cobrapy

FreedomIntelligence/OpenClaw-Medical-SkillsBiology Medicine and BioinformaticsAudit pendingSnapshot fc4184fd9119

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SKILL.md

COBRApy - Constraint-Based Reconstruction and Analysis

Overview

COBRApy 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.

Core Capabilities

COBRApy provides comprehensive tools organized into several key areas:

1. Model Management

Load existing models from repositories or files:

from cobra.io import load_model

# Load bundled test models
model = load_model("textbook")  # E. coli core model
model = load_model("ecoli")     # Full E. coli model
model = load_model("salmonella")

# Load from files
from cobra.io import read_sbml_model, load_json_model, load_yaml_model
model = read_sbml_model("path/to/model.xml")
model = load_json_model("path/to/model.json")
model = load_yaml_model("path/to/model.yml")

Save models in various formats:

from cobra.io import write_sbml_model, save_json_model, save_yaml_model
write_sbml_model(model, "output.xml")  # Preferred format
save_json_model(model, "output.json")  # For Escher compatibility
save_yaml_model(model, "output.yml")   # Human-readable

2. Model Structure and Components

Access and inspect model components:

# Access components
model.reactions      # DictList of all reactions
model.metabolites    # DictList of all metabolites
model.genes          # DictList of all genes

# Get specific items by ID or index
reaction = model.reactions.get_by_id("PFK")
metabolite = model.metabolites[0]

# Inspect properties
print(reaction.reaction)        # Stoichiometric equation
print(reaction.bounds)          # Flux constraints
print(reaction.gene_reaction_rule)  # GPR logic
print(metabolite.formula)       # Chemical formula
print(metabolite.compartment)   # Cellular location

3. Flux Balance Analysis (FBA)

Perform standard FBA simulation:

# Basic optimization
solution = model.optimize()
print(f"Objective value: {solution.objective_value}")
print(f"Status: {solution.status}")

# Access fluxes
print(solution.fluxes["PFK"])
print(solution.fluxes.head())

# Fast optimization (objective value only)
objective_value = model.slim_optimize()

# Change objective
model.objective = "ATPM"
solution = model.optimize()

from cobra.flux_analysis import pfba
solution = pfba(model)

from cobra.flux_analysis import geometric_fba
solution = geometric_fba(model)

from cobra.flux_analysis import flux_variability_analysis

# Standard FVA
fva_result = flux_variability_analysis(model)

# FVA at 90% optimality
fva_result = flux_variability_analysis(model, fraction_of_optimum=0.9)

# Loopless FVA (eliminates thermodynamically infeasible loops)
fva_result = flux_variability_analysis(model, loopless=True)

# FVA for specific reactions
fva_result = flux_variability_analysis(
    model,
    reaction_list=["PFK", "FBA", "PGI"]
)

from cobra.flux_analysis import (
    single_gene_deletion,
    single_reaction_deletion,
    double_gene_deletion,
    double_reaction_deletion
)

# Single deletions
gene_results = single_gene_deletion(model)
reaction_results = single_reaction_deletion(model)

# Double deletions (uses multiprocessing)
double_gene_results = double_gene_deletion(
    model,
    processes=4  # Number of CPU cores
)

# Manual knockout using context manager
with model:
    model.genes.get_by_id("b0008").knock_out()
    solution = model.optimize()
    print(f"Growth after knockout: {solution.objective_value}")
# Model automatically reverts after context exit

# View current medium
print(model.medium)

# Modify medium (must reassign entire dict)
medium = model.medium
medium["EX_glc__D_e"] = 10.0  # Set glucose uptake
medium["EX_o2_e"] = 0.0       # Anaerobic conditions
model.medium = medium

# Calculate minimal media
from cobra.medium import minimal_medium

# Minimize total import flux
min_medium = minimal_medium(model, minimize_components=False)

# Minimize number of components (uses MILP, slower)
min_medium = minimal_medium(
    model,
    minimize_components=True,
    open_exchanges=True
)

from cobra.sampling import sample

# Sample using OptGP (default, supports parallel processing)
samples = sample(model, n=1000, method="optgp", processes=4)

# Sample using ACHR
samples = sample(model, n=1000, method="achr")

# Validate samples
from cobra.sampling import OptGPSampler
sampler = OptGPSampler(model, processes=4)
sampler.sample(1000)
validation = sampler.validate(sampler.samples)
print(validation.value_counts())  # Should be all 'v' for valid

from cobra.flux_analysis import production_envelope

# Standard production envelope
envelope = production_envelope(
    model,
    reactions=["EX_glc__D_e", "EX_o2_e"],
    objective="EX_ac_e"  # Acetate production
)

# With carbon yield
envelope = production_envelope(
    model,
    reactions=["EX_glc__D_e", "EX_o2_e"],
    carbon_sources="EX_glc__D_e"
)

# Visualize (use matplotlib or pandas plotting)
import matplotlib.pyplot as plt
envelope.plot(x="EX_glc__D_e", y="EX_o2_e", kind="scatter")
plt.show()

from cobra.flux_analysis import gapfill

# Prepare universal model with candidate reactions
universal = load_model("universal")

# Perform gapfilling
with model:
    # Remove reactions to create gaps for demonstration
    model.remove_reactions([model.reactions.PGI])

    # Find reactions needed
    solution = gapfill(model, universal)
    print(f"Reactions to add: {solution}")

from cobra import Model, Reaction, Metabolite

# Create model
model = Model("my_model")

# Create metabolites
atp_c = Metabolite("atp_c", formula="C10H12N5O13P3",
                   name="ATP", compartment="c")
adp_c = Metabolite("adp_c", formula="C10H12N5O10P2",
                   name="ADP", compartment="c")
pi_c = Metabolite("pi_c", formula="HO4P",
                  name="Phosphate", compartment="c")

# Create reaction
reaction = Reaction("ATPASE")
reaction.name = "ATP hydrolysis"
reaction.subsystem = "Energy"
reaction.lower_bound = 0.0
reaction.upper_bound = 1000.0

# Add metabolites with stoichiometry
reaction.add_metabolites({
    atp_c: -1.0,
    adp_c: 1.0,
    pi_c: 1.0
})

# Add gene-reaction rule
reaction.gene_reaction_rule = "(gene1 and gene2) or gene3"

# Add to model
model.add_reactions([reaction])

# Add boundary reactions
model.add_boundary(atp_c, type="exchange")
model.add_boundary(adp_c, type="demand")

# Set objective
model.objective = "ATPASE"

from cobra.io import load_model

# Load model
model = load_model("ecoli")

# Run FBA
solution = model.optimize()
print(f"Growth rate: {solution.objective_value:.3f} /h")

# Show active pathways
print(solution.fluxes[solution.fluxes.abs() > 1e-6])

from cobra.io import load_model
from cobra.flux_analysis import single_gene_deletion

# Load model
model = load_model("ecoli")

# Perform single gene deletions
results = single_gene_deletion(model)

# Find essential genes (growth < threshold)
essential_genes = results[results["growth"] < 0.01]
print(f"Found {len(essential_genes)} essential genes")

# Find genes with minimal impact
neutral_genes = results[results["growth"] > 0.9 * solution.objective_value]

from cobra.io import load_model
from cobra.medium import minimal_medium

# Load model
model = load_model("ecoli")

# Calculate minimal medium for 50% of max growth
target_growth = model.slim_optimize() * 0.5
min_medium = minimal_medium(
    model,
    target_growth,
    minimize_components=True
)

print(f"Minimal medium components: {len(min_medium)}")
print(min_medium)

from cobra.io import load_model
from cobra.flux_analysis import flux_variability_analysis
from cobra.sampling import sample

# Load model
model = load_model("ecoli")

# First check flux ranges at optimality
fva = flux_variability_analysis(model, fraction_of_optimum=1.0)

# For reactions with large ranges, sample to understand distribution
samples = sample(model, n=1000)

# Analyze specific reaction
reaction_id = "PFK"
import matplotlib.pyplot as plt
samples[reaction_id].hist(bins=50)
plt.xlabel(f"Flux through {reaction_id}")
plt.ylabel("Frequency")
plt.show()

# Model remains unchanged outside context
with model:
    # Temporarily change objective
    model.objective = "ATPM"

    # Temporarily modify bounds
    model.reactions.EX_glc__D_e.lower_bound = -5.0

    # Temporarily knock out genes
    model.genes.b0008.knock_out()

    # Optimize with changes
    solution = model.optimize()
    print(f"Modified growth: {solution.objective_value}")

# All changes automatically reverted
solution = model.optimize()
print(f"Original growth: {solution.objective_value}")

# Access by index
first_reaction = model.reactions[0]

# Access by ID
pfk = model.reactions.get_by_id("PFK")

# Query methods
atp_reactions = model.reactions.query("atp")

# AND logic (both required)
reaction.gene_reaction_rule = "gene1 and gene2"

# OR logic (either sufficient)
reaction.gene_reaction_rule = "gene1 or gene2"

# Complex logic
reaction.gene_reaction_rule = "(gene1 and gene2) or (gene3 and gene4)"

cobrapy

Summary

SKILL.md

COBRApy - Constraint-Based Reconstruction and Analysis

Overview

Core Capabilities

1. Model Management

2. Model Structure and Components

3. Flux Balance Analysis (FBA)

4. Flux Variability Analysis (FVA)

5. Gene and Reaction Deletion Studies

6. Growth Media and Minimal Media

7. Flux Sampling

8. Production Envelopes

9. Gapfilling

10. Model Building

Common Workflows

Workflow 1: Load Model and Predict Growth

Workflow 2: Gene Knockout Screen

Workflow 3: Media Optimization

Workflow 4: Flux Uncertainty Analysis

Workflow 5: Context Manager for Temporary Changes

Key Concepts

DictList Objects

Flux Constraints

Gene-Reaction Rules (GPR)

Exchange Reactions

Best Practices

Troubleshooting

References

Related skills