md",21741,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fcobrapy\u002Freferences\u002Fworkflows.md",[73,104,131,164,199,223],{"skillNo":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":84,"publishedAt":33},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[79],{"categoryNo":80,"categoryName":81,"taxonomyType":82,"taxonomyTypeDesc":83},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[85,86,87,88,89,30,90,91,92,93,94,95,96,97,98,99,100,101,102,103],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":105,"name":106,"nameZh":6,"description":107,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":108,"categories":109,"keywords":111,"publishedAt":33},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[110],{"categoryNo":80,"categoryName":81,"taxonomyType":82,"taxonomyTypeDesc":83},[112,113,114,115,116,117,118,119,120,121,122,93,123,124,125,126,127,128,129,130],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":146,"publishedAt":33},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[137,140,141],{"categoryNo":138,"categoryName":139,"taxonomyType":82,"taxonomyTypeDesc":83},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":80,"categoryName":81,"taxonomyType":82,"taxonomyTypeDesc":83},{"categoryNo":142,"categoryName":143,"taxonomyType":144,"taxonomyTypeDesc":145},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[147,148,149,150,151,152,153,30,154,155,156,157,158,159,160,92,161,162,93,163],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":168,"licenseFileUrl":169,"sourceHtmlUrl":170,"categories":171,"keywords":175,"publishedAt":33},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[172],{"categoryNo":173,"categoryName":174,"taxonomyType":82,"taxonomyTypeDesc":83},"01JBSEEDCAT000000000000009","前端开发",[176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,30,198],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":168,"licenseFileUrl":203,"sourceHtmlUrl":204,"categories":205,"keywords":210,"publishedAt":33},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[206,209],{"categoryNo":207,"categoryName":208,"taxonomyType":82,"taxonomyTypeDesc":83},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":173,"categoryName":174,"taxonomyType":82,"taxonomyTypeDesc":83},[211,212,213,214,215,177,216,186,187,217,218,219,220,30,221,95,222],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":224,"name":225,"nameZh":6,"description":226,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":168,"licenseFileUrl":227,"sourceHtmlUrl":228,"categories":229,"keywords":237,"publishedAt":33},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[230,233,234],{"categoryNo":231,"categoryName":232,"taxonomyType":82,"taxonomyTypeDesc":83},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":80,"categoryName":81,"taxonomyType":82,"taxonomyTypeDesc":83},{"categoryNo":235,"categoryName":236,"taxonomyType":144,"taxonomyTypeDesc":145},"01JBSEEDCAT000000000000036","内容创作者",[238,239,240,241,242,243,244,245,179,246,189,123,247,248,249,250,251,159,252,93,253,20,254,255,256,101,257],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","claims","argument","assemble","recommendations"]