def _load_constraintbased_model(sbml_model, model):
model_extended = model.copy()
_load_compartments(sbml_model, model_extended)
_load_metabolites(sbml_model, model_extended)
_load_reactions(sbml_model, model_extended)
_load_cobra_bounds(sbml_model, model_extended)
_load_cobra_objective(sbml_model, model_extended)
reactions_ids = list(model_extended.reactions.keys())[len(model.reactions):]
return (model_extended, reactions_ids)
def _load_constraintbased_model(sbml_model, model):
model_extended = model.copy()
_load_compartments(sbml_model, model_extended)
_load_metabolites(sbml_model, model_extended)
_load_reactions(sbml_model, model_extended)
_load_cobra_bounds(sbml_model, model_extended)
_load_cobra_objective(sbml_model, model_extended)
reactions_ids = model_extended.reactions.keys()[len(model.reactions):]
return (model_extended, reactions_ids)
def load_kinetic_model(filename, kmap=None):
"""
Load the dynamic model present in a SBML file.
Args:
filename (str): SBML file.
kmap (dict): Dictionary with the parameters that can be used in the strain
optimization process for each reaction.{id_reaction: [param1, param2]}
Returns: KineticModel
Contains all information related with the dynamic model
(reactions, kinetic equations, metabolites, compartments, etc.)
"""
document = readSBMLFromFile(filename)
sbmlModel = document.getModel()
if sbmlModel is None:
raise IOError('Failed to load model.')
model = KineticModel(sbmlModel.getId())
_load_compartments(sbmlModel, model)
_load_metabolites(sbmlModel, model)
_load_reactions(sbmlModel, model)
_load_concentrations(sbmlModel, model)
_load_global_parameters(sbmlModel, model)
_load_local_parameters(sbmlModel, model)
_load_ratelaws(sbmlModel, model)
_load_assignment_rules(sbmlModel, model)
# parse rates, rules and xdot expressions
model._set_parsed_attr()
if isinstance(kmap, str):
aux = OrderedDict([(rId, re.findall(kmap, ratelaw))
for rId, ratelaw in model.ratelaws.items()])
# aux = OrderedDict([(rId , [rId+"_"+x for x in re.findall("(rmax\w*)", ratelaw)])
# for rId, ratelaw in model.ratelaws.items()]) # CHASSAGNOLE
model.reacParamsFactors = OrderedDict([(rId, params)
for rId, params in aux.items()
if len(params) > 0])
else:
model.set_reactions_parameters_factors(kmap)
return model