def test_moma_sanity(self, model):
"""Test optimization criterion and optimality."""
try:
solver = sutil.get_solver_name(qp=True)
model.solver = solver
except sutil.SolverNotFound:
pytest.skip("no qp support")
sol = model.optimize()
with model:
model.reactions.PFK.knock_out()
knock_sol = model.optimize()
ssq = (knock_sol.fluxes - sol.fluxes).pow(2).sum()
with model:
add_moma(model)
model.reactions.PFK.knock_out()
moma_sol = model.optimize()
moma_ssq = (moma_sol.fluxes - sol.fluxes).pow(2).sum()
# Use normal FBA as reference solution.
with model:
add_moma(model, solution=sol)
model.reactions.PFK.knock_out()
moma_ref_sol = model.optimize()
moma_ref_ssq = (moma_ref_sol.fluxes - sol.fluxes).pow(2).sum()
assert numpy.isclose(moma_sol.objective_value, moma_ssq)
assert moma_ssq < ssq
assert numpy.isclose(moma_sol.objective_value,
moma_ref_sol.objective_value)
assert numpy.isclose(moma_ssq, moma_ref_ssq)
def test_moma_sanity(model: Model, qp_solvers: List[str]) -> None:
"""Test optimization criterion and optimality for MOMA."""
model.solver = qp_solvers
sol = model.optimize()
with model:
model.reactions.PFK.knock_out()
knock_sol = model.optimize()
ssq = (knock_sol.fluxes - sol.fluxes).pow(2).sum()
with model:
add_moma(model, linear=False)
model.reactions.PFK.knock_out()
moma_sol = model.optimize()
moma_ssq = (moma_sol.fluxes - sol.fluxes).pow(2).sum()
# Use normal FBA as reference solution.
with model:
add_moma(model, solution=sol, linear=False)
model.reactions.PFK.knock_out()
moma_ref_sol = model.optimize()
moma_ref_ssq = (moma_ref_sol.fluxes - sol.fluxes).pow(2).sum()
assert np.isclose(moma_sol.objective_value, moma_ssq)
assert moma_ssq < ssq
assert np.isclose(moma_sol.objective_value, moma_ref_sol.objective_value)
assert np.isclose(moma_ssq, moma_ref_ssq)
def test_single_gene_deletion_linear_moma(self, model):
try:
solver = sutil.get_solver_name(qp=True)
model.solver = solver
except sutil.SolverNotFound:
pytest.skip("no qp support")
# expected knockouts for textbook model
growth_dict = {
"b0008": 0.87,
"b0114": 0.76,
"b0116": 0.65,
"b2276": 0.08,
"b1779": 0.00
}
result = single_gene_deletion(model,
gene_list=growth_dict.keys(),
method="linear moma")['growth']
assert all(
abs(result[frozenset([gene])] - expected) < 0.01
for gene, expected in iteritems(growth_dict))
with model:
add_moma(model, linear=True)
with pytest.raises(ValueError):
add_moma(model)
def test_moma_sanity(model, qp_solvers):
"""Test optimization criterion and optimality for MOMA."""
model.solver = qp_solvers
sol = model.optimize()
with model:
model.reactions.PFK.knock_out()
knock_sol = model.optimize()
ssq = (knock_sol.fluxes - sol.fluxes).pow(2).sum()
with model:
add_moma(model, linear=False)
model.reactions.PFK.knock_out()
moma_sol = model.optimize()
moma_ssq = (moma_sol.fluxes - sol.fluxes).pow(2).sum()
# Use normal FBA as reference solution.
with model:
add_moma(model, solution=sol, linear=False)
model.reactions.PFK.knock_out()
moma_ref_sol = model.optimize()
moma_ref_ssq = (moma_ref_sol.fluxes - sol.fluxes).pow(2).sum()
assert np.isclose(moma_sol.objective_value, moma_ssq)
assert moma_ssq < ssq
assert np.isclose(moma_sol.objective_value,
moma_ref_sol.objective_value)
assert np.isclose(moma_ssq, moma_ref_ssq)
def test_moma_sanity(self, model):
"""Test optimization criterion and optimality."""
try:
solver = sutil.get_solver_name(qp=True)
model.solver = solver
except sutil.SolverNotFound:
pytest.skip("no qp support")
sol = model.optimize()
with model:
model.reactions.PFK.knock_out()
knock_sol = model.optimize()
ssq = (knock_sol.fluxes - sol.fluxes).pow(2).sum()
with model:
add_moma(model)
model.reactions.PFK.knock_out()
moma_sol = model.optimize()
moma_ssq = (moma_sol.fluxes - sol.fluxes).pow(2).sum()
# Use normal FBA as reference solution.
with model:
add_moma(model, solution=sol)
model.reactions.PFK.knock_out()
moma_ref_sol = model.optimize()
moma_ref_ssq = (moma_ref_sol.fluxes - sol.fluxes).pow(2).sum()
assert numpy.isclose(moma_sol.objective_value, moma_ssq)
assert moma_ssq < ssq
assert numpy.isclose(moma_sol.objective_value,
moma_ref_sol.objective_value)
assert numpy.isclose(moma_ssq, moma_ref_ssq)
def test_single_gene_deletion_moma(self, model):
try:
sutil.get_solver_name(qp=True)
except sutil.SolverNotFound:
pytest.skip("no qp support")
# expected knockouts for textbook model
growth_dict = {
"b0008": 0.87,
"b0114": 0.71,
"b0116": 0.56,
"b2276": 0.11,
"b1779": 0.00
}
df = single_gene_deletion(model,
gene_list=growth_dict.keys(),
method="moma")
assert numpy.all([df.status == 'optimal'])
assert all(
abs(df.flux[gene] - expected) < 0.01
for gene, expected in iteritems(growth_dict))
with model:
add_moma(model)
with pytest.raises(ValueError):
add_moma(model)
def test_linear_moma_sanity(self, model):
"""Test optimization criterion and optimality."""
sol = model.optimize()
with model:
model.reactions.PFK.knock_out()
knock_sol = model.optimize()
sabs = (knock_sol.fluxes - sol.fluxes).abs().sum()
with model:
add_moma(model, linear=True)
model.reactions.PFK.knock_out()
moma_sol = model.optimize()
moma_sabs = (moma_sol.fluxes - sol.fluxes).abs().sum()
# Use normal FBA as reference solution.
with model:
add_moma(model, solution=sol, linear=True)
model.reactions.PFK.knock_out()
moma_ref_sol = model.optimize()
moma_ref_sabs = (moma_ref_sol.fluxes - sol.fluxes).abs().sum()
assert numpy.allclose(moma_sol.objective_value, moma_sabs)
assert moma_sabs < sabs
assert numpy.isclose(moma_sol.objective_value,
moma_ref_sol.objective_value)
assert numpy.isclose(moma_sabs, moma_ref_sabs)
with model:
add_moma(model, linear=True)
with pytest.raises(ValueError):
add_moma(model)
def test_linear_moma_sanity(model: Model, all_solvers: List[str]) -> None:
"""Test optimization criterion and optimality for linear MOMA."""
model.solver = all_solvers
sol = model.optimize()
with model:
model.reactions.PFK.knock_out()
knock_sol = model.optimize()
sabs = (knock_sol.fluxes - sol.fluxes).abs().sum()
with model:
add_moma(model, linear=True)
model.reactions.PFK.knock_out()
moma_sol = model.optimize()
moma_sabs = (moma_sol.fluxes - sol.fluxes).abs().sum()
# Use normal FBA as reference solution.
with model:
add_moma(model, solution=sol, linear=True)
model.reactions.PFK.knock_out()
moma_ref_sol = model.optimize()
moma_ref_sabs = (moma_ref_sol.fluxes - sol.fluxes).abs().sum()
assert np.allclose(moma_sol.objective_value, moma_sabs)
assert moma_sabs < sabs
assert np.isclose(moma_sol.objective_value, moma_ref_sol.objective_value)
assert np.isclose(moma_sabs, moma_ref_sabs)
with model:
add_moma(model, linear=True)
with pytest.raises(ValueError):
add_moma(model)
def test_linear_moma_sanity(self, model):
"""Test optimization criterion and optimality."""
sol = model.optimize()
with model:
model.reactions.PFK.knock_out()
knock_sol = model.optimize()
sabs = (knock_sol.fluxes - sol.fluxes).abs().sum()
with model:
add_moma(model, linear=True)
model.reactions.PFK.knock_out()
moma_sol = model.optimize()
moma_sabs = (moma_sol.fluxes - sol.fluxes).abs().sum()
# Use normal FBA as reference solution.
with model:
add_moma(model, solution=sol, linear=True)
model.reactions.PFK.knock_out()
moma_ref_sol = model.optimize()
moma_ref_sabs = (moma_ref_sol.fluxes - sol.fluxes).abs().sum()
assert numpy.allclose(moma_sol.objective_value, moma_sabs)
assert moma_sabs < sabs
assert numpy.isclose(moma_sol.objective_value,
moma_ref_sol.objective_value)
assert numpy.isclose(moma_sabs, moma_ref_sabs)
with model:
add_moma(model, linear=True)
with pytest.raises(ValueError):
add_moma(model)
def test_linear_moma_sanity(self, model):
"""Test optimization criterion and optimality."""
sol = model.optimize()
with model:
model.reactions.PFK.knock_out()
knock_sol = model.optimize()
sabs = (knock_sol.fluxes - sol.fluxes).abs().sum()
with model:
add_moma(model, linear=True)
model.reactions.PFK.knock_out()
moma_sol = model.optimize()
moma_sabs = (moma_sol.fluxes - sol.fluxes).abs().sum()
assert numpy.allclose(moma_sol.objective_value, moma_sabs)
assert moma_sabs < sabs
def test_single_gene_deletion_linear_moma(self, model):
try:
solver = sutil.get_solver_name(qp=True)
model.solver = solver
except sutil.SolverNotFound:
pytest.skip("no qp support")
# expected knockouts for textbook model
growth_dict = {"b0008": 0.87, "b0114": 0.76, "b0116": 0.65,
"b2276": 0.08, "b1779": 0.00}
result = single_gene_deletion(
model, gene_list=growth_dict.keys(),
method="linear moma")['growth']
assert all(abs(result[frozenset([gene])] - expected) < 0.01
for gene, expected in iteritems(growth_dict))
with model:
add_moma(model, linear=True)
with pytest.raises(ValueError):
add_moma(model)
def test_single_gene_deletion_linear_moma(self, model):
# expected knockouts for textbook model
growth_dict = {
"b0008": 0.87,
"b0114": 0.76,
"b0116": 0.65,
"b2276": 0.08,
"b1779": 0.00
}
df = single_gene_deletion(model,
gene_list=growth_dict.keys(),
method="linear moma")
assert numpy.all([df.status == 'optimal'])
assert all(
abs(df.flux[gene] - expected) < 0.01
for gene, expected in iteritems(growth_dict))
with model:
add_moma(model, linear=True)
with pytest.raises(ValueError):
add_moma(model)
def test_linear_moma_sanity(self, model):
"""Test optimization criterion and optimality."""
try:
solver = sutil.get_solver_name(qp=True)
model.solver = solver
except sutil.SolverNotFound:
pytest.skip("no qp support")
sol = model.optimize()
with model:
model.reactions.PFK.knock_out()
knock_sol = model.optimize()
sabs = (knock_sol.fluxes - sol.fluxes).abs().sum()
with model:
add_moma(model, linear=True)
model.reactions.PFK.knock_out()
moma_sol = model.optimize()
moma_sabs = (moma_sol.fluxes - sol.fluxes).abs().sum()
assert numpy.allclose(moma_sol.objective_value, moma_sabs)
assert moma_sabs < sabs
def test_linear_moma_sanity(self, model):
"""Test optimization criterion and optimality."""
try:
solver = sutil.get_solver_name(qp=True)
model.solver = solver
except sutil.SolverNotFound:
pytest.skip("no qp support")
sol = model.optimize()
with model:
model.reactions.PFK.knock_out()
knock_sol = model.optimize()
sabs = (knock_sol.fluxes - sol.fluxes).abs().sum()
with model:
add_moma(model, linear=True)
model.reactions.PFK.knock_out()
moma_sol = model.optimize()
moma_sabs = (moma_sol.fluxes - sol.fluxes).abs().sum()
assert numpy.allclose(moma_sol.objective_value, moma_sabs)
assert moma_sabs < sabs
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 _multi_deletion(model, entity, element_lists, method="fba",
solution=None, processes=None, **kwargs):
"""
Provide a common interface for single or multiple knockouts.
Parameters
----------
model : cobra.Model
The metabolic model to perform deletions in.
entity : 'gene' or 'reaction'
The entity to knockout (``cobra.Gene`` or ``cobra.Reaction``).
element_lists : list
List of iterables ``cobra.Reaction``s or ``cobra.Gene``s (or their IDs)
to be deleted.
method: {"fba", "moma", "linear moma", "room", "linear room"}, optional
Method used to predict the growth rate.
solution : cobra.Solution, optional
A previous solution to use as a reference for (linear) MOMA or ROOM.
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.
kwargs :
Passed on to underlying simulation functions.
Returns
-------
pandas.DataFrame
A representation of all combinations of entity deletions. The
columns are 'growth' and 'status', where
index : frozenset([str])
The gene or reaction identifiers that were knocked out.
growth : float
The growth rate of the adjusted model.
status : str
The solution's status.
"""
solver = sutil.interface_to_str(model.problem.__name__)
if method == "moma" and solver not in sutil.qp_solvers:
raise RuntimeError(
"Cannot use MOMA since '{}' is not QP-capable."
"Please choose a different solver or use FBA only.".format(solver))
if processes is None:
processes = CONFIGURATION.processes
with model:
if "moma" in method:
add_moma(model, solution=solution, linear="linear" in method)
elif "room" in method:
add_room(model, solution=solution, linear="linear" in method,
**kwargs)
args = set([frozenset(comb) for comb in product(*element_lists)])
processes = min(processes, len(args))
def extract_knockout_results(result_iter):
result = pd.DataFrame([
(frozenset(ids), growth, status)
for (ids, growth, status) in result_iter
], columns=['ids', 'growth', 'status'])
result.set_index('ids', inplace=True)
return result
if processes > 1:
worker = dict(gene=_gene_deletion_worker,
reaction=_reaction_deletion_worker)[entity]
chunk_size = len(args) // processes
pool = multiprocessing.Pool(
processes, initializer=_init_worker, initargs=(model,)
)
results = extract_knockout_results(pool.imap_unordered(
worker,
args,
chunksize=chunk_size
))
pool.close()
pool.join()
else:
worker = dict(gene=_gene_deletion,
reaction=_reaction_deletion)[entity]
results = extract_knockout_results(map(
partial(worker, model), args))
return results
def single_reaction_deletion_moma(cobra_model, reaction_list, solver=None,
**solver_args):
"""Sequentially knocks out each reaction in a model using MOMA.
Not supposed to be called directly use
`single_reactions_deletion(..., method="moma")` instead.
Parameters
----------
reaction_list : iterable
List of reaction 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})
"""
# The same function can not be used because MOMA can not re-use the
# same LP object. Problem re-use leads to incorrect solutions.
# This is *not* true for optlang solvers!
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 reaction in reaction_list:
with m:
reaction.bounds = (0.0, 0.0)
m.solver.optimize()
status = m.solver.status
status_dict[reaction.id] = status
if status == OPTIMAL:
growth = m.variables.moma_old_objective.primal
else:
growth = 0.0
growth_rate_dict[reaction.id] = growth
else:
for reaction in reaction_list:
index = cobra_model.reactions.index(reaction)
solution = moma.moma_knockout(moma_model, moma_objective, (index,),
solver=solver, **solver_args)
status_dict[reaction.id] = solution.status
growth_rate_dict[reaction.id] = solution.f
return growth_rate_dict, status_dict
def _multi_deletion(model, entity, element_lists, method="fba",
processes=None):
"""
Provide a common interface for single or multiple knockouts.
Parameters
----------
model : cobra.Model
The metabolic model to perform deletions in.
entity : 'gene' or 'reaction'
The entity to knockout (``cobra.Gene`` or ``cobra.Reaction``).
element_lists : list
List of iterables ``cobra.Reaction``s or ``cobra.Gene``s (or their IDs)
to be deleted.
method: {"fba", "moma", "linear moma"}, optional
Method used to predict 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
-------
pandas.DataFrame
A representation of all combinations of entity deletions. The
columns are 'growth' and 'status', where
index : frozenset([str])
The gene or reaction identifiers that were knocked out.
growth : float
The growth rate of the adjusted model.
status : str
The solution's status.
"""
solver = sutil.interface_to_str(model.problem.__name__)
if "moma" in method and solver not in sutil.qp_solvers:
raise RuntimeError(
"Cannot use MOMA since '{}' is not QP-capable."
"Please choose a different solver or use FBA only.".format(solver))
if processes is None:
try:
processes = multiprocessing.cpu_count()
except NotImplementedError:
warn("Number of cores could not be detected - assuming 1.")
processes = 1
with model:
if "moma" in method:
add_moma(model, linear="linear" in method)
args = set([frozenset(comb) for comb in product(*element_lists)])
processes = min(processes, len(args))
def extract_knockout_results(result_iter):
result = pd.DataFrame([
(frozenset(ids), growth, status)
for (ids, growth, status) in result_iter
], columns=['ids', 'growth', 'status'])
result.set_index('ids', inplace=True)
return result
if processes > 1:
worker = dict(gene=_gene_deletion_worker,
reaction=_reaction_deletion_worker)[entity]
chunk_size = len(args) // processes
pool = multiprocessing.Pool(
processes, initializer=_init_worker, initargs=(model,)
)
results = extract_knockout_results(pool.imap_unordered(
worker,
args,
chunksize=chunk_size
))
pool.close()
pool.join()
else:
worker = dict(gene=_gene_deletion,
reaction=_reaction_deletion)[entity]
results = extract_knockout_results(map(
partial(worker, model), args))
return results
def _multi_deletion(model, entity, element_lists, method="fba", num_jobs=None):
"""
Provide a common interface for single or multiple knockouts.
Parameters
----------
model : cobra.Model
The metabolic model to perform deletions in.
entity : 'gene' or 'reaction'
The entity to knockout (``cobra.Gene`` or ``cobra.Reaction``).
element_lists : list
List of iterables ``cobra.Reaction``s or ``cobra.Gene``s (or their IDs)
to be deleted.
method: {"fba", "moma", "linear moma"}, optional
Method used to predict the growth rate.
num_jobs : 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
-------
pandas.DataFrame
A representation of all combinations of entity deletions. The
columns are 'growth' and 'status', where
index : frozenset([str])
The gene or reaction identifiers that were knocked out.
growth : float
The growth rate of the adjusted model.
status : str
The solution's status.
"""
solver = sutil.interface_to_str(model.problem.__name__)
if "moma" in method and solver not in sutil.qp_solvers:
raise RuntimeError(
"Cannot use MOMA since '{}' is not QP-capable."
"Please choose a different solver or use FBA only.".format(solver))
if num_jobs is None:
try:
num_cpu = multiprocessing.cpu_count()
except NotImplementedError:
warn("Number of cores could not be detected - assuming 1.")
num_cpu = 1
else:
num_cpu = num_jobs
with model:
if "moma" in method:
add_moma(model, linear="linear" in method)
args = set([frozenset(comb) for comb in product(*element_lists)])
num_cpu = min(num_cpu, len(args))
def extract_knockout_results(result_iter):
result = pd.DataFrame([(frozenset(ids), growth, status)
for (ids, growth, status) in result_iter],
columns=['ids', 'growth', 'status'])
result.set_index('ids', inplace=True)
return result
if num_cpu > 1:
num_cpu = min(num_cpu, len(args))
worker = dict(gene=_gene_deletion_worker,
reaction=_reaction_deletion_worker)[entity]
chunk_size = len(args) // num_cpu
pool = multiprocessing.Pool(num_cpu,
initializer=_init_worker,
initargs=(model, ))
results = extract_knockout_results(
pool.imap_unordered(worker, args, chunksize=chunk_size))
pool.close()
pool.join()
else:
worker = dict(gene=_gene_deletion,
reaction=_reaction_deletion)[entity]
results = extract_knockout_results(
map(partial(worker, model), args))
return results
developedRxnsS2 = addDevelopedReactions(model, 's2', reactionsS2)
# Compute baseline behavior
print('3-HP Production, Baseline')
baseline = model.optimize()
baselineTargetFlux = baseline['3HP_SINK']
print(' Growth %f (core), %f (wt)' %
(baseline[biomassCoreRxn.id], baseline[biomassWtRxn.id]))
print(' Glucose %f -> 3HP %f' %
(baseline['EX_glc__D_e'], baselineTargetFlux))
# Set up MOMA constraints if needed
if method == 'moma':
add_moma(model, solution=baseline)
# Compute engineered behavior with pathway reactions enabled
for rxn in pathwayRxns:
rxn.lower_bound = -1000
rxn.upper_bound = 1000
print('3-HP Production, w/Synthetic Path')
engineered = model.optimize()
normalTargetFlux = engineered['3HP_SINK']
print(' Growth %f (core), %f (wt)' %
(engineered[biomassCoreRxn.id], engineered[biomassWtRxn.id]))
print(' Glucose %f -> 3HP %f' %
(engineered['EX_glc__D_e'], normalTargetFlux))
