def test_set_id(self, solved_model):
solution, model = solved_model
met = Metabolite("test")
with pytest.raises(TypeError):
setattr(met, 'id', 1)
model.add_metabolites([met])
with pytest.raises(ValueError):
setattr(met, "id", 'g6p_c')
met.id = "test2"
assert "test2" in model.metabolites
assert "test" not in model.metabolites
def test_set_id(self, solved_model):
solution, model = solved_model
met = Metabolite("test")
with pytest.raises(TypeError):
setattr(met, 'id', 1)
model.add_metabolites([met])
with pytest.raises(ValueError):
setattr(met, "id", 'g6p_c')
met.id = "test2"
assert "test2" in model.metabolites
assert "test" not in model.metabolites
def create_cobra_model_from_sbml_file(sbml_filename, old_sbml=False,
legacy_metabolite=False,
print_time=False, use_hyphens=False):
"""convert an SBML XML file into a cobra.Model object.
Supports SBML Level 2 Versions 1 and 4. The function will detect if the
SBML fbc package is used in the file and run the converter if the fbc
package is used.
Parameters
----------
sbml_filename: string
old_sbml: bool
Set to True if the XML file has metabolite formula appended to
metabolite names. This was a poorly designed artifact that persists in
some models.
legacy_metabolite: bool
If True then assume that the metabolite id has the compartment id
appended after an underscore (e.g. _c for cytosol). This has not been
implemented but will be soon.
print_time: bool
deprecated
use_hyphens: bool
If True, double underscores (__) in an SBML ID will be converted to
hyphens
Returns
-------
Model : The parsed cobra model
"""
if not libsbml:
raise ImportError('create_cobra_model_from_sbml_file '
'requires python-libsbml')
__default_lower_bound = -1000
__default_upper_bound = 1000
__default_objective_coefficient = 0
# Ensure that the file exists
if not isfile(sbml_filename):
raise IOError('Your SBML file is not found: %s' % sbml_filename)
# Expressions to change SBML Ids to Palsson Lab Ids
metabolite_re = re.compile('^M_')
reaction_re = re.compile('^R_')
compartment_re = re.compile('^C_')
if print_time:
warn("print_time is deprecated", DeprecationWarning)
model_doc = libsbml.readSBML(sbml_filename)
if model_doc.getPlugin("fbc") is not None:
from libsbml import ConversionProperties, LIBSBML_OPERATION_SUCCESS
conversion_properties = ConversionProperties()
conversion_properties.addOption(
"convert fbc to cobra", True, "Convert FBC model to Cobra model")
result = model_doc.convert(conversion_properties)
if result != LIBSBML_OPERATION_SUCCESS:
raise Exception("Conversion of SBML+fbc to COBRA failed")
sbml_model = model_doc.getModel()
sbml_model_id = sbml_model.getId()
sbml_species = sbml_model.getListOfSpecies()
sbml_reactions = sbml_model.getListOfReactions()
sbml_compartments = sbml_model.getListOfCompartments()
compartment_dict = dict([(compartment_re.split(x.getId())[-1], x.getName())
for x in sbml_compartments])
if legacy_metabolite:
# Deal with the palsson lab appending the compartment id to the
# metabolite id
new_dict = {}
for the_id, the_name in compartment_dict.items():
if the_name == '':
new_dict[the_id[0].lower()] = the_id
else:
new_dict[the_id] = the_name
compartment_dict = new_dict
legacy_compartment_converter = dict(
[(v, k) for k, v in iteritems(compartment_dict)])
cobra_model = Model(sbml_model_id)
metabolites = []
metabolite_dict = {}
# Convert sbml_metabolites to cobra.Metabolites
for sbml_metabolite in sbml_species:
# Skip sbml boundary species
if sbml_metabolite.getBoundaryCondition():
continue
if (old_sbml or legacy_metabolite) and \
sbml_metabolite.getId().endswith('_b'):
# Deal with incorrect sbml from bigg.ucsd.edu
continue
tmp_metabolite = Metabolite()
metabolite_id = tmp_metabolite.id = sbml_metabolite.getId()
tmp_metabolite.compartment = compartment_re.split(
sbml_metabolite.getCompartment())[-1]
if legacy_metabolite:
if tmp_metabolite.compartment not in compartment_dict:
tmp_metabolite.compartment = legacy_compartment_converter[
tmp_metabolite.compartment]
tmp_metabolite.id = parse_legacy_id(
tmp_metabolite.id, tmp_metabolite.compartment,
use_hyphens=use_hyphens)
if use_hyphens:
tmp_metabolite.id = metabolite_re.split(
tmp_metabolite.id)[-1].replace('__', '-')
else:
# Just in case the SBML ids are ill-formed and use -
tmp_metabolite.id = metabolite_re.split(
tmp_metabolite.id)[-1].replace('-', '__')
tmp_metabolite.name = sbml_metabolite.getName()
tmp_formula = ''
tmp_metabolite.notes = parse_legacy_sbml_notes(
sbml_metabolite.getNotesString())
if sbml_metabolite.isSetCharge():
tmp_metabolite.charge = sbml_metabolite.getCharge()
if "CHARGE" in tmp_metabolite.notes:
note_charge = tmp_metabolite.notes["CHARGE"][0]
try:
note_charge = float(note_charge)
if note_charge == int(note_charge):
note_charge = int(note_charge)
except:
warn("charge of %s is not a number (%s)" %
(tmp_metabolite.id, str(note_charge)))
else:
if ((tmp_metabolite.charge is None) or
(tmp_metabolite.charge == note_charge)):
tmp_metabolite.notes.pop("CHARGE")
# set charge to the one from notes if not assigend before
# the same
tmp_metabolite.charge = note_charge
else: # tmp_metabolite.charge != note_charge
msg = "different charges specified for %s (%d and %d)"
msg = msg % (tmp_metabolite.id,
tmp_metabolite.charge, note_charge)
warn(msg)
# Chances are a 0 note charge was written by mistake. We
# will default to the note_charge in this case.
if tmp_metabolite.charge == 0:
tmp_metabolite.charge = note_charge
for the_key in tmp_metabolite.notes.keys():
if the_key.lower() == 'formula':
tmp_formula = tmp_metabolite.notes.pop(the_key)[0]
break
if tmp_formula == '' and old_sbml:
tmp_formula = tmp_metabolite.name.split('_')[-1]
tmp_metabolite.name = tmp_metabolite.name[:-len(tmp_formula) - 1]
tmp_metabolite.formula = tmp_formula
metabolite_dict.update({metabolite_id: tmp_metabolite})
metabolites.append(tmp_metabolite)
cobra_model.add_metabolites(metabolites)
# Construct the vectors and matrices for holding connectivity and numerical
# info to feed to the cobra toolbox.
# Always assume steady state simulations so b is set to 0
cobra_reaction_list = []
coefficients = {}
for sbml_reaction in sbml_reactions:
if use_hyphens:
# Change the ids to match conventions used by the Palsson lab.
reaction = Reaction(reaction_re.split(
sbml_reaction.getId())[-1].replace('__', '-'))
else:
# Just in case the SBML ids are ill-formed and use -
reaction = Reaction(reaction_re.split(
sbml_reaction.getId())[-1].replace('-', '__'))
cobra_reaction_list.append(reaction)
# reaction.exchange_reaction = 0
reaction.name = sbml_reaction.getName()
cobra_metabolites = {}
# Use the cobra.Metabolite class here
for sbml_metabolite in sbml_reaction.getListOfReactants():
tmp_metabolite_id = sbml_metabolite.getSpecies()
# This deals with boundary metabolites
if tmp_metabolite_id in metabolite_dict:
tmp_metabolite = metabolite_dict[tmp_metabolite_id]
cobra_metabolites[tmp_metabolite] = - \
sbml_metabolite.getStoichiometry()
for sbml_metabolite in sbml_reaction.getListOfProducts():
tmp_metabolite_id = sbml_metabolite.getSpecies()
# This deals with boundary metabolites
if tmp_metabolite_id in metabolite_dict:
tmp_metabolite = metabolite_dict[tmp_metabolite_id]
# Handle the case where the metabolite was specified both
# as a reactant and as a product.
if tmp_metabolite in cobra_metabolites:
warn("%s appears as a reactant and product %s" %
(tmp_metabolite_id, reaction.id))
cobra_metabolites[
tmp_metabolite] += sbml_metabolite.getStoichiometry()
# if the combined stoichiometry is 0, remove the metabolite
if cobra_metabolites[tmp_metabolite] == 0:
cobra_metabolites.pop(tmp_metabolite)
else:
cobra_metabolites[
tmp_metabolite] = sbml_metabolite.getStoichiometry()
# check for nan
for met, v in iteritems(cobra_metabolites):
if isnan(v) or isinf(v):
warn("invalid value %s for metabolite '%s' in reaction '%s'" %
(str(v), met.id, reaction.id))
reaction.add_metabolites(cobra_metabolites)
# Parse the kinetic law info here.
parameter_dict = {}
# If lower and upper bounds are specified in the Kinetic Law then
# they override the sbml reversible attribute. If they are not
# specified then the bounds are determined by getReversible.
if not sbml_reaction.getKineticLaw():
if sbml_reaction.getReversible():
parameter_dict['lower_bound'] = __default_lower_bound
parameter_dict['upper_bound'] = __default_upper_bound
else:
# Assume that irreversible reactions only proceed from left to
# right.
parameter_dict['lower_bound'] = 0
parameter_dict['upper_bound'] = __default_upper_bound
parameter_dict[
'objective_coefficient'] = __default_objective_coefficient
else:
for sbml_parameter in \
sbml_reaction.getKineticLaw().getListOfParameters():
parameter_dict[
sbml_parameter.getId().lower()] = sbml_parameter.getValue()
if 'lower_bound' in parameter_dict:
reaction.lower_bound = parameter_dict['lower_bound']
elif 'lower bound' in parameter_dict:
reaction.lower_bound = parameter_dict['lower bound']
elif sbml_reaction.getReversible():
reaction.lower_bound = __default_lower_bound
else:
reaction.lower_bound = 0
if 'upper_bound' in parameter_dict:
reaction.upper_bound = parameter_dict['upper_bound']
elif 'upper bound' in parameter_dict:
reaction.upper_bound = parameter_dict['upper bound']
else:
reaction.upper_bound = __default_upper_bound
objective_coefficient = parameter_dict.get(
'objective_coefficient', parameter_dict.get(
'objective_coefficient', __default_objective_coefficient))
if objective_coefficient != 0:
coefficients[reaction] = objective_coefficient
# ensure values are not set to nan or inf
if isnan(reaction.lower_bound) or isinf(reaction.lower_bound):
reaction.lower_bound = __default_lower_bound
if isnan(reaction.upper_bound) or isinf(reaction.upper_bound):
reaction.upper_bound = __default_upper_bound
reaction_note_dict = parse_legacy_sbml_notes(
sbml_reaction.getNotesString())
# Parse the reaction notes.
# POTENTIAL BUG: DEALING WITH LEGACY 'SBML' THAT IS NOT IN A
# STANDARD FORMAT
# TODO: READ IN OTHER NOTES AND GIVE THEM A reaction_ prefix.
# TODO: Make sure genes get added as objects
if 'GENE ASSOCIATION' in reaction_note_dict:
rule = reaction_note_dict['GENE ASSOCIATION'][0]
try:
rule.encode('ascii')
except (UnicodeEncodeError, UnicodeDecodeError):
warn("gene_reaction_rule '%s' is not ascii compliant" % rule)
if rule.startswith(""") and rule.endswith("""):
rule = rule[6:-6]
reaction.gene_reaction_rule = rule
if 'GENE LIST' in reaction_note_dict:
reaction.systematic_names = reaction_note_dict['GENE LIST'][0]
elif ('GENES' in reaction_note_dict and
reaction_note_dict['GENES'] != ['']):
reaction.systematic_names = reaction_note_dict['GENES'][0]
elif 'LOCUS' in reaction_note_dict:
gene_id_to_object = dict([(x.id, x) for x in reaction._genes])
for the_row in reaction_note_dict['LOCUS']:
tmp_row_dict = {}
the_row = 'LOCUS:' + the_row.lstrip('_').rstrip('#')
for the_item in the_row.split('#'):
k, v = the_item.split(':')
tmp_row_dict[k] = v
tmp_locus_id = tmp_row_dict['LOCUS']
if 'TRANSCRIPT' in tmp_row_dict:
tmp_locus_id = tmp_locus_id + \
'.' + tmp_row_dict['TRANSCRIPT']
if 'ABBREVIATION' in tmp_row_dict:
gene_id_to_object[tmp_locus_id].name = tmp_row_dict[
'ABBREVIATION']
if 'SUBSYSTEM' in reaction_note_dict:
reaction.subsystem = reaction_note_dict.pop('SUBSYSTEM')[0]
reaction.notes = reaction_note_dict
# Now, add all of the reactions to the model.
cobra_model.id = sbml_model.getId()
# Populate the compartment list - This will be done based on
# cobra.Metabolites in cobra.Reactions in the future.
cobra_model.compartments = compartment_dict
cobra_model.add_reactions(cobra_reaction_list)
set_objective(cobra_model, coefficients)
return cobra_model
def from_mat_struct(mat_struct, model_id=None, inf=inf):
"""create a model from the COBRA toolbox struct
The struct will be a dict read in by scipy.io.loadmat
"""
m = mat_struct
if m.dtype.names is None:
raise ValueError("not a valid mat struct")
if not {"rxns", "mets", "S", "lb", "ub"} <= set(m.dtype.names):
raise ValueError("not a valid mat struct")
if "c" in m.dtype.names:
c_vec = m["c"][0, 0]
else:
c_vec = None
warn("objective vector 'c' not found")
model = Model()
if model_id is not None:
model.id = model_id
elif "description" in m.dtype.names:
description = m["description"][0, 0][0]
if not isinstance(description, string_types) and len(description) > 1:
model.id = description[0]
warn("Several IDs detected, only using the first.")
else:
model.id = description
else:
model.id = "imported_model"
for i, name in enumerate(m["mets"][0, 0]):
new_metabolite = Metabolite()
new_metabolite.id = str(name[0][0])
if all(var in m.dtype.names
for var in ['metComps', 'comps', 'compNames']):
comp_index = m["metComps"][0, 0][i][0] - 1
new_metabolite.compartment = m['comps'][0, 0][comp_index][0][0]
if new_metabolite.compartment not in model.compartments:
comp_name = m['compNames'][0, 0][comp_index][0][0]
model.compartments[new_metabolite.compartment] = comp_name
else:
new_metabolite.compartment = _get_id_compartment(new_metabolite.id)
if new_metabolite.compartment not in model.compartments:
model.compartments[
new_metabolite.compartment] = new_metabolite.compartment
try:
new_metabolite.name = str(m["metNames"][0, 0][i][0][0])
except (IndexError, ValueError):
pass
try:
new_metabolite.formula = str(m["metFormulas"][0][0][i][0][0])
except (IndexError, ValueError):
pass
try:
new_metabolite.charge = float(m["metCharge"][0, 0][i][0])
int_charge = int(new_metabolite.charge)
if new_metabolite.charge == int_charge:
new_metabolite.charge = int_charge
except (IndexError, ValueError):
pass
model.add_metabolites([new_metabolite])
new_reactions = []
coefficients = {}
for i, name in enumerate(m["rxns"][0, 0]):
new_reaction = Reaction()
new_reaction.id = str(name[0][0])
new_reaction.lower_bound = float(m["lb"][0, 0][i][0])
new_reaction.upper_bound = float(m["ub"][0, 0][i][0])
if isinf(new_reaction.lower_bound) and new_reaction.lower_bound < 0:
new_reaction.lower_bound = -inf
if isinf(new_reaction.upper_bound) and new_reaction.upper_bound > 0:
new_reaction.upper_bound = inf
if c_vec is not None:
coefficients[new_reaction] = float(c_vec[i][0])
try:
new_reaction.gene_reaction_rule = str(m['grRules'][0, 0][i][0][0])
except (IndexError, ValueError):
pass
try:
new_reaction.name = str(m["rxnNames"][0, 0][i][0][0])
except (IndexError, ValueError):
pass
try:
new_reaction.subsystem = str(m['subSystems'][0, 0][i][0][0])
except (IndexError, ValueError):
pass
new_reactions.append(new_reaction)
model.add_reactions(new_reactions)
set_objective(model, coefficients)
coo = scipy_sparse.coo_matrix(m["S"][0, 0])
for i, j, v in zip(coo.row, coo.col, coo.data):
model.reactions[j].add_metabolites({model.metabolites[i]: v})
return model
def create_cobra_model_from_sbml_file(sbml_filename, old_sbml=False,
legacy_metabolite=False,
print_time=False, use_hyphens=False):
"""convert an SBML XML file into a cobra.Model object.
Supports SBML Level 2 Versions 1 and 4. The function will detect if the
SBML fbc package is used in the file and run the converter if the fbc
package is used.
Parameters
----------
sbml_filename: string
old_sbml: bool
Set to True if the XML file has metabolite formula appended to
metabolite names. This was a poorly designed artifact that persists in
some models.
legacy_metabolite: bool
If True then assume that the metabolite id has the compartment id
appended after an underscore (e.g. _c for cytosol). This has not been
implemented but will be soon.
print_time: bool
deprecated
use_hyphens: bool
If True, double underscores (__) in an SBML ID will be converted to
hyphens
Returns
-------
Model : The parsed cobra model
"""
if not libsbml:
raise ImportError('create_cobra_model_from_sbml_file '
'requires python-libsbml')
__default_lower_bound = -1000
__default_upper_bound = 1000
__default_objective_coefficient = 0
# Ensure that the file exists
if not isfile(sbml_filename):
raise IOError('Your SBML file is not found: %s' % sbml_filename)
# Expressions to change SBML Ids to Palsson Lab Ids
metabolite_re = re.compile('^M_')
reaction_re = re.compile('^R_')
compartment_re = re.compile('^C_')
if print_time:
warn("print_time is deprecated", DeprecationWarning)
model_doc = libsbml.readSBML(sbml_filename)
if model_doc.getPlugin("fbc") is not None:
from libsbml import ConversionProperties, LIBSBML_OPERATION_SUCCESS
conversion_properties = ConversionProperties()
conversion_properties.addOption(
"convert fbc to cobra", True, "Convert FBC model to Cobra model")
result = model_doc.convert(conversion_properties)
if result != LIBSBML_OPERATION_SUCCESS:
raise Exception("Conversion of SBML+fbc to COBRA failed")
sbml_model = model_doc.getModel()
sbml_model_id = sbml_model.getId()
sbml_species = sbml_model.getListOfSpecies()
sbml_reactions = sbml_model.getListOfReactions()
sbml_compartments = sbml_model.getListOfCompartments()
compartment_dict = dict([(compartment_re.split(x.getId())[-1], x.getName())
for x in sbml_compartments])
if legacy_metabolite:
# Deal with the palsson lab appending the compartment id to the
# metabolite id
new_dict = {}
for the_id, the_name in compartment_dict.items():
if the_name == '':
new_dict[the_id[0].lower()] = the_id
else:
new_dict[the_id] = the_name
compartment_dict = new_dict
legacy_compartment_converter = dict(
[(v, k) for k, v in iteritems(compartment_dict)])
cobra_model = Model(sbml_model_id)
metabolites = []
metabolite_dict = {}
# Convert sbml_metabolites to cobra.Metabolites
for sbml_metabolite in sbml_species:
# Skip sbml boundary species
if sbml_metabolite.getBoundaryCondition():
continue
if (old_sbml or legacy_metabolite) and \
sbml_metabolite.getId().endswith('_b'):
# Deal with incorrect sbml from bigg.ucsd.edu
continue
tmp_metabolite = Metabolite()
metabolite_id = tmp_metabolite.id = sbml_metabolite.getId()
tmp_metabolite.compartment = compartment_re.split(
sbml_metabolite.getCompartment())[-1]
if legacy_metabolite:
if tmp_metabolite.compartment not in compartment_dict:
tmp_metabolite.compartment = legacy_compartment_converter[
tmp_metabolite.compartment]
tmp_metabolite.id = parse_legacy_id(
tmp_metabolite.id, tmp_metabolite.compartment,
use_hyphens=use_hyphens)
if use_hyphens:
tmp_metabolite.id = metabolite_re.split(
tmp_metabolite.id)[-1].replace('__', '-')
else:
# Just in case the SBML ids are ill-formed and use -
tmp_metabolite.id = metabolite_re.split(
tmp_metabolite.id)[-1].replace('-', '__')
tmp_metabolite.name = sbml_metabolite.getName()
tmp_formula = ''
tmp_metabolite.notes = parse_legacy_sbml_notes(
sbml_metabolite.getNotesString())
if sbml_metabolite.isSetCharge():
tmp_metabolite.charge = sbml_metabolite.getCharge()
if "CHARGE" in tmp_metabolite.notes:
note_charge = tmp_metabolite.notes["CHARGE"][0]
try:
note_charge = float(note_charge)
if note_charge == int(note_charge):
note_charge = int(note_charge)
except:
warn("charge of %s is not a number (%s)" %
(tmp_metabolite.id, str(note_charge)))
else:
if ((tmp_metabolite.charge is None) or
(tmp_metabolite.charge == note_charge)):
tmp_metabolite.notes.pop("CHARGE")
# set charge to the one from notes if not assigend before
# the same
tmp_metabolite.charge = note_charge
else: # tmp_metabolite.charge != note_charge
msg = "different charges specified for %s (%d and %d)"
msg = msg % (tmp_metabolite.id,
tmp_metabolite.charge, note_charge)
warn(msg)
# Chances are a 0 note charge was written by mistake. We
# will default to the note_charge in this case.
if tmp_metabolite.charge == 0:
tmp_metabolite.charge = note_charge
for the_key in tmp_metabolite.notes.keys():
if the_key.lower() == 'formula':
tmp_formula = tmp_metabolite.notes.pop(the_key)[0]
break
if tmp_formula == '' and old_sbml:
tmp_formula = tmp_metabolite.name.split('_')[-1]
tmp_metabolite.name = tmp_metabolite.name[:-len(tmp_formula) - 1]
tmp_metabolite.formula = tmp_formula
metabolite_dict.update({metabolite_id: tmp_metabolite})
metabolites.append(tmp_metabolite)
cobra_model.add_metabolites(metabolites)
# Construct the vectors and matrices for holding connectivity and numerical
# info to feed to the cobra toolbox.
# Always assume steady state simulations so b is set to 0
cobra_reaction_list = []
coefficients = {}
for sbml_reaction in sbml_reactions:
if use_hyphens:
# Change the ids to match conventions used by the Palsson lab.
reaction = Reaction(reaction_re.split(
sbml_reaction.getId())[-1].replace('__', '-'))
else:
# Just in case the SBML ids are ill-formed and use -
reaction = Reaction(reaction_re.split(
sbml_reaction.getId())[-1].replace('-', '__'))
cobra_reaction_list.append(reaction)
# reaction.exchange_reaction = 0
reaction.name = sbml_reaction.getName()
cobra_metabolites = {}
# Use the cobra.Metabolite class here
for sbml_metabolite in sbml_reaction.getListOfReactants():
tmp_metabolite_id = sbml_metabolite.getSpecies()
# This deals with boundary metabolites
if tmp_metabolite_id in metabolite_dict:
tmp_metabolite = metabolite_dict[tmp_metabolite_id]
cobra_metabolites[tmp_metabolite] = - \
sbml_metabolite.getStoichiometry()
for sbml_metabolite in sbml_reaction.getListOfProducts():
tmp_metabolite_id = sbml_metabolite.getSpecies()
# This deals with boundary metabolites
if tmp_metabolite_id in metabolite_dict:
tmp_metabolite = metabolite_dict[tmp_metabolite_id]
# Handle the case where the metabolite was specified both
# as a reactant and as a product.
if tmp_metabolite in cobra_metabolites:
warn("%s appears as a reactant and product %s" %
(tmp_metabolite_id, reaction.id))
cobra_metabolites[
tmp_metabolite] += sbml_metabolite.getStoichiometry()
# if the combined stoichiometry is 0, remove the metabolite
if cobra_metabolites[tmp_metabolite] == 0:
cobra_metabolites.pop(tmp_metabolite)
else:
cobra_metabolites[
tmp_metabolite] = sbml_metabolite.getStoichiometry()
# check for nan
for met, v in iteritems(cobra_metabolites):
if isnan(v) or isinf(v):
warn("invalid value %s for metabolite '%s' in reaction '%s'" %
(str(v), met.id, reaction.id))
reaction.add_metabolites(cobra_metabolites)
# Parse the kinetic law info here.
parameter_dict = {}
# If lower and upper bounds are specified in the Kinetic Law then
# they override the sbml reversible attribute. If they are not
# specified then the bounds are determined by getReversible.
if not sbml_reaction.getKineticLaw():
if sbml_reaction.getReversible():
parameter_dict['lower_bound'] = __default_lower_bound
parameter_dict['upper_bound'] = __default_upper_bound
else:
# Assume that irreversible reactions only proceed from left to
# right.
parameter_dict['lower_bound'] = 0
parameter_dict['upper_bound'] = __default_upper_bound
parameter_dict[
'objective_coefficient'] = __default_objective_coefficient
else:
for sbml_parameter in \
sbml_reaction.getKineticLaw().getListOfParameters():
parameter_dict[
sbml_parameter.getId().lower()] = sbml_parameter.getValue()
if 'lower_bound' in parameter_dict:
reaction.lower_bound = parameter_dict['lower_bound']
elif 'lower bound' in parameter_dict:
reaction.lower_bound = parameter_dict['lower bound']
elif sbml_reaction.getReversible():
reaction.lower_bound = __default_lower_bound
else:
reaction.lower_bound = 0
if 'upper_bound' in parameter_dict:
reaction.upper_bound = parameter_dict['upper_bound']
elif 'upper bound' in parameter_dict:
reaction.upper_bound = parameter_dict['upper bound']
else:
reaction.upper_bound = __default_upper_bound
objective_coefficient = parameter_dict.get(
'objective_coefficient', parameter_dict.get(
'objective_coefficient', __default_objective_coefficient))
if objective_coefficient != 0:
coefficients[reaction] = objective_coefficient
# ensure values are not set to nan or inf
if isnan(reaction.lower_bound) or isinf(reaction.lower_bound):
reaction.lower_bound = __default_lower_bound
if isnan(reaction.upper_bound) or isinf(reaction.upper_bound):
reaction.upper_bound = __default_upper_bound
reaction_note_dict = parse_legacy_sbml_notes(
sbml_reaction.getNotesString())
# Parse the reaction notes.
# POTENTIAL BUG: DEALING WITH LEGACY 'SBML' THAT IS NOT IN A
# STANDARD FORMAT
# TODO: READ IN OTHER NOTES AND GIVE THEM A reaction_ prefix.
# TODO: Make sure genes get added as objects
if 'GENE ASSOCIATION' in reaction_note_dict:
rule = reaction_note_dict['GENE ASSOCIATION'][0]
try:
rule.encode('ascii')
except (UnicodeEncodeError, UnicodeDecodeError):
warn("gene_reaction_rule '%s' is not ascii compliant" % rule)
if rule.startswith(""") and rule.endswith("""):
rule = rule[6:-6]
reaction.gene_reaction_rule = rule
if 'GENE LIST' in reaction_note_dict:
reaction.systematic_names = reaction_note_dict['GENE LIST'][0]
elif ('GENES' in reaction_note_dict and
reaction_note_dict['GENES'] != ['']):
reaction.systematic_names = reaction_note_dict['GENES'][0]
elif 'LOCUS' in reaction_note_dict:
gene_id_to_object = dict([(x.id, x) for x in reaction._genes])
for the_row in reaction_note_dict['LOCUS']:
tmp_row_dict = {}
the_row = 'LOCUS:' + the_row.lstrip('_').rstrip('#')
for the_item in the_row.split('#'):
k, v = the_item.split(':')
tmp_row_dict[k] = v
tmp_locus_id = tmp_row_dict['LOCUS']
if 'TRANSCRIPT' in tmp_row_dict:
tmp_locus_id = tmp_locus_id + \
'.' + tmp_row_dict['TRANSCRIPT']
if 'ABBREVIATION' in tmp_row_dict:
gene_id_to_object[tmp_locus_id].name = tmp_row_dict[
'ABBREVIATION']
if 'SUBSYSTEM' in reaction_note_dict:
reaction.subsystem = reaction_note_dict.pop('SUBSYSTEM')[0]
reaction.notes = reaction_note_dict
# Now, add all of the reactions to the model.
cobra_model.id = sbml_model.getId()
# Populate the compartment list - This will be done based on
# cobra.Metabolites in cobra.Reactions in the future.
cobra_model.compartments = compartment_dict
cobra_model.add_reactions(cobra_reaction_list)
set_objective(cobra_model, coefficients)
return cobra_model
def from_mat_struct(mat_struct, model_id=None, inf=inf):
"""create a model from the COBRA toolbox struct
The struct will be a dict read in by scipy.io.loadmat
"""
m = mat_struct
if m.dtype.names is None:
raise ValueError("not a valid mat struct")
if not {"rxns", "mets", "S", "lb", "ub"} <= set(m.dtype.names):
raise ValueError("not a valid mat struct")
if "c" in m.dtype.names:
c_vec = m["c"][0, 0]
else:
c_vec = None
warn("objective vector 'c' not found")
model = Model()
if model_id is not None:
model.id = model_id
elif "description" in m.dtype.names:
description = m["description"][0, 0][0]
if not isinstance(description, string_types) and len(description) > 1:
model.id = description[0]
warn("Several IDs detected, only using the first.")
else:
model.id = description
else:
model.id = "imported_model"
for i, name in enumerate(m["mets"][0, 0]):
new_metabolite = Metabolite()
new_metabolite.id = str(name[0][0])
if all(var in m.dtype.names for var in
['metComps', 'comps', 'compNames']):
comp_index = m["metComps"][0, 0][i][0] - 1
new_metabolite.compartment = m['comps'][0, 0][comp_index][0][0]
if new_metabolite.compartment not in model.compartments:
comp_name = m['compNames'][0, 0][comp_index][0][0]
model.compartments[new_metabolite.compartment] = comp_name
else:
new_metabolite.compartment = _get_id_compartment(new_metabolite.id)
if new_metabolite.compartment not in model.compartments:
model.compartments[
new_metabolite.compartment] = new_metabolite.compartment
try:
new_metabolite.name = str(m["metNames"][0, 0][i][0][0])
except (IndexError, ValueError):
pass
try:
new_metabolite.formula = str(m["metFormulas"][0][0][i][0][0])
except (IndexError, ValueError):
pass
try:
new_metabolite.charge = float(m["metCharge"][0, 0][i][0])
int_charge = int(new_metabolite.charge)
if new_metabolite.charge == int_charge:
new_metabolite.charge = int_charge
except (IndexError, ValueError):
pass
model.add_metabolites([new_metabolite])
new_reactions = []
coefficients = {}
for i, name in enumerate(m["rxns"][0, 0]):
new_reaction = Reaction()
new_reaction.id = str(name[0][0])
new_reaction.lower_bound = float(m["lb"][0, 0][i][0])
new_reaction.upper_bound = float(m["ub"][0, 0][i][0])
if isinf(new_reaction.lower_bound) and new_reaction.lower_bound < 0:
new_reaction.lower_bound = -inf
if isinf(new_reaction.upper_bound) and new_reaction.upper_bound > 0:
new_reaction.upper_bound = inf
if c_vec is not None:
coefficients[new_reaction] = float(c_vec[i][0])
try:
new_reaction.gene_reaction_rule = str(m['grRules'][0, 0][i][0][0])
except (IndexError, ValueError):
pass
try:
new_reaction.name = str(m["rxnNames"][0, 0][i][0][0])
except (IndexError, ValueError):
pass
try:
new_reaction.subsystem = str(m['subSystems'][0, 0][i][0][0])
except (IndexError, ValueError):
pass
new_reactions.append(new_reaction)
model.add_reactions(new_reactions)
set_objective(model, coefficients)
coo = scipy_sparse.coo_matrix(m["S"][0, 0])
for i, j, v in zip(coo.row, coo.col, coo.data):
model.reactions[j].add_metabolites({model.metabolites[i]: v})
return model
