def find_essential_reactions_1(self):
""" Computes the list of essential reactions of the model
Saves the result in __essential_reactions attribute.
Returns a list of error during the computation.
:return: list of error during the computation.
:rtype: list of str
"""
errors = []
try:
self.__objective_value = self.get_growth()
if isnan(self.__objective_value):
self.__objective_value = None
self.__essential_reactions = {}
deletions = single_reaction_deletion(self.__cobra_model,
method='fba',
processes=self.__processes)
essential = deletions.loc[:, :]['growth']
for r, g in essential.iteritems():
reaction = self.__cobra_model.reactions.get_by_id(list(r)[0])
self.__essential_reactions[reaction] = g
except Exception as error:
errors.append(str(error))
self.__essential_reactions = {}
return errors
def find_essential_reactions(model, threshold=None, processes=None):
"""Return a set of essential reactions.
A reaction is considered essential if restricting its flux to zero
causes the objective, e.g., the growth rate, to also be zero, below the
threshold, or infeasible.
Parameters
----------
model : cobra.Model
The model to find the essential reactions for.
threshold : float, optional
Minimal objective flux to be considered viable. By default this is
1% of the maximal objective.
processes : int, optional
The number of parallel processes to run. Can speed up the computations
if the number of knockouts to perform is large. If not passed,
will be set to the number of CPUs found.
Returns
-------
set
Set of essential reactions
"""
if threshold is None:
threshold = model.slim_optimize(error_value=None) * 1E-02
deletions = single_reaction_deletion(model,
method='fba',
processes=processes)
essential = deletions.loc[deletions['growth'].isna() |
(deletions['growth'] < threshold), :].index
return {model.reactions.get_by_id(r) for ids in essential for r in ids}
def find_essential_reactions(model, threshold=None, processes=None):
"""Return a set of essential reactions.
A reaction is considered essential if restricting its flux to zero
causes the objective (e.g. the growth rate) to also be zero.
Parameters
----------
model : cobra.Model
The model to find the essential reactions for.
threshold : float, optional
Minimal objective flux to be considered viable. By default this is
0.01 times the growth rate.
processes : int, optional
The number of parallel processes to run. Can speed up the computations
if the number of knockouts to perform is large. If not passed,
will be set to the number of CPUs found.
Returns
-------
set
Set of essential reactions
"""
if threshold is None:
threshold = model.slim_optimize(error_value=None) * 1E-02
deletions = single_reaction_deletion(model, method='fba',
processes=processes)
essential = deletions.loc[deletions['growth'].isna() |
(deletions['growth'] < threshold), :].index
return set(model.reactions.get_by_id(r) for ids in essential for r in ids)
def find_essential_reactions(model, threshold=None):
"""Return a set of essential reactions.
A reaction is considered essential if restricting its flux to zero
causes the objective (e.g. the growth rate) to also be zero.
Parameters
----------
model : cobra.Model
The model to find the essential reactions for.
threshold : float, optional
Minimal objective flux to be considered viable. By default this is
0.01 times the growth rate.
Returns
-------
set
Set of essential reactions
"""
if threshold is None:
threshold = model.slim_optimize(error_value=None) * 1E-02
deletions = single_reaction_deletion(model, method='fba')
essential = deletions.loc[deletions['growth'].isna() |
(deletions['growth'] < threshold), :].index
return set(model.reactions.get_by_id(r) for ids in essential for r in ids)
def find_essential_reactions(model, threshold=None):
"""Return a set of essential reactions.
A reaction is considered essential if restricting its flux to zero
causes the objective (e.g. the growth rate) to also be zero.
Parameters
----------
model : cobra.Model
The model to find the essential reactions for.
threshold : float, optional
Minimal objective flux to be considered viable. By default this is
0.01 times the growth rate.
Returns
-------
set
Set of essential reactions
"""
if threshold is None:
threshold = model.slim_optimize(error_value=None) * 1E-02
deletions = single_reaction_deletion(model, method='fba')
essential = deletions.loc[deletions['growth'].isna() |
(deletions['growth'] < threshold), :].index
return set(model.reactions.get_by_id(r) for ids in essential for r in ids)
def test_single_reaction_deletion(model, all_solvers):
"""Test single reaction deletion."""
model.solver = all_solvers
expected_results = {'FBA': 0.70404, 'FBP': 0.87392, 'CS': 0,
'FUM': 0.81430, 'GAPD': 0, 'GLUDy': 0.85139}
result = single_reaction_deletion(
model=model,
reaction_list=list(expected_results),
processes=1
)['growth']
for reaction, value in iteritems(expected_results):
assert np.isclose(result[frozenset([reaction])], value,
atol=1E-05)
def test_single_reaction_deletion(model, all_solvers):
"""Test single reaction deletion."""
model.solver = all_solvers
expected_results = {
'FBA': 0.70404,
'FBP': 0.87392,
'CS': 0,
'FUM': 0.81430,
'GAPD': 0,
'GLUDy': 0.85139
}
result = single_reaction_deletion(model=model,
reaction_list=list(expected_results),
processes=1)['growth']
for reaction, value in iteritems(expected_results):
assert np.isclose(result[frozenset([reaction])], value, atol=1E-05)
def test_single_reaction_deletion(model: Model, all_solvers) -> None:
"""Test single reaction deletion."""
model.solver = all_solvers
expected_results = {
"FBA": 0.70404,
"FBP": 0.87392,
"CS": 0,
"FUM": 0.81430,
"GAPD": 0,
"GLUDy": 0.85139,
}
result = single_reaction_deletion(
model=model, reaction_list=list(expected_results), processes=1
)
for reaction, value in expected_results.items():
assert np.isclose(result.knockout[reaction].growth, value, atol=1e-05)
def test_deletion_accessor(small_model: Model) -> None:
"""Test the DataFrame accessor."""
single = single_reaction_deletion(small_model, small_model.reactions[0:10])
double = double_reaction_deletion(small_model, small_model.reactions[0:10])
rxn1 = small_model.reactions[0]
rxn2 = small_model.reactions[1]
with pytest.raises(ValueError):
single.knockout[1]
with pytest.raises(ValueError):
single.knockout[{"a": 1}]
assert single.knockout[rxn1].ids.iloc[0] == {rxn1.id}
assert double.knockout[{rxn1, rxn2}].ids.iloc[0] == {rxn1.id, rxn2.id}
assert all(single.knockout[rxn1.id] == single.knockout[rxn1])
assert all(double.knockout[{rxn1.id, rxn2.id}] == double.knockout[{rxn1, rxn2}])
assert single.knockout[rxn1, rxn2].shape == (2, 3)
assert double.knockout[rxn1, rxn2].shape == (2, 3)
assert double.knockout[{rxn1, rxn2}].shape == (1, 3)
assert double.knockout[{rxn1}, {rxn2}].shape == (2, 3)