def test_pfba(self):
fba_solution = fba(self.model)
fba_flux_sum = sum((abs(val) for val in list(fba_solution.fluxes.values())))
pfba_solution = pfba(self.model)
pfba_flux_sum = sum((abs(val) for val in list(pfba_solution.fluxes.values())))
# looks like GLPK finds a parsimonious solution without the flux minimization objective
self.assertTrue((pfba_flux_sum - fba_flux_sum) < 1e-6,
msg="FBA sum is suppose to be lower than PFBA (was %f)" % (pfba_flux_sum - fba_flux_sum))
def test_pfba_iJO(self):
fba_solution = fba(iJO_MODEL)
fba_flux_sum = sum((abs(val) for val in list(fba_solution.fluxes.values())))
pfba_solution = pfba(iJO_MODEL)
pfba_flux_sum = sum((abs(val) for val in list(pfba_solution.fluxes.values())))
print(pfba_flux_sum)
self.assertTrue((pfba_flux_sum - fba_flux_sum) < 1e-6,
msg="FBA sum is suppose to be lower than PFBA (was %f)" % (pfba_flux_sum - fba_flux_sum))
def test_fba_with_reaction_filter(self, core_model):
original_objective = core_model.objective
solution = fba(
core_model,
reactions=['EX_o2_LPAREN_e_RPAREN_', 'EX_glc_LPAREN_e_RPAREN_'])
assert abs(solution.objective_value - 0.873921) < 0.000001
assert len(solution.fluxes) == 2
assert core_model.objective is original_objective
def test_pfba_ijo1366(self, ijo1366):
original_objective = ijo1366.objective
fba_solution = fba(ijo1366)
fba_flux_sum = sum((abs(val) for val in fba_solution.fluxes.values))
pfba_solution = pfba(ijo1366)
pfba_flux_sum = sum((abs(val) for val in pfba_solution.fluxes.values))
assert (pfba_flux_sum - fba_flux_sum) < 1e-6, \
"FBA sum is suppose to be lower than PFBA (was %f)" % (pfba_flux_sum - fba_flux_sum)
assert ijo1366.objective.expression == original_objective.expression
def test_pfba_ijo1366(self, ijo1366):
original_objective = ijo1366.objective
fba_solution = fba(ijo1366)
fba_flux_sum = sum((abs(val) for val in fba_solution.fluxes.values))
pfba_solution = pfba(ijo1366)
pfba_flux_sum = sum((abs(val) for val in pfba_solution.fluxes.values))
assert (pfba_flux_sum - fba_flux_sum) < 1e-6, \
"FBA sum is suppose to be lower than PFBA (was %f)" % (pfba_flux_sum - fba_flux_sum)
assert ijo1366.objective.expression == original_objective.expression
def test_pfba_iJO(self):
original_objective = self.model.objective
fba_solution = fba(iJO_MODEL)
fba_flux_sum = sum((abs(val) for val in fba_solution.fluxes.values()))
pfba_solution = pfba(iJO_MODEL)
pfba_flux_sum = sum((abs(val) for val in pfba_solution.fluxes.values()))
self.assertTrue((pfba_flux_sum - fba_flux_sum) < 1e-6,
msg="FBA sum is suppose to be lower than PFBA (was %f)" % (pfba_flux_sum - fba_flux_sum))
self.assertIs(self.model.objective, original_objective)
def test_pfba_iJO(self):
original_objective = self.model.objective
fba_solution = fba(iJO_MODEL)
fba_flux_sum = sum((abs(val) for val in fba_solution.fluxes.values()))
pfba_solution = pfba(iJO_MODEL)
pfba_flux_sum = sum((abs(val) for val in pfba_solution.fluxes.values()))
self.assertTrue((pfba_flux_sum - fba_flux_sum) < 1e-6,
msg="FBA sum is suppose to be lower than PFBA (was %f)" % (pfba_flux_sum - fba_flux_sum))
self.assertIs(self.model.objective, original_objective)
def test_pfba(self):
original_objective = self.model.objective
fba_solution = fba(self.model)
fba_flux_sum = sum((abs(val) for val in list(fba_solution.fluxes.values())))
pfba_solution = pfba(self.model)
pfba_flux_sum = sum((abs(val) for val in list(pfba_solution.fluxes.values())))
# looks like GLPK finds a parsimonious solution without the flux minimization objective
self.assertTrue((pfba_flux_sum - fba_flux_sum) < 1e-6,
msg="FBA sum is suppose to be lower than PFBA (was %f)" % (pfba_flux_sum - fba_flux_sum))
self.assertIs(self.model.objective, original_objective)
def test_pfba(self, core_model):
original_objective = core_model.objective
fba_solution = fba(core_model)
fba_flux_sum = sum((abs(val) for val in list(fba_solution.fluxes.values)))
pfba_solution = pfba(core_model)
pfba_flux_sum = sum((abs(val) for val in list(pfba_solution.fluxes.values)))
# looks like GLPK finds a parsimonious solution without the flux minimization objective
assert (pfba_flux_sum - fba_flux_sum) < 1e-6, \
"FBA sum is suppose to be lower than PFBA (was %f)" % (pfba_flux_sum - fba_flux_sum)
assert core_model.objective.expression == original_objective.expression
def test_pfba(self, core_model):
original_objective = core_model.objective
fba_solution = fba(core_model)
fba_flux_sum = sum((abs(val) for val in list(fba_solution.fluxes.values)))
pfba_solution = pfba(core_model)
pfba_flux_sum = sum((abs(val) for val in list(pfba_solution.fluxes.values)))
# looks like GLPK finds a parsimonious solution without the flux minimization objective
assert (pfba_flux_sum - fba_flux_sum) < 1e-6, \
"FBA sum is suppose to be lower than PFBA (was %f)" % (pfba_flux_sum - fba_flux_sum)
assert core_model.objective.expression == original_objective.expression
def test_pfba_iJO(self):
fba_solution = fba(iJO_MODEL)
fba_flux_sum = sum(
(abs(val) for val in list(fba_solution.fluxes.values())))
pfba_solution = pfba(iJO_MODEL)
pfba_flux_sum = sum(
(abs(val) for val in list(pfba_solution.fluxes.values())))
print(pfba_flux_sum)
self.assertTrue(
(pfba_flux_sum - fba_flux_sum) < 1e-6,
msg="FBA sum is suppose to be lower than PFBA (was %f)" %
(pfba_flux_sum - fba_flux_sum))
def test_fba(self):
solution = fba(self.model)
original_objective = self.model.objective
self.assertAlmostEqual(solution.objective_value, 0.873921, delta=0.000001)
self.assertEqual(len(solution.fluxes), len(self.model.reactions))
self.assertIs(self.model.objective, original_objective)
def test_fba(self, core_model):
solution = fba(core_model)
original_objective = core_model.objective
assert abs(solution.objective_value - 0.873921) < 0.000001
assert len(solution.fluxes) == len(core_model.reactions)
assert core_model.objective.expression == original_objective.expression
def test_fba_with_reaction_filter(self):
original_objective = self.model.objective
solution = fba(self.model, reactions=['EX_o2_LPAREN_e_RPAREN_', 'EX_glc_LPAREN_e_RPAREN_'])
self.assertAlmostEqual(solution.objective_value, 0.873921, delta=0.000001)
self.assertEqual(len(solution.fluxes), 2)
self.assertIs(self.model.objective, original_objective)
def test_fba(self):
solution = fba(self.model)
self.assertAlmostEqual(solution.objective_value,
0.873921,
delta=0.000001)
def test_fba(self):
solution = fba(self.model)
self.assertAlmostEqual(solution.objective_value, 0.873921, delta=0.000001)
def display_on_map(self, index=0, map_name=None, palette="YlGnBu"):
with TimeMachine() as tm:
for ko in self.data_frame.loc[index, "reactions"]:
self._model.reactions.get_by_id(ko).knock_out(tm)
fluxes = fba(self._model)
fluxes.display_on_map(map_name=map_name, palette=palette)
def display_on_map(self, index=0, map_name=None, palette="YlGnBu"):
with self._model:
for ko in self.data_frame.loc[index, "reactions"]:
self._model.reactions.get_by_id(ko).knock_out()
fluxes = fba(self._model)
fluxes.display_on_map(map_name=map_name, palette=palette)
def run(self,
target=None,
max_enforced_flux=0.9,
number_of_results=10,
exclude=(),
simulation_method=fba,
simulation_kwargs=None):
"""
Performs a Flux Scanning based on Enforced Objective Flux (FSEOF) analysis.
Parameters
----------
target: str, Reaction, Metabolite
The target for optimization.
max_enforced_flux : float, optional
The maximal flux of secondary_objective that will be enforced, relative to the theoretical maximum (
defaults to 0.9).
number_of_results : int, optional
The number of enforced flux levels (defaults to 10).
exclude : Iterable of reactions or reaction ids that will not be included in the output.
Returns
-------
FseofResult
An object containing the identified reactions and the used parameters.
References
----------
.. [1] H. S. Choi, S. Y. Lee, T. Y. Kim, and H. M. Woo, 'In silico identification of gene amplification targets
for improvement of lycopene production.,' Appl Environ Microbiol, vol. 76, no. 10, pp. 3097–3105, May 2010.
"""
model = self.model
target = get_reaction_for(model, target)
simulation_kwargs = simulation_kwargs if simulation_kwargs is not None else {}
simulation_kwargs['objective'] = self.primary_objective
if 'reference' not in simulation_kwargs:
reference = simulation_kwargs['reference'] = pfba(
model, **simulation_kwargs)
else:
reference = simulation_kwargs['reference']
ndecimals = config.ndecimals
# Exclude list
exclude = list(exclude) + model.exchanges
exclude_ids = [target.id]
for reaction in exclude:
if isinstance(reaction, Reaction):
exclude_ids.append(reaction.id)
else:
exclude_ids.append(reaction)
with TimeMachine() as tm:
tm(do=int,
undo=partial(setattr, model, "objective", model.objective))
tm(do=int,
undo=partial(setattr, target, "lower_bound",
target.lower_bound))
tm(do=int,
undo=partial(setattr, target, "upper_bound",
target.upper_bound))
# Find initial flux of enforced reaction
initial_fluxes = reference.fluxes
initial_flux = round(initial_fluxes[target.id], ndecimals)
# Find theoretical maximum of enforced reaction
max_theoretical_flux = round(
fba(model, objective=target.id,
reactions=[target.id]).fluxes[target.id], ndecimals)
max_flux = max_theoretical_flux * max_enforced_flux
# Calculate enforcement levels
levels = [
initial_flux + (i + 1) *
(max_flux - initial_flux) / number_of_results
for i in range(number_of_results)
]
# FSEOF results
results = {reaction.id: [] for reaction in model.reactions}
for level in levels:
target.lower_bound = level
target.upper_bound = level
solution = simulation_method(model, **simulation_kwargs)
for reaction_id, flux in solution.fluxes.iteritems():
results[reaction_id].append(round(flux, ndecimals))
# Test each reaction
fseof_reactions = []
for reaction_id, fluxes in results.items():
if reaction_id not in exclude_ids \
and max(abs(max(fluxes)), abs(min(fluxes))) > abs(reference[reaction_id]) \
and min(fluxes) * max(fluxes) >= 0:
fseof_reactions.append(model.reactions.get_by_id(reaction_id))
results = {rea.id: results[rea.id] for rea in fseof_reactions}
run_args = dict(max_enforced_flux=max_enforced_flux,
number_of_results=number_of_results,
solution_method=simulation_method,
simulation_kwargs=simulation_kwargs,
exclude=exclude)
return FSEOFResult(fseof_reactions, target, model,
self.primary_objective, levels, results, run_args,
reference)
def test_fba(self):
solution = fba(self.model)
original_objective = self.model.objective
self.assertAlmostEqual(solution.objective_value, 0.873921, delta=0.000001)
self.assertEqual(len(solution.fluxes), len(self.model.reactions))
self.assertIs(self.model.objective, original_objective)
def test_fba(self, core_model):
solution = fba(core_model)
original_objective = core_model.objective
assert abs(solution.objective_value - 0.873921) < 0.000001
assert len(solution.fluxes) == len(core_model.reactions)
assert core_model.objective.expression == original_objective.expression
def test_fba_with_reaction_filter(self, core_model):
original_objective = core_model.objective
solution = fba(core_model, reactions=['EX_o2_LPAREN_e_RPAREN_', 'EX_glc_LPAREN_e_RPAREN_'])
assert abs(solution.objective_value - 0.873921) < 0.000001
assert len(solution.fluxes) == 2
assert core_model.objective.expression == original_objective.expression
def run(self, target=None, max_enforced_flux=0.9, number_of_results=10, exclude=(), simulation_method=fba,
simulation_kwargs=None):
"""
Performs a Flux Scanning based on Enforced Objective Flux (FSEOF) analysis.
Parameters
----------
target: str, Reaction, Metabolite
The target for optimization.
max_enforced_flux : float, optional
The maximal flux of secondary_objective that will be enforced, relative to the theoretical maximum (
defaults to 0.9).
number_of_results : int, optional
The number of enforced flux levels (defaults to 10).
exclude : Iterable of reactions or reaction ids that will not be included in the output.
Returns
-------
FseofResult
An object containing the identified reactions and the used parameters.
References
----------
.. [1] H. S. Choi, S. Y. Lee, T. Y. Kim, and H. M. Woo, 'In silico identification of gene amplification targets
for improvement of lycopene production.,' Appl Environ Microbiol, vol. 76, no. 10, pp. 3097–3105, May 2010.
"""
model = self.model
target = get_reaction_for(model, target)
simulation_kwargs = simulation_kwargs if simulation_kwargs is not None else {}
simulation_kwargs['objective'] = self.primary_objective
if 'reference' not in simulation_kwargs:
reference = simulation_kwargs['reference'] = pfba(model, **simulation_kwargs)
else:
reference = simulation_kwargs['reference']
ndecimals = config.ndecimals
# Exclude list
exclude = list(exclude) + model.boundary
exclude_ids = [target.id]
for reaction in exclude:
if isinstance(reaction, Reaction):
exclude_ids.append(reaction.id)
else:
exclude_ids.append(reaction)
with TimeMachine() as tm:
tm(do=int, undo=partial(setattr, model, "objective", model.objective))
tm(do=int, undo=partial(setattr, target, "lower_bound", target.lower_bound))
tm(do=int, undo=partial(setattr, target, "upper_bound", target.upper_bound))
# Find initial flux of enforced reaction
initial_fluxes = reference.fluxes
initial_flux = round(initial_fluxes[target.id], ndecimals)
# Find theoretical maximum of enforced reaction
max_theoretical_flux = round(fba(model, objective=target.id, reactions=[target.id]).fluxes[target.id],
ndecimals)
max_flux = max_theoretical_flux * max_enforced_flux
# Calculate enforcement levels
levels = [initial_flux + (i + 1) * (max_flux - initial_flux) / number_of_results for i in
range(number_of_results)]
# FSEOF results
results = {reaction.id: [] for reaction in model.reactions}
for level in levels:
target.lower_bound = level
target.upper_bound = level
solution = simulation_method(model, **simulation_kwargs)
for reaction_id, flux in solution.fluxes.iteritems():
results[reaction_id].append(round(flux, ndecimals))
# Test each reaction
fseof_reactions = []
for reaction_id, fluxes in results.items():
if reaction_id not in exclude_ids \
and max(abs(max(fluxes)), abs(min(fluxes))) > abs(reference[reaction_id]) \
and min(fluxes) * max(fluxes) >= 0:
fseof_reactions.append(model.reactions.get_by_id(reaction_id))
results = {rea.id: results[rea.id] for rea in fseof_reactions}
run_args = dict(max_enforced_flux=max_enforced_flux,
number_of_results=number_of_results,
solution_method=simulation_method,
simulation_kwargs=simulation_kwargs,
exclude=exclude)
return FSEOFResult(fseof_reactions, target, model, self.primary_objective, levels, results, run_args, reference)
def test_fba_with_reaction_filter(self):
original_objective = self.model.objective
solution = fba(self.model, reactions=['EX_o2_LPAREN_e_RPAREN_', 'EX_glc_LPAREN_e_RPAREN_'])
self.assertAlmostEqual(solution.objective_value, 0.873921, delta=0.000001)
self.assertEqual(len(solution.fluxes), 2)
self.assertIs(self.model.objective, original_objective)
