gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/cobrapy/references/api_quick_reference.md

COBRApy API Quick Reference

This document provides quick reference for common COBRApy functions, signatures, and usage patterns.

Model I/O

Loading Models

from cobra.io import load_model, read_sbml_model, load_json_model, load_yaml_model, load_matlab_model

# Bundled test models
model = load_model("textbook")   # E. coli core metabolism
model = load_model("ecoli")      # Full E. coli iJO1366
model = load_model("salmonella") # Salmonella LT2

# From files
model = read_sbml_model(filename, f_replace={}, **kwargs)
model = load_json_model(filename)
model = load_yaml_model(filename)
model = load_matlab_model(filename, variable_name=None)

Saving Models

from cobra.io import write_sbml_model, save_json_model, save_yaml_model, save_matlab_model

write_sbml_model(model, filename, f_replace={}, **kwargs)
save_json_model(model, filename, pretty=False, **kwargs)
save_yaml_model(model, filename, **kwargs)
save_matlab_model(model, filename, **kwargs)

Model Structure

Core Classes

from cobra import Model, Reaction, Metabolite, Gene

# Create model
model = Model(id_or_model=None, name=None)

# Create metabolite
metabolite = Metabolite(
    id=None,
    formula=None,
    name="",
    charge=None,
    compartment=None
)

# Create reaction
reaction = Reaction(
    id=None,
    name="",
    subsystem="",
    lower_bound=0.0,
    upper_bound=None
)

# Create gene
gene = Gene(id=None, name="", functional=True)

Model Attributes

# Component access (DictList objects)
model.reactions       # DictList of Reaction objects
model.metabolites     # DictList of Metabolite objects
model.genes          # DictList of Gene objects

# Special reaction lists
model.exchanges      # Exchange reactions (external transport)
model.demands        # Demand reactions (metabolite sinks)
model.sinks          # Sink reactions
model.boundary       # All boundary reactions

# Model properties
model.objective      # Current objective (read/write)
model.objective_direction  # "max" or "min"
model.medium         # Growth medium (dict of exchange: bound)
model.solver         # Optimization solver

DictList Methods

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

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

# Query by string (substring match)
items = model.reactions.query("atp")      # Case-insensitive search
items = model.reactions.query(lambda x: x.subsystem == "Glycolysis")

# List comprehension
items = [r for r in model.reactions if r.lower_bound < 0]

# Check membership
"PFK" in model.reactions

Optimization

Basic Optimization

# Full optimization (returns Solution object)
solution = model.optimize()

# Attributes of Solution
solution.objective_value   # Objective function value
solution.status           # Optimization status ("optimal", "infeasible", etc.)
solution.fluxes          # Pandas Series of reaction fluxes
solution.shadow_prices   # Pandas Series of metabolite shadow prices
solution.reduced_costs   # Pandas Series of reduced costs

# Fast optimization (returns float only)
objective_value = model.slim_optimize()

# Change objective
model.objective = "ATPM"
model.objective = model.reactions.ATPM
model.objective = {model.reactions.ATPM: 1.0}

# Change optimization direction
model.objective_direction = "max"  # or "min"

Solver Configuration

# Check available solvers
from cobra.util.solver import solvers
print(solvers)

# Change solver
model.solver = "glpk"  # or "cplex", "gurobi", etc.

# Solver-specific configuration
model.solver.configuration.timeout = 60  # seconds
model.solver.configuration.verbosity = 1
model.solver.configuration.tolerances.feasibility = 1e-9

Flux Analysis

Flux Balance Analysis (FBA)

from cobra.flux_analysis import pfba, geometric_fba

# Parsimonious FBA
solution = pfba(model, fraction_of_optimum=1.0, **kwargs)

# Geometric FBA
solution = geometric_fba(model, epsilon=1e-06, max_tries=200)

Flux Variability Analysis (FVA)

from cobra.flux_analysis import flux_variability_analysis

fva_result = flux_variability_analysis(
    model,
    reaction_list=None,        # List of reaction IDs or None for all
    loopless=False,            # Eliminate thermodynamically infeasible loops
    fraction_of_optimum=1.0,   # Optimality fraction (0.0-1.0)
    pfba_factor=None,          # Optional pFBA constraint
    processes=1                # Number of parallel processes
)

# Returns DataFrame with columns: minimum, maximum

Gene and Reaction Deletions

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

# Single deletions
results = single_gene_deletion(
    model,
    gene_list=None,     # None for all genes
    processes=1,
    **kwargs
)

results = single_reaction_deletion(
    model,
    reaction_list=None,  # None for all reactions
    processes=1,
    **kwargs
)

# Double deletions
results = double_gene_deletion(
    model,
    gene_list1=None,
    gene_list2=None,
    processes=1,
    **kwargs
)

results = double_reaction_deletion(
    model,
    reaction_list1=None,
    reaction_list2=None,
    processes=1,
    **kwargs
)

# Returns DataFrame with columns: ids, growth, status
# For double deletions, index is MultiIndex of gene/reaction pairs

Flux Sampling

from cobra.sampling import sample, OptGPSampler, ACHRSampler

# Simple interface
samples = sample(
    model,
    n,                  # Number of samples
    method="optgp",     # or "achr"
    thinning=100,       # Thinning factor (sample every n iterations)
    processes=1,        # Parallel processes (OptGP only)
    seed=None          # Random seed
)

# Advanced interface with sampler objects
sampler = OptGPSampler(model, processes=4, thinning=100)
sampler = ACHRSampler(model, thinning=100)

# Generate samples
samples = sampler.sample(n)

# Validate samples
validation = sampler.validate(sampler.samples)
# Returns array of 'v' (valid), 'l' (lower bound violation),
# 'u' (upper bound violation), 'e' (equality violation)

# Batch sampling
sampler.batch(n_samples, n_batches)

Production Envelopes

from cobra.flux_analysis import production_envelope

envelope = production_envelope(
    model,
    reactions,              # List of 1-2 reaction IDs
    objective=None,         # Objective reaction ID (None uses model objective)
    carbon_sources=None,    # Carbon source for yield calculation
    points=20,              # Number of points to calculate
    threshold=0.01          # Minimum objective value threshold
)

# Returns DataFrame with columns:
# - First reaction flux
# - Second reaction flux (if provided)
# - objective_minimum, objective_maximum
# - carbon_yield_minimum, carbon_yield_maximum (if carbon source specified)
# - mass_yield_minimum, mass_yield_maximum

Gapfilling

from cobra.flux_analysis import gapfill

# Basic gapfilling
solution = gapfill(
    model,
    universal=None,         # Universal model with candidate reactions
    lower_bound=0.05,       # Minimum objective flux
    penalties=None,         # Dict of reaction: penalty
    demand_reactions=True,  # Add demand reactions if needed
    exchange_reactions=False,
    iterations=1
)

# Returns list of Reaction objects to add

# Multiple solutions
solutions = []
for i in range(5):
    sol = gapfill(model, universal, iterations=1)
    solutions.append(sol)
    # Prevent finding same solution by increasing penalties

Other Analysis Methods

from cobra.flux_analysis import (
    find_blocked_reactions,
    find_essential_genes,
    find_essential_reactions
)

# Blocked reactions (cannot carry flux)
blocked = find_blocked_reactions(
    model,
    reaction_list=None,
    zero_cutoff=1e-9,
    open_exchanges=False
)

# Essential genes/reactions
essential_genes = find_essential_genes(model, threshold=0.01)
essential_reactions = find_essential_reactions(model, threshold=0.01)

Media and Boundary Conditions

Medium Management

# Get current medium (returns dict)
medium = model.medium

# Set medium (must reassign entire dict)
medium = model.medium
medium["EX_glc__D_e"] = 10.0
medium["EX_o2_e"] = 20.0
model.medium = medium

# Alternative: individual modification
with model:
    model.reactions.EX_glc__D_e.lower_bound = -10.0

Minimal Media

from cobra.medium import minimal_medium

min_medium = minimal_medium(
    model,
    min_objective_value=0.1,  # Minimum growth rate
    minimize_components=False, # If True, uses MILP (slower)
    open_exchanges=False,      # Open all exchanges before optimization
    exports=False,             # Allow metabolite export
    penalties=None             # Dict of exchange: penalty
)

# Returns Series of exchange reactions with fluxes

Boundary Reactions

# Add boundary reaction
model.add_boundary(
    metabolite,
    type="exchange",    # or "demand", "sink"
    reaction_id=None,   # Auto-generated if None
    lb=None,
    ub=None,
    sbo_term=None
)

# Access boundary reactions
exchanges = model.exchanges     # System boundary
demands = model.demands         # Intracellular removal
sinks = model.sinks            # Intracellular exchange
boundaries = model.boundary    # All boundary reactions

Model Manipulation

Adding Components

# Add reactions
model.add_reactions([reaction1, reaction2, ...])
model.add_reaction(reaction)

# Add metabolites
reaction.add_metabolites({
    metabolite1: -1.0,  # Consumed (negative stoichiometry)
    metabolite2: 1.0    # Produced (positive stoichiometry)
})

# Add metabolites to model
model.add_metabolites([metabolite1, metabolite2, ...])

# Add genes (usually automatic via gene_reaction_rule)
model.genes += [gene1, gene2, ...]

Removing Components

# Remove reactions
model.remove_reactions([reaction1, reaction2, ...])
model.remove_reactions(["PFK", "FBA"])

# Remove metabolites (removes from reactions too)
model.remove_metabolites([metabolite1, metabolite2, ...])

# Remove genes (usually via gene_reaction_rule)
model.genes.remove(gene)

Modifying Reactions

# Set bounds
reaction.bounds = (lower, upper)
reaction.lower_bound = 0.0
reaction.upper_bound = 1000.0

# Modify stoichiometry
reaction.add_metabolites({metabolite: 1.0})
reaction.subtract_metabolites({metabolite: 1.0})

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

# Knock out
reaction.knock_out()
gene.knock_out()

Model Copying

# Deep copy (independent model)
model_copy = model.copy()

# Copy specific reactions
new_model = Model("subset")
reactions_to_copy = [model.reactions.PFK, model.reactions.FBA]
new_model.add_reactions(reactions_to_copy)

Context Management

Use context managers for temporary modifications:

# Changes automatically revert after with block
with model:
    model.objective = "ATPM"
    model.reactions.EX_glc__D_e.lower_bound = -5.0
    model.genes.b0008.knock_out()
    solution = model.optimize()

# Model state restored here

# Multiple nested contexts
with model:
    model.objective = "ATPM"
    with model:
        model.genes.b0008.knock_out()
        # Both modifications active
    # Only objective change active

# Context management with reactions
with model:
    model.reactions.PFK.knock_out()
    # Equivalent to: reaction.lower_bound = reaction.upper_bound = 0

Reaction and Metabolite Properties

Reaction Attributes

reaction.id                      # Unique identifier
reaction.name                    # Human-readable name
reaction.subsystem               # Pathway/subsystem
reaction.bounds                  # (lower_bound, upper_bound)
reaction.lower_bound
reaction.upper_bound
reaction.reversibility          # Boolean (lower_bound < 0)
reaction.gene_reaction_rule     # GPR string
reaction.genes                  # Set of associated Gene objects
reaction.metabolites            # Dict of {metabolite: stoichiometry}

# Methods
reaction.reaction               # Stoichiometric equation string
reaction.build_reaction_string() # Same as above
reaction.check_mass_balance()   # Returns imbalances or empty dict
reaction.get_coefficient(metabolite_id)
reaction.add_metabolites({metabolite: coeff})
reaction.subtract_metabolites({metabolite: coeff})
reaction.knock_out()

Metabolite Attributes

metabolite.id                   # Unique identifier
metabolite.name                 # Human-readable name
metabolite.formula              # Chemical formula
metabolite.charge               # Charge
metabolite.compartment          # Compartment ID
metabolite.reactions            # FrozenSet of associated reactions

# Methods
metabolite.summary()            # Print production/consumption
metabolite.copy()

Gene Attributes

gene.id                         # Unique identifier
gene.name                       # Human-readable name
gene.functional                 # Boolean activity status
gene.reactions                  # FrozenSet of associated reactions

# Methods
gene.knock_out()

Model Validation

Consistency Checking

from cobra.manipulation import check_mass_balance, check_metabolite_compartment_formula

# Check all reactions for mass balance
unbalanced = {}
for reaction in model.reactions:
    balance = reaction.check_mass_balance()
    if balance:
        unbalanced[reaction.id] = balance

# Check metabolite formulas are valid
check_metabolite_compartment_formula(model)

Model Statistics

# Basic stats
print(f"Reactions: {len(model.reactions)}")
print(f"Metabolites: {len(model.metabolites)}")
print(f"Genes: {len(model.genes)}")

# Advanced stats
print(f"Exchanges: {len(model.exchanges)}")
print(f"Demands: {len(model.demands)}")

# Blocked reactions
from cobra.flux_analysis import find_blocked_reactions
blocked = find_blocked_reactions(model)
print(f"Blocked reactions: {len(blocked)}")

# Essential genes
from cobra.flux_analysis import find_essential_genes
essential = find_essential_genes(model)
print(f"Essential genes: {len(essential)}")

Summary Methods

# Model summary
model.summary()                  # Overall model info

# Metabolite summary
model.metabolites.atp_c.summary()

# Reaction summary
model.reactions.PFK.summary()

# Summary with FVA
model.summary(fva=0.95)         # Include FVA at 95% optimality

Common Patterns

Batch Analysis Pattern

results = []
for condition in conditions:
    with model:
        # Apply condition
        setup_condition(model, condition)

        # Analyze
        solution = model.optimize()

        # Store result
        results.append({
            "condition": condition,
            "growth": solution.objective_value,
            "status": solution.status
        })

df = pd.DataFrame(results)

Systematic Knockout Pattern

knockout_results = []
for gene in model.genes:
    with model:
        gene.knock_out()

        solution = model.optimize()

        knockout_results.append({
            "gene": gene.id,
            "growth": solution.objective_value if solution.status == "optimal" else 0,
            "status": solution.status
        })

df = pd.DataFrame(knockout_results)

Parameter Scan Pattern

parameter_values = np.linspace(0, 20, 21)
results = []

for value in parameter_values:
    with model:
        model.reactions.EX_glc__D_e.lower_bound = -value

        solution = model.optimize()

        results.append({
            "glucose_uptake": value,
            "growth": solution.objective_value,
            "acetate_secretion": solution.fluxes["EX_ac_e"]
        })

df = pd.DataFrame(results)

This quick reference covers the most commonly used COBRApy functions and patterns. For complete API documentation, see https://cobrapy.readthedocs.io/