def EssentialReactions(self,tol=1e-10):
sdel = single_deletion(self, element_type='reaction')
rv = []
for reac in sdel[1]:
if sdel[1][reac] == "infeasible":
rv.append(reac)
return rv
def EssentialGenes(self,tol=1e-10):
sdel = single_deletion(self, element_type='gene')
rv = []
for gene in sdel[1]:
if sdel[1][gene] == "infeasible":
rv.append(gene)
return rv
def pruneModel(model, rxnToRemove_dict, solver_arg):
rxnToRemoveEssn_dict = {}
rxnRemoved_dict = {}
rxnRetained_dict = {}
for rxnid in rxnToRemove_dict.keys():
#Single reaction deletion is performed only for reactions labelled as "False"
if rxnToRemove_dict[rxnid] == False:
growth_rate_dict, solution_status_dict, problem_dict = single_deletion(
model,
list([rxnid]),
element_type='reaction',
solver=solver_arg)
#Checks optimality first.
if str(solution_status_dict.values()[0]) == 'optimal':
#Full list of reactions and predicted growth rates upon their deletions
rxnToRemoveEssn_dict[rxnid] = float(
growth_rate_dict.values()[0])
#Checks growth rate upon reaction deletion
if float(growth_rate_dict.values()[0]) >= 0.01:
model.remove_reactions(rxnid)
#List of reactions removed from the template model
rxnRemoved_dict[rxnid] = float(
growth_rate_dict.values()[0])
logging.debug("Removed reaction: %s, %s, %s, %s", rxnid,
growth_rate_dict.values()[0],
len(model.reactions), len(model.metabolites))
else:
#List of reactions retained in the template model
rxnRetained_dict[rxnid] = float(
growth_rate_dict.values()[0])
logging.debug("Retained reaction: %s, %s, %s, %s", rxnid,
growth_rate_dict.values()[0],
len(model.reactions), len(model.metabolites))
#Removing metabolites that are not used in the reduced model
prune_unused_metabolites(model)
modelPruned = copy.deepcopy(model)
return modelPruned, rxnToRemoveEssn_dict, rxnRemoved_dict, rxnRetained_dict
from cobra.test import create_test_model, salmonella_pickle, ecoli_pickle
from cobra.flux_analysis import single_deletion
from cobra.flux_analysis import double_deletion
cobra_model = create_test_model(salmonella_pickle)
### Single Deletions
# Perform all single gene deletions on a model
start = time() # start timer()
growth_rates, statuses = single_deletion(cobra_model)
print("All single gene deletions completed in %.2f sec" % (time() - start))
# Prints:
# All single gene deletions completed in 4.01 sec
# These can also be done for only a subset of genes
single_deletion(cobra_model, element_list=cobra_model.genes[:100])
# Single deletions can also be run on reactions
start = time() # start timer()
growth_rates, statuses = single_deletion(cobra_model, element_type="reaction")
print("All single reaction deletions completed in %.2f sec" % (time() - start))
# Prints:
from math import floor
from cobra.manipulation import initialize_growth_medium
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)
initialize_growth_medium(cobra_model, 'LB')
#Expected growth rates for the salmonella model with deletions in LB medium
the_loci = ['STM4081', 'STM0247', 'STM3867', 'STM2952']
the_genes = tpiA, metN, atpA, eno = map(cobra_model.genes.get_by_id, the_loci)
id_to_name = dict([(x.id, x.name) for x in the_genes])
the_growth_rates = {tpiA.id:2.41, metN.id:2.43, atpA.id:1.87, eno.id:1.81} #expected growth rates after deletion
#Perform a single gene deletion
the_results = single_deletion(cobra_model, [tpiA])
gene_list = the_growth_rates.keys()
#Perform deletions for all genes in the list
start_time = time()
rates, statuses, problems = single_deletion(cobra_model,
gene_list)
for the_gene, v in statuses.items():
if v != 'optimal':
print '\t\tdeletion %s was not optimal'%the_gene
for the_gene, v in rates.items():
v = floor(100*v)/100
if v != the_growth_rates[the_gene]:
print '\t\tFAILED: %s simulation (%1.3f) != expectation (%1.2f)'%(id_to_name[the_gene],
cobra_model = create_test_model(salmonella_pickle)
initialize_growth_medium(cobra_model, 'LB')
target_genes = ['STM4081', 'STM0247', 'STM3867', 'STM2952']
# Expected growth rates for the salmonella model after a deletions in LB medium
expected_growth_rates = {
"STM4081": 2.41,
"STM0247": 2.43,
"STM3867": 1.87,
"STM2952": 1.81}
start_time = time() # start timer
# Perform deletions for all genes in the list
rates, statuses, problems = single_deletion(cobra_model, target_genes)
total_time = time() - start_time # stop timer
# print out results
passed_string = 'PASSED: %s simulation (%1.3f) ~= expectation (%1.2f)'
failed_string = 'FAILED: %s simulation (%1.3f) != expectation (%1.2f)'
for gene_locus, rate in rates.items():
# get gene name from gene locus (i.e. STM4081 -> tpiA)
name = cobra_model.genes.get_by_id(gene_locus).name
# test if the simulation failed
if statuses[gene_locus] != "optimal":
print "deletion failed for %s (%s)" % (name, gene_locus)
if abs(rate - expected_growth_rates[gene_locus]) > 0.01:
print failed_string % (name, rate, expected_growth_rates[gene_locus])
else:
cobra_model = create_test_model(salmonella_pickle)
initialize_growth_medium(cobra_model, 'LB')
target_genes = ['STM4081', 'STM0247', 'STM3867', 'STM2952']
# Expected growth rates for the salmonella model after a deletions in LB medium
expected_growth_rates = {
"STM4081": 2.41,
"STM0247": 2.43,
"STM3867": 1.87,
"STM2952": 1.81
}
start_time = time() # start timer
# Perform deletions for all genes in the list
rates, statuses, problems = single_deletion(cobra_model, target_genes)
total_time = time() - start_time # stop timer
# print out results
passed_string = 'PASSED: %s simulation (%1.3f) ~= expectation (%1.2f)'
failed_string = 'FAILED: %s simulation (%1.3f) != expectation (%1.2f)'
for gene_locus, rate in rates.items():
# get gene name from gene locus (i.e. STM4081 -> tpiA)
name = cobra_model.genes.get_by_id(gene_locus).name
# test if the simulation failed
if statuses[gene_locus] != "optimal":
print "deletion failed for %s (%s)" % (name, gene_locus)
if abs(rate - expected_growth_rates[gene_locus]) > 0.01:
print failed_string % (name, rate, expected_growth_rates[gene_locus])
else:
def SingleDeletion(self, element_list=None,method='fba', element_type='gene', solver=None):
return single_deletion(self, element_list=element_list, method=method, element_type=element_type, solver=solver)
# .
from time import time
from cobra.test import create_test_model, salmonella_pickle, ecoli_pickle
from cobra.flux_analysis import single_deletion
from cobra.flux_analysis import double_deletion
cobra_model = create_test_model(salmonella_pickle)
### Single Deletions
# Perform all single gene deletions on a model
start = time() # start timer()
growth_rates, statuses = single_deletion(cobra_model)
print("All single gene deletions completed in %.2f sec" % (time() - start))
# Prints:
# All single gene deletions completed in 4.01 sec
# These can also be done for only a subset of genes
single_deletion(cobra_model, element_list=cobra_model.genes[:100])
# Single deletions can also be run on reactions
start = time() # start timer()
growth_rates, statuses = single_deletion(cobra_model, element_type="reaction")
print("All single reaction deletions completed in %.2f sec" % (time() - start))
# Prints:
# All single reaction deletions completed in 7.41 sec
