def run(self,
max_knockouts=5,
biomass=None,
target=None,
max_results=1,
*args,
**kwargs):
"""
Perform the OptKnock simulation
Parameters
----------
target: str, Metabolite or Reaction
The design target
biomass: str, Metabolite or Reaction
The biomass definition in the model
max_knockouts: int
Max number of knockouts allowed
max_results: int
Max number of different designs to return if found
Returns
-------
OptKnockResult
"""
# TODO: why not required arguments?
if biomass is None or target is None:
raise ValueError('missing biomass and/or target reaction')
target = get_reaction_for(self._model, target, add=False)
biomass = get_reaction_for(self._model, biomass, add=False)
knockout_list = []
fluxes_list = []
production_list = []
biomass_list = []
loader_id = ui.loading()
with self._model:
self._model.objective = target.id
self._number_of_knockouts_constraint.lb = self._number_of_knockouts_constraint.ub - max_knockouts
count = 0
while count < max_results:
try:
solution = self._model.optimize(raise_error=True)
except OptimizationError as e:
logger.debug(
"Problem could not be solved. Terminating and returning "
+ str(count) + " solutions")
logger.debug(str(e))
break
knockouts = tuple(reaction
for y, reaction in self._y_vars.items()
if round(y.primal, 3) == 0)
assert len(knockouts) <= max_knockouts
if self.reaction_groups:
combinations = decompose_reaction_groups(
self.reaction_groups, knockouts)
for kos in combinations:
knockout_list.append({r.id for r in kos})
fluxes_list.append(solution.fluxes)
production_list.append(solution.f)
biomass_list.append(solution.fluxes[biomass.id])
else:
knockout_list.append({r.id for r in knockouts})
fluxes_list.append(solution.fluxes)
production_list.append(solution.f)
biomass_list.append(solution.fluxes[biomass.id])
# Add an integer cut
y_vars_to_cut = [
y for y in self._y_vars if round(y.primal, 3) == 0
]
integer_cut = self._model.solver.interface.Constraint(
Add(*y_vars_to_cut),
lb=1,
name="integer_cut_" + str(count))
if len(knockouts) < max_knockouts:
self._number_of_knockouts_constraint.lb = self._number_of_knockouts_constraint.ub - len(
knockouts)
self._model.add_cons_vars(integer_cut)
count += 1
ui.stop_loader(loader_id)
return OptKnockResult(self._original_model, knockout_list,
fluxes_list, production_list, biomass_list,
target.id, biomass)
def run(self, max_knockouts=5, biomass=None, target=None, max_results=1, *args, **kwargs):
"""
Perform the OptKnock simulation
Parameters
----------
target: str, Metabolite or Reaction
The design target
biomass: str, Metabolite or Reaction
The biomass definition in the model
max_knockouts: int
Max number of knockouts allowed
max_results: int
Max number of different designs to return if found
Returns
-------
OptKnockResult
"""
target = self._model.reaction_for(target, add=False)
biomass = self._model.reaction_for(biomass, add=False)
knockout_list = []
fluxes_list = []
production_list = []
biomass_list = []
loader_id = ui.loading()
with TimeMachine() as tm:
self._model.objective = target.id
self._number_of_knockouts_constraint.lb = self._number_of_knockouts_constraint.ub - max_knockouts
count = 0
while count < max_results:
try:
solution = self._model.solve()
except SolveError as e:
logger.debug("Problem could not be solved. Terminating and returning " + str(count) + " solutions")
logger.debug(str(e))
break
knockouts = set(reaction.id for y, reaction in self._y_vars.items() if round(y.primal, 3) == 0)
assert len(knockouts) <= max_knockouts
knockout_list.append(knockouts)
fluxes_list.append(solution.fluxes)
production_list.append(solution.f)
biomass_list.append(solution.fluxes[biomass.id])
# Add an integer cut
y_vars_to_cut = [y for y in self._y_vars if round(y.primal, 3) == 0]
integer_cut = self._model.solver.interface.Constraint(Add(*y_vars_to_cut),
lb=1,
name="integer_cut_" + str(count))
if len(knockouts) < max_knockouts:
self._number_of_knockouts_constraint.lb = self._number_of_knockouts_constraint.ub - len(knockouts)
tm(do=partial(self._model.solver.add, integer_cut),
undo=partial(self._model.solver.remove, integer_cut))
count += 1
ui.stop_loader(loader_id)
return OptKnockResult(self._original_model, knockout_list, fluxes_list,
production_list, biomass_list, target.id, biomass)
def run(self, max_knockouts=5, biomass=None, target=None, max_results=1, *args, **kwargs):
"""
Perform the OptKnock simulation
Parameters
----------
target: str, Metabolite or Reaction
The design target
biomass: str, Metabolite or Reaction
The biomass definition in the model
max_knockouts: int
Max number of knockouts allowed
max_results: int
Max number of different designs to return if found
Returns
-------
OptKnockResult
"""
target = self._model._reaction_for(target, add=False)
biomass = self._model._reaction_for(biomass, add=False)
knockout_list = []
fluxes_list = []
production_list = []
biomass_list = []
loader_id = ui.loading()
with TimeMachine() as tm:
self._model.objective = target.id
self._number_of_knockouts_constraint.lb = self._number_of_knockouts_constraint.ub - max_knockouts
count = 0
while count < max_results:
try:
solution = self._model.solve()
except SolveError as e:
logger.debug("Problem could not be solved. Terminating and returning " + str(count) + " solutions")
logger.debug(str(e))
break
knockouts = set(reaction.id for y, reaction in self._y_vars.items() if round(y.primal, 3) == 0)
assert len(knockouts) <= max_knockouts
knockout_list.append(knockouts)
fluxes_list.append(solution.fluxes)
production_list.append(solution.f)
biomass_list.append(solution.fluxes[biomass.id])
# Add an integer cut
y_vars_to_cut = [y for y in self._y_vars if round(y.primal, 3) == 0]
integer_cut = self._model.solver.interface.Constraint(Add(*y_vars_to_cut),
lb=1,
name="integer_cut_" + str(count))
if len(knockouts) < max_knockouts:
self._number_of_knockouts_constraint.lb = self._number_of_knockouts_constraint.ub - len(knockouts)
tm(do=partial(self._model.solver.add, integer_cut),
undo=partial(self._model.solver.remove, integer_cut))
count += 1
ui.stop_loader(loader_id)
return OptKnockResult(self._original_model, knockout_list, fluxes_list,
production_list, biomass_list, target.id, biomass)