def run_perturbation_MOMA(self, boundaries, objectives={}):
print("running MOMA...")
print(boundaries)
print(objectives)
moma_model = self.model.copy()
"""
if self.pFBA:
print("running pFBA")
old_sol = cobra.flux_analysis.parsimonious.optimize_minimal_flux(moma_model)
else:
print("running FBA")
old_sol = moma_model.optimize()
"""
reaction_position = self.reaction_position
# make perturbations
for (R, boundary_type, boundary_value) in boundaries:
i = reaction_position[R]
if boundary_type == 'l':
moma_model.reactions[i].lower_bound = boundary_value
elif boundary_type == 'u':
moma_model.reactions[i].upper_bound = boundary_value
if objectives:
#model.objective = model.reactions[reaction_position[objective["objective_id"]]]
# TODO
moma_model.objective = {
moma_model.reactions[self.reaction_position[r_id]]:
int(objectives[r_id])
for r_id in objectives
}
#print(model.objective)
self.perturbed_sol = moma.moma(self.model, moma_model)
self.perturbed_fluxes = self.perturbed_sol.x_dict
self.perturbed_fluxes = {
x: self.perturbed_fluxes[x]
for x in self.perturbed_fluxes if x in self.fluxes
}
def assess_medium_component_essentiality(cobra_model, the_components=None,
the_medium=None,
medium_compartment='e', solver='glpk',
the_condition=None, method='fba'):
"""Determines which components in an in silico medium are essential for
growth in the context of the remaining components.
cobra_model: A Model object.
the_components: None or a list of external boundary reactions that will be
sequentially disabled.
the_medium: Is None, a string, or a dictionary. If a string then the
initialize_growth_medium function expects that the_model has an attribute
dictionary called media_compositions, which is a dictionary of dictionaries
for various medium compositions. Where a medium composition is a
dictionary of external boundary reaction ids for the medium components and
the external boundary fluxes for each medium component.
medium_compartment: the compartment in which the boundary reactions
supplying the medium components exist
NOTE: that these fluxes must be negative because the convention is
backwards means something is feed into the system.
solver: 'glpk', 'gurobi', or 'cplex'
returns: essentiality_dict: A dictionary providing the maximum growth rate
accessible when the respective component is removed from the medium.
"""
if method.lower() == 'moma':
wt_model = cobra_model.copy()
cobra_model = cobra_model.copy()
if isinstance(the_medium, str):
try:
the_medium = cobra_model.media_compositions[the_medium]
except:
raise Exception(
the_medium + " is not in cobra_model.media_compositions")
if the_medium is not None:
initialize_growth_medium(cobra_model, the_medium, medium_compartment)
if the_components is None:
the_components = the_medium.keys()
if not the_components:
raise Exception("You need to specify the_components or the_medium")
essentiality_dict = {}
for the_component in the_components:
the_reaction = cobra_model.reactions.get_by_id(the_component)
original_lower_bound = float(the_reaction.lower_bound)
the_reaction.lower_bound = 0.
if method.lower() == 'fba':
cobra_model.optimize(solver=solver)
objective_value = cobra_model.solution.f
elif method.lower() == 'moma':
objective_value = moma(wt_model, cobra_model, solver=solver)[
'objective_value']
essentiality_dict[the_component] = objective_value
the_reaction.lower_bound = original_lower_bound
return(essentiality_dict)
def assess_medium_component_essentiality(
cobra_model,
the_components=None,
the_medium=None,
solver="glpk",
the_problem="return",
the_condition=None,
method="fba",
):
"""Deterimes which components in an in silico medium are essential for growth in the
context of the remaining components.
cobra_model: A Model object.
the_components: None or a list of exchange reactions that will be sequentially
disabled.
the_medium: Is None, a string, or a dictionary. If a string then the
initialize_growth_medium function expects that the_model has an
attribute dictionary called media_compositions, which is a dictionary of
dictionaries for various medium compositions. Where a medium
composition is a dictionary of exchange reaction ids for the medium
components and the exchange fluxes for each medium component; note that
these fluxes must be negative because they are being exchanged into the
system.
solver: 'glpk', 'gurobi', or 'cplex'
the_problem: Is None, 'return', or an LP model object for the solver.
the_condition: None or a String that provides a description of the medium
simulation
returns:
essentiality_dict: A dictionary providing the minimum lower bounds for
each component of the growth medium.
"""
warn("assess_medium_component_essentiality needs to be updated to " + "deal with new style reactions")
from cobra.core.ArrayBasedModel import ArrayBasedModel
if method.lower() == "moma":
wt_model = ArrayBasedModel(cobra_model.copy())
if isinstance(cobra_model, tuple):
if len(cobra_model) == 3:
the_condition = cobra_model[2]
the_components = cobra_model[1]
cobra_model = cobra_model[0]
cobra_model = ArrayBasedModel(cobra_model.copy())
if not the_components:
if the_medium:
if hasattr(the_medium, "keys") or (
hasattr(cobra_model, "media_compositions") and the_medium in cobra_model.media_compositions
):
initialize_growth_medium(cobra_model, the_medium)
the_components = cobra_model.media_compositions[the_medium]
else:
raise Exception("%s is not a dict and not in the model's media list" % the_medium)
else:
raise Exception("You need to specify the_components or the_medium")
essentiality_dict = {}
for the_component in the_components:
component_index = cobra_model.reactions.index(the_component)
tmp_lb = float(cobra_model.lower_bounds[component_index])
cobra_model.reactions[component_index].lower_bound = cobra_model.lower_bounds[component_index] = 0
if method.lower() == "fba":
cobra_model.optimize(solver=solver, the_problem=the_problem)
objective_value = cobra_model.solution.f
elif method.lower() == "moma":
objective_value = moma(wt_model, cobra_model, solver=solver)["objective_value"]
essentiality_dict[the_component] = objective_value
cobra_model.reactions[component_index].lower_bound = cobra_model.lower_bounds[component_index] = tmp_lb
if the_condition:
essentiality_dict["the_condition"] = the_condition
return essentiality_dict
def MOMA2mutant(self, mutant_model, objective_sense='maximize', solver=None, tolerance_optimality=1e-8, tolerance_feasibility=1e-8,minimize_norm=False, the_problem='return', lp_method=0,combined_model=None, norm_type='euclidean'):
moma.moma(self, mutant_model=mutant_model, objective_sense=objective_sense, solver=solver, tolerance_optimality=tolerance_optimality, tolerance_feasibility=tolerance_feasibility,minimize_norm=minimize_norm, the_problem=the_problem, lp_method=lp_method,combined_model=combined_model, norm_type=norm_type)
