def _gene_knockout_computation(m: Model, gene_ids: List[str],
expected_reaction_ids: List[str]) -> None:
"""Compute gene knockout."""
genes = [m.genes.get_by_id(i) for i in gene_ids]
expected_reactions = {
m.reactions.get_by_id(i)
for i in expected_reaction_ids
}
removed1 = set(find_gene_knockout_reactions(m, genes))
removed2 = set(_find_gene_knockout_reactions_fast(m, genes))
assert removed1 == expected_reactions
assert removed2 == expected_reactions
delete_model_genes(m, gene_ids, cumulative_deletions=False)
assert _get_removed(m) == expected_reaction_ids
undelete_model_genes(m)
def test_gene_knockout(salmonella: Model) -> None:
"""Test gene knockout."""
gene_list = ["STM1067", "STM0227"]
dependent_reactions = {
"3HAD121",
"3HAD160",
"3HAD80",
"3HAD140",
"3HAD180",
"3HAD100",
"3HAD181",
"3HAD120",
"3HAD60",
"3HAD141",
"3HAD161",
"T2DECAI",
"3HAD40",
}
_gene_knockout_computation(salmonella, gene_list, dependent_reactions)
_gene_knockout_computation(salmonella, ["STM4221"], {"PGI"})
_gene_knockout_computation(salmonella, ["STM1746.S"], {"4PEPTabcpp"})
# test cumulative behavior
delete_model_genes(salmonella, gene_list[:1])
delete_model_genes(salmonella, gene_list[1:], cumulative_deletions=True)
delete_model_genes(salmonella, ["STM4221"], cumulative_deletions=True)
dependent_reactions.add("PGI")
assert _get_removed(salmonella) == dependent_reactions
# non-cumulative following cumulative
delete_model_genes(salmonella, ["STM4221"], cumulative_deletions=False)
assert _get_removed(salmonella) == {"PGI"}
# make sure on reset that the bounds are correct
reset_bound = salmonella.reactions.get_by_id("T2DECAI").upper_bound
assert reset_bound == 1000.0
# test computation when gene name is a subset of another
test_model = Model()
test_reaction_1 = Reaction("test1")
test_reaction_1.gene_reaction_rule = "eggs or (spam and eggspam)"
test_model.add_reactions([test_reaction_1])
_gene_knockout_computation(test_model, ["eggs"], set())
_gene_knockout_computation(test_model, ["eggs", "spam"], {"test1"})
# test computation with nested boolean expression
test_reaction_1.gene_reaction_rule = "g1 and g2 and (g3 or g4 or (g5 and g6))"
_gene_knockout_computation(test_model, ["g3"], set())
_gene_knockout_computation(test_model, ["g1"], {"test1"})
_gene_knockout_computation(test_model, ["g5"], set())
_gene_knockout_computation(test_model, ["g3", "g4", "g5"], {"test1"})
# test computation when gene names are python expressions
test_reaction_1.gene_reaction_rule = "g1 and (for or in)"
_gene_knockout_computation(test_model, ["for", "in"], {"test1"})
_gene_knockout_computation(test_model, ["for"], set())
test_reaction_1.gene_reaction_rule = "g1 and g2 and g2.conjugate"
_gene_knockout_computation(test_model, ["g2"], {"test1"})
_gene_knockout_computation(test_model, ["g2.conjugate"], {"test1"})
test_reaction_1.gene_reaction_rule = "g1 and (try:' or 'except:1)"
_gene_knockout_computation(test_model, ["try:'"], set())
_gene_knockout_computation(test_model, ["try:'", "'except:1"], {"test1"})
def single_gene_deletion_moma(cobra_model, gene_list, solver=None,
**solver_args):
"""Sequentially knocks out each gene in a model using MOMA.
Not supposed to be called directly use
`single_reactions_deletion(..., method="moma")` instead.
Parameters
----------
gene_list : iterable
List of gene IDs or cobra.Reaction.
solver: str, optional
The name of the solver to be used.
Returns
-------
tuple of dicts
A tuple ({reaction_id: growth_rate}, {reaction_id: status})
"""
if moma is None:
raise RuntimeError("scipy required for moma")
legacy = False
if solver is None:
solver = cobra_model.solver
elif "optlang-" in solver:
solver = solvers.interface_to_str(solver)
solver = solvers.solvers[solver]
else:
legacy = True
solver = legacy_solvers.solver_dict[solver]
moma_model, moma_objective = moma.create_euclidian_moma_model(
cobra_model)
growth_rate_dict = {}
status_dict = {}
if not legacy:
with cobra_model as m:
m.solver = solver
moma.add_moma(m)
for gene in gene_list:
ko = find_gene_knockout_reactions(cobra_model, [gene])
with m:
for reaction in ko:
reaction.bounds = (0.0, 0.0)
m.solver.optimize()
status = m.solver.status
status_dict[gene.id] = status
if status == "optimal":
growth = m.variables.moma_old_objective.primal
else:
growth = 0.0
growth_rate_dict[gene.id] = growth
else:
for gene in gene_list:
delete_model_genes(moma_model, [gene.id])
solution = moma.solve_moma_model(moma_model, moma_objective,
solver=solver, **solver_args)
status_dict[gene.id] = solution.status
growth_rate_dict[gene.id] = solution.f
undelete_model_genes(moma_model)
return growth_rate_dict, status_dict
def double_gene_deletion_moma(cobra_model, gene_list_1=None, gene_list_2=None,
method='moma', single_deletion_growth_dict=None,
solver='glpk', growth_tolerance=1e-8,
error_reporting=None):
"""This will disable reactions for all gene pairs from gene_list_1 and
gene_list_2 and then run simulations to optimize for the objective
function. The contribution of each reaction to the objective function
is indicated in cobra_model.reactions[:].objective_coefficient vector.
NOTE: We've assumed that there is no such thing as a synthetic rescue with
this modeling framework.
cobra_model: a cobra.Model object
gene_list_1: Is None or a list of genes. If None then both gene_list_1
and gene_list_2 are assumed to correspond to cobra_model.genes.
gene_list_2: Is None or a list of genes. If None then gene_list_2 is
assumed to correspond to gene_list_1.
method: 'fba' or 'moma' to run flux balance analysis or minimization
of metabolic adjustments.
single_deletion_growth_dict: A dictionary that provides the growth
rate information for single gene knock outs. This can speed up
simulations because nonviable single deletion strains imply that all
double deletion strains will also be nonviable.
solver: 'glpk', 'gurobi', or 'cplex'.
error_reporting: None or True
growth_tolerance: float. The effective lower bound on the growth rate
for a single deletion that is still considered capable of growth.
Returns a dictionary of the gene ids in the x dimension (x) and the y
dimension (y), and the growth simulation data (data).
"""
#BUG: Since this might be called from ppmap, the modules need to
#be imported. Modify ppmap to take depfuncs
from numpy import zeros
nan = float('nan')
from cobra.flux_analysis.single_deletion import single_deletion
from cobra.manipulation import delete_model_genes, undelete_model_genes
##TODO: Use keywords instead
if isinstance(cobra_model, dict):
tmp_dict = cobra_model
cobra_model = tmp_dict['cobra_model']
if 'gene_list_1' in tmp_dict:
gene_list_1 = tmp_dict['gene_list_1']
if 'gene_list_2' in tmp_dict:
gene_list_2 = tmp_dict['gene_list_2']
if 'method' in tmp_dict:
method = tmp_dict['method']
if 'single_deletion_growth_dict' in tmp_dict:
single_deletion_growth_dict = tmp_dict['single_deletion_growth_dict']
if 'solver' in tmp_dict:
solver = tmp_dict['solver']
if 'error_reporting' in tmp_dict:
error_reporting = tmp_dict['error_reporting']
else:
cobra_model = cobra_model
#this is a slow way to revert models.
wt_model = cobra_model #NOTE: It may no longer be necessary to use a wt_model
#due to undelete_model_genes
if gene_list_1 is None:
gene_list_1 = cobra_model.genes
elif not hasattr(gene_list_1[0], 'id'):
gene_list_1 = map(cobra_model.genes.get_by_id, gene_list_1)
#Get default values to use if the deletions do not alter any reactions
cobra_model.optimize(solver=solver)
basal_f = cobra_model.solution.f
if method.lower() == 'moma':
wt_model = cobra_model.copy()
combined_model = None
single_gene_set = set(gene_list_1)
if gene_list_2 is not None:
if not hasattr(gene_list_2[0], 'id'):
gene_list_2 = map(cobra_model.genes.get_by_id, gene_list_2)
single_gene_set.update(gene_list_2)
#Run the single deletion analysis to account for double deletions that
#target the same gene and lethal deletions. We assume that there
#aren't synthetic rescues.
single_deletion_growth_dict = single_deletion(cobra_model,
list(single_gene_set),
method=method,
solver=solver)[0]
if gene_list_2 is None or gene_list_1 == gene_list_2:
number_of_genes = len(gene_list_1)
gene_list_2 = gene_list_1
deletion_array = zeros([number_of_genes, number_of_genes])
##TODO: Speed up this triangular process
#For the case where the contents of the lists are the same cut the work in half.
#There might be a faster way to do this by using a triangular array function
#in numpy
#Populate the diagonal from the single deletion lists
for i, the_gene in enumerate(gene_list_1):
deletion_array[i, i] = single_deletion_growth_dict[the_gene.id]
for i, gene_1 in enumerate(gene_list_1[:-1]):
#TODO: Since there cannot be synthetic rescues we can assume
#that the whole row for a lethal deletion
#will be equal to that deletion.
if single_deletion_growth_dict[gene_1.id] < growth_tolerance:
tmp_solution = single_deletion_growth_dict[gene_1.id]
for j in range(i+1, number_of_genes):
deletion_array[j, i] = deletion_array[i, j] = tmp_solution
else:
for j, gene_2 in enumerate(gene_list_1[i+1:], i+1):
if single_deletion_growth_dict[gene_2.id] < growth_tolerance:
tmp_solution = single_deletion_growth_dict[gene_2.id]
else:
delete_model_genes(cobra_model, [gene_1, gene_2])
if cobra_model._trimmed:
if method.lower() == 'fba':
#Assumes that the majority of perturbations don't change
#reactions which is probably false
cobra_model.optimize(solver=solver, error_reporting=error_reporting)
the_status = cobra_model.solution.status
tmp_solution = cobra_model.solution.f
elif method.lower() == 'moma':
try:
moma_solution = moma(wt_model, cobra_model,
combined_model=combined_model,
solver=solver)
tmp_solution = float(moma_solution.pop('objective_value'))
the_status = moma_solution.pop('status')
combined_model = moma_solution.pop('combined_model')
del moma_solution
except:
tmp_solution = nan
the_status = 'failed'
if the_status not in ['opt', 'optimal'] and \
error_reporting:
print('%s / %s: %s status: %s'%(gene_1, gene_2, solver,
the_status))
#Reset the model to orginial form.
undelete_model_genes(cobra_model)
else:
tmp_solution = basal_f
deletion_array[j, i] = deletion_array[i, j] = tmp_solution
else:
deletion_array = zeros([len(gene_list_1), len(gene_list_2)])
#Now deal with the case where the gene lists are different
for i, gene_1 in enumerate(gene_list_1):
if single_deletion_growth_dict[gene_1.id] <= 0:
for j in range(len(gene_list_2)):
deletion_array[i, j] = 0.
else:
for j, gene_2 in enumerate(gene_list_2):
#Assume no such thing as a synthetic rescue
if single_deletion_growth_dict[gene_2.id] <= growth_tolerance:
tmp_solution = single_deletion_growth_dict[gene_2.id]
else:
delete_model_genes(cobra_model, [gene_1, gene_2])
if cobra_model._trimmed:
if method.lower() == 'fba':
cobra_model.optimize(solver=solver)
tmp_solution = cobra_model.solution.f
the_status = cobra_model.solution.status
elif method.lower() == 'moma':
try:
moma_solution = moma(wt_model, cobra_model,
combined_model=combined_model,
solver=solver)
tmp_solution = float(moma_solution.pop('objective_value'))
the_status = moma_solution.pop('status')
combined_model = moma_solution.pop('combined_model')
del moma_solution
except:
tmp_solution = nan
the_status = 'failed'
if the_status not in ['opt', 'optimal'] and \
error_reporting:
print('%s / %s: %s status: %s'%(repr(gene_1), repr(gene_2), solver,
cobra_model.solution.status))
#Reset the model to wt form
undelete_model_genes(cobra_model)
else:
tmp_solution = basal_f
deletion_array[i, j] = tmp_solution
if hasattr(gene_list_1, 'id'):
gene_list_1 = [x.id for x in gene_list_1]
if hasattr(gene_list_2, 'id'):
gene_list_2 = [x.id for x in gene_list_2]
return({'x': gene_list_1, 'y': gene_list_2, 'data': deletion_array})
def double_gene_deletion(
cobra_model,
gene_list_1=None,
gene_list_2=None,
method="fba",
single_deletion_growth_dict=None,
the_problem="return",
solver="glpk",
error_reporting=None,
):
"""This will disable reactions for all gene pairs from gene_list_1 and
gene_list_2 and then run simulations to optimize for the objective
function. The contribution of each reaction to the objective function
is indicated in cobra_model.reactions[:].objective_coefficient vector.
cobra_model: a cobra.Model object
gene_list_1: Is None or a list of genes. If None then both gene_list_1
and gene_list_2 are assumed to correspond to cobra_model.genes.
gene_list_2: Is None or a list of genes. If None then gene_list_2 is
assumed to correspond to gene_list_1.
method: 'fba' or 'moma' to run flux balance analysis or minimization
of metabolic adjustments.
single_deletion_growth_dict: A dictionary that provides the growth
rate information for single gene knock outs. This can speed up
simulations because nonviable single deletion strains imply that all
double deletion strains will also be nonviable.
the_problem: Is None, 'return', or an LP model object for the solver.
solver: 'glpk', 'gurobi', or 'cplex'.
error_reporting: None or True
Returns a dictionary of the genes in the x dimension (x), the y
dimension (y), and the growth simulation data (data).
"""
# BUG: Since this might be called from ppmap, the modules need to
# be imported. Modify ppmap to take depfuncs
from numpy import zeros, nan
from cobra.flux_analysis.single_deletion import single_deletion
from cobra.manipulation import initialize_growth_medium
from cobra.manipulation import delete_model_genes, undelete_model_genes
##TODO: Use keywords instead
if isinstance(cobra_model, dict):
tmp_dict = cobra_model
cobra_model = tmp_dict["cobra_model"]
if "gene_list_1" in tmp_dict:
gene_list_1 = tmp_dict["gene_list_1"]
if "gene_list_2" in tmp_dict:
gene_list_2 = tmp_dict["gene_list_2"]
if "method" in tmp_dict:
method = tmp_dict["method"]
if "the_problem" in tmp_dict:
the_problem = tmp_dict["the_problem"]
if "single_deletion_growth_dict" in tmp_dict:
single_deletion_growth_dict = tmp_dict["single_deletion_growth_dict"]
if "solver" in tmp_dict:
solver = tmp_dict["solver"]
if "error_reporting" in tmp_dict:
error_reporting = tmp_dict["error_reporting"]
else:
cobra_model = cobra_model
# this is a slow way to revert models.
wt_model = cobra_model # NOTE: It may no longer be necessary to use a wt_model
# due to undelete_model_genes
if gene_list_1 is None:
gene_list_1 = cobra_model.genes
elif not hasattr(gene_list_1[0], "id"):
gene_list_1 = map(cobra_model.genes.get_by_id, gene_list_1)
# Get default values to use if the deletions do not alter any reactions
the_problem = cobra_model.optimize(the_problem=the_problem, solver=solver)
basal_f = cobra_model.solution.f
if method.lower() == "moma":
wt_model = cobra_model.copy()
the_problem = "return"
combined_model = None
single_gene_set = set(gene_list_1)
if gene_list_2:
if not hasattr(gene_list_2[0], "id"):
gene_list_2 = map(cobra_model.genes.get_by_id, gene_list_2)
single_gene_set.update(gene_list_2)
# Run the single deletion analysis to account for double deletions that
# target the same gene and lethal deletions. We assume that there
# aren't synthetic rescues.
single_deletion_growth_dict = single_deletion(
cobra_model,
list(single_gene_set),
method=method,
the_problem=the_problem,
solver=solver,
error_reporting=error_reporting,
)[0]
if gene_list_2 is None or gene_list_1 == gene_list_2:
deletion_array = zeros([len(gene_list_1), len(gene_list_1)])
##TODO: Speed up this triangular process
# For the case where the contents of the lists are the same cut the work in half.
# There might be a faster way to do this by using a triangular array function
# in numpy
# Populate the diagonal from the single deletion lists
for i, the_gene in enumerate(gene_list_1):
deletion_array[i, i] = single_deletion_growth_dict[the_gene.id]
for i in range(len(gene_list_1) - 1):
gene_1 = gene_list_1[i]
# TODO: Since there cannot be synthetic rescues we can assume
# that the whole row for a lethal deletion
# will be equal to that deletion.
if single_deletion_growth_dict[gene_1.id] <= 0:
for j in range(i + 1, len(gene_list_1)):
deletion_array[j, i] = deletion_array[i, j] = single_deletion_growth_dict[gene_2.id]
else:
for j in range(i + 1, len(gene_list_1)):
if single_deletion_growth_dict[gene_1.id] <= 0:
tmp_solution = single_deletion_growth_dict[gene_1.id]
else:
gene_2 = gene_list_1[j]
delete_model_genes(cobra_model, [gene_1, gene_2])
if cobra_model._trimmed:
if method.lower() == "fba":
# Assumes that the majority of perturbations don't change
# reactions which is probably false
cobra_model.optimize(
the_problem=the_problem, solver=solver, error_reporting=error_reporting
)
the_status = cobra_model.solution.status
tmp_solution = cobra_model.solution.f
elif method.lower() == "moma":
try:
moma_solution = moma(
wt_model,
cobra_model,
combined_model=combined_model,
solver=solver,
the_problem=the_problem,
)
tmp_solution = float(moma_solution.pop("objective_value"))
the_problem = moma_solution.pop("the_problem")
the_status = moma_solution.pop("status")
combined_model = moma_solution.pop("combined_model")
del moma_solution
except:
tmp_solution = nan
the_status = "failed"
if the_status not in ["opt", "optimal"] and error_reporting:
print "%s / %s: %s status: %s" % (gene_1, gene_2, solver, the_status)
# Reset the model to orginial form.
undelete_model_genes(cobra_model)
else:
tmp_solution = basal_f
deletion_array[j, i] = deletion_array[i, j] = tmp_solution
else:
deletion_array = zeros([len(gene_list_1), len(gene_list_2)])
# Now deal with the case where the gene lists are different
for i, gene_1 in enumerate(gene_list_1):
if single_deletion_growth_dict[gene_1.id] <= 0:
for j in range(len(gene_list_2)):
deletion_array[i, j] = 0.0
else:
for j, gene_2 in enumerate(gene_list_2):
# Assume no such thing as a synthetic rescue
if single_deletion_growth_dict[gene_2.id] <= 0:
tmp_solution = single_deletion_growth_dict[gene_2.id]
else:
delete_model_genes(cobra_model, [gene_1, gene_2])
if cobra_model._trimmed:
if method.lower() == "fba":
cobra_model.optimize(
the_problem=the_problem, solver=solver, error_reporting=error_reporting
)
tmp_solution = cobra_model.solution.f
the_status = cobra_model.solution.status
elif method.lower() == "moma":
try:
moma_solution = moma(
wt_model,
cobra_model,
combined_model=combined_model,
solver=solver,
the_problem=the_problem,
)
tmp_solution = float(moma_solution.pop("objective_value"))
the_problem = moma_solution.pop("the_problem")
the_status = moma_solution.pop("status")
combined_model = moma_solution.pop("combined_model")
del moma_solution
except:
tmp_solution = nan
the_status = "failed"
if the_status not in ["opt", "optimal"] and error_reporting:
print "%s / %s: %s status: %s" % (
repr(gene_1),
repr(gene_2),
solver,
cobra_model.solution.status,
)
# Reset the model to wt form
undelete_model_genes(cobra_model)
else:
tmp_solution = basal_f
deletion_array[i, j] = tmp_solution
return {"x": gene_list_1, "y": gene_list_2, "data": deletion_array}
def double_gene_deletion_moma(cobra_model,
gene_list_1=None,
gene_list_2=None,
method='moma',
single_deletion_growth_dict=None,
solver='glpk',
growth_tolerance=1e-8,
error_reporting=None):
"""This will disable reactions for all gene pairs from gene_list_1 and
gene_list_2 and then run simulations to optimize for the objective
function. The contribution of each reaction to the objective function
is indicated in cobra_model.reactions[:].objective_coefficient vector.
NOTE: We've assumed that there is no such thing as a synthetic rescue with
this modeling framework.
cobra_model: a cobra.Model object
gene_list_1: Is None or a list of genes. If None then both gene_list_1
and gene_list_2 are assumed to correspond to cobra_model.genes.
gene_list_2: Is None or a list of genes. If None then gene_list_2 is
assumed to correspond to gene_list_1.
method: 'fba' or 'moma' to run flux balance analysis or minimization
of metabolic adjustments.
single_deletion_growth_dict: A dictionary that provides the growth
rate information for single gene knock outs. This can speed up
simulations because nonviable single deletion strains imply that all
double deletion strains will also be nonviable.
solver: 'glpk', 'gurobi', or 'cplex'.
error_reporting: None or True
growth_tolerance: float. The effective lower bound on the growth rate
for a single deletion that is still considered capable of growth.
Returns a dictionary of the gene ids in the x dimension (x) and the y
dimension (y), and the growth simulation data (data).
"""
#BUG: Since this might be called from ppmap, the modules need to
#be imported. Modify ppmap to take depfuncs
from numpy import zeros
nan = float('nan')
from cobra.flux_analysis.single_deletion import single_deletion
from cobra.manipulation import delete_model_genes, undelete_model_genes
##TODO: Use keywords instead
if isinstance(cobra_model, dict):
tmp_dict = cobra_model
cobra_model = tmp_dict['cobra_model']
if 'gene_list_1' in tmp_dict:
gene_list_1 = tmp_dict['gene_list_1']
if 'gene_list_2' in tmp_dict:
gene_list_2 = tmp_dict['gene_list_2']
if 'method' in tmp_dict:
method = tmp_dict['method']
if 'single_deletion_growth_dict' in tmp_dict:
single_deletion_growth_dict = tmp_dict[
'single_deletion_growth_dict']
if 'solver' in tmp_dict:
solver = tmp_dict['solver']
if 'error_reporting' in tmp_dict:
error_reporting = tmp_dict['error_reporting']
else:
cobra_model = cobra_model
#this is a slow way to revert models.
wt_model = cobra_model #NOTE: It may no longer be necessary to use a wt_model
#due to undelete_model_genes
if gene_list_1 is None:
gene_list_1 = cobra_model.genes
elif not hasattr(gene_list_1[0], 'id'):
gene_list_1 = map(cobra_model.genes.get_by_id, gene_list_1)
#Get default values to use if the deletions do not alter any reactions
cobra_model.optimize(solver=solver)
basal_f = cobra_model.solution.f
if method.lower() == 'moma':
wt_model = cobra_model.copy()
combined_model = None
single_gene_set = set(gene_list_1)
if gene_list_2 is not None:
if not hasattr(gene_list_2[0], 'id'):
gene_list_2 = map(cobra_model.genes.get_by_id, gene_list_2)
single_gene_set.update(gene_list_2)
#Run the single deletion analysis to account for double deletions that
#target the same gene and lethal deletions. We assume that there
#aren't synthetic rescues.
single_deletion_growth_dict = single_deletion(cobra_model,
list(single_gene_set),
method=method,
solver=solver)[0]
if gene_list_2 is None or gene_list_1 == gene_list_2:
number_of_genes = len(gene_list_1)
gene_list_2 = gene_list_1
deletion_array = zeros([number_of_genes, number_of_genes])
##TODO: Speed up this triangular process
#For the case where the contents of the lists are the same cut the work in half.
#There might be a faster way to do this by using a triangular array function
#in numpy
#Populate the diagonal from the single deletion lists
for i, the_gene in enumerate(gene_list_1):
deletion_array[i, i] = single_deletion_growth_dict[the_gene.id]
for i, gene_1 in enumerate(gene_list_1[:-1]):
#TODO: Since there cannot be synthetic rescues we can assume
#that the whole row for a lethal deletion
#will be equal to that deletion.
if single_deletion_growth_dict[gene_1.id] < growth_tolerance:
tmp_solution = single_deletion_growth_dict[gene_1.id]
for j in range(i + 1, number_of_genes):
deletion_array[j, i] = deletion_array[i, j] = tmp_solution
else:
for j, gene_2 in enumerate(gene_list_1[i + 1:], i + 1):
if single_deletion_growth_dict[
gene_2.id] < growth_tolerance:
tmp_solution = single_deletion_growth_dict[gene_2.id]
else:
delete_model_genes(cobra_model, [gene_1, gene_2])
if cobra_model._trimmed:
if method.lower() == 'fba':
#Assumes that the majority of perturbations don't change
#reactions which is probably false
cobra_model.optimize(
solver=solver,
error_reporting=error_reporting)
the_status = cobra_model.solution.status
tmp_solution = cobra_model.solution.f
elif method.lower() == 'moma':
try:
moma_solution = moma(
wt_model,
cobra_model,
combined_model=combined_model,
solver=solver)
tmp_solution = float(
moma_solution.pop('objective_value'))
the_status = moma_solution.pop('status')
combined_model = moma_solution.pop(
'combined_model')
del moma_solution
except:
tmp_solution = nan
the_status = 'failed'
if the_status not in ['opt', 'optimal'] and \
error_reporting:
print('%s / %s: %s status: %s' %
(gene_1, gene_2, solver, the_status))
#Reset the model to orginial form.
undelete_model_genes(cobra_model)
else:
tmp_solution = basal_f
deletion_array[j, i] = deletion_array[i, j] = tmp_solution
else:
deletion_array = zeros([len(gene_list_1), len(gene_list_2)])
#Now deal with the case where the gene lists are different
for i, gene_1 in enumerate(gene_list_1):
if single_deletion_growth_dict[gene_1.id] <= 0:
for j in range(len(gene_list_2)):
deletion_array[i, j] = 0.
else:
for j, gene_2 in enumerate(gene_list_2):
#Assume no such thing as a synthetic rescue
if single_deletion_growth_dict[
gene_2.id] <= growth_tolerance:
tmp_solution = single_deletion_growth_dict[gene_2.id]
else:
delete_model_genes(cobra_model, [gene_1, gene_2])
if cobra_model._trimmed:
if method.lower() == 'fba':
cobra_model.optimize(solver=solver)
tmp_solution = cobra_model.solution.f
the_status = cobra_model.solution.status
elif method.lower() == 'moma':
try:
moma_solution = moma(
wt_model,
cobra_model,
combined_model=combined_model,
solver=solver)
tmp_solution = float(
moma_solution.pop('objective_value'))
the_status = moma_solution.pop('status')
combined_model = moma_solution.pop(
'combined_model')
del moma_solution
except:
tmp_solution = nan
the_status = 'failed'
if the_status not in ['opt', 'optimal'] and \
error_reporting:
print('%s / %s: %s status: %s' %
(repr(gene_1), repr(gene_2), solver,
cobra_model.solution.status))
#Reset the model to wt form
undelete_model_genes(cobra_model)
else:
tmp_solution = basal_f
deletion_array[i, j] = tmp_solution
if hasattr(gene_list_1, 'id'):
gene_list_1 = [x.id for x in gene_list_1]
if hasattr(gene_list_2, 'id'):
gene_list_2 = [x.id for x in gene_list_2]
return ({'x': gene_list_1, 'y': gene_list_2, 'data': deletion_array})