Notes on the Python version of OpenCOBRA:

Implemented features

Flux analysis package:

Double deletion
MOMA
Essentiality (assess_medium_component_essentiality)
Objective (assess objective, update objective)
Single deletion
Variability (find blocked reactions, FVA)
I/O: Load Matlab model, save matlab model.
SBML: Create Cobra model from SBML, write Cobra model to SBML
Manipulate: Prune unused metabolites, prune unused reactions.
Modify: Initalize growth medium
mlab:
Solvers: cplex, glpk, gurobi.
Stats

MATLAB Cobra Toolbox Features

Cobra

MOMA
OMNI
Detect dead ends (metabolites which either participate in only one reaction or can only be produced or consumed)
Double robustness analysis
Dynamic FBA
Extract subnetwork
Find synthetic lethals
Find metabolic junctions
Find neighbor reactions
FVA
Geometric FBA
Linear MOMA
Optimize two models (minimize difference between flux vectors)
PhenotypePhasePlanes
Random objective FBA solution
Robustness analysis
Gap filling

Cobra/Design

Genetic Design Through Local Search
OptKnock
OptGene
Random KO
Simple OptKnock

Fluxomics

Gap-filling

Find orphan reactions
GapFind

I/O

Output Cytoscape network

Reconstruction

Add sink reactions
Check biomass precursor synthesis

CobraPy Missing features

Cobra Toolbox documentation for reference: http://opencobra.sourceforge.net/openCOBRA/opencobra_documentation/cobra_toolbox_2/index.html

OptKnock?
Fluxomics?
rFBA?

addRatioReaction? http://opencobra.sourceforge.net/openCOBRA/opencobra_documentation/cobra_toolbox_2/cobra/reconstruction/addRatioReaction.html

Basic commands

Import model create and write routines:

from cobra.io.sbml import create_cobra_model_from_sbml_file
from cobra.io.sbml import write_cobra_model_to_sbml_file

Tests:

from cobra.test import test_all

test_all()

Import sbml routines:

from cobra.io.sbml import create_cobra_model_from_sbml_file
from cobra.io.sbml import write_cobra_model_to_sbml_file

Create cobra model from SBML file:

cobra_model = create_cobra_model_from_sbml_file(sbml_file, print_time = True)

Save Cobra model as SBML file:

write_cobra_model_to_sbml_file(cobra_model, sbml_out_file, sbml_level, sbml_version, print_time = True)

Run optimization:

cobra_model.optimize(solver="gurobi")

Display result:

print '\nSimulated growth rate is %1.3f' % cobra_model.solution.f

Set growth medium:

from cobra.manipulation import initialize_growth_medium
initialize_growth_medium(cobra_model, 'LB')

Test effect of gene deletion on growth rates:

from cobra.flux_analysis import single_deletion
from cPickle import load
from time import time

from cobra.test import salmonella_pickle #This is the name of the test file
with open(salmonella_pickle) as in_file:
   cobra_model = load(in_file)

target_genes =  ['STM4081', 'STM0247', 'STM3867', 'STM2952']
rates, statuses, problems = single_deletion(cobra_model, target_genes)

Change objective function:

new_objective = cobra_model.reactions.get_by_id('3OAS140')
cobra_model.optimize(new_objective)

Add a reaction:

from cobra import Reaction
reaction = Reaction('My_reaction')
reaction.name = 'Testreaction'
reaction.subsystem = 'Central carbon metabolism'
reaction.lower_bound = 0. 
reaction.upper_bound = 1000.
reaction.reversibility = 0
reaction.objective_coefficient =0.

Creating metabolite:

from cobra import Metabolite
ACP_c = Metabolite('ACP_c', formula = 'C11H21N2O9PRS',name='acyl-carrier-protein', compartment='c')

Add metabolites to reaction:

reaction.add_metabolites({malACP_c: -1.0,h_c: -1.0}

Display reaction formula:

print reaction.reaction

Subpackages

See http://cobrapy.readthedocs.org/en/latest/cobra.html#

Core package

Metabolite module:

get_model()
get_reactions()
remove_from_model()

Model module:

add_metabolites(metabolite_list) #Adds metabolites to the model and expands the stoichiometric matrix.
add_reaction(reaction)
add_reactions(reaction_list)

Reaction module:

add_gene_reaction_rule(the_rule)

Adds a boolean gene requirement rule. See Schellenberger et al 2011 Nature Protocols 6(9):1290-307.

add_metabolites(the_metabolites, combine=True, add_to_container_model=True)

the_metabolites: dict of metabolites and stoichiometric coefficients
combine: If the metabolite already exists in the reaction, and combine = True, coefficients will be added together. If False, the old value is discarded.
add_to_container: If true and the reaction exists within a model object, the metabolite is added to the model.

get_products()

get_reactants()

optimize:

optimize(new_objective=None, objective_sense='maximize', the_problem=None, solver='glpk', error_reporting=None, quadratic_component=None, tolerance_optimality=1e-06, tolerance_feasibility=1e-06, tolerance_barrier=1e-10, **kwargs)

Flux analysis package

io package

manipulation package

Bibliography

Ebrahim A, Lerman JA, Palsson BO, Hyduke DR✝. COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Bio.: http://www.biomedcentral.com/1752-0509/7/74/abstract

Applications

GIM3E: http://bioinformatics.oxfordjournals.org/content/early/2013/08/22/bioinformatics.btt493.abstract

Todo

https://cobrapy.readthedocs.org/en/latest/_modules/cobra/core/Model.html?highlight=objective

8.2.5.2. delete Module

Mailing lists

https://groups.google.com/forum/#!forum/cobra-pie

https://groups.google.com/forum/#!forum/cobra-toolbox

User:Jarle Pahr/CobraPy

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