def addMetabolomicsReaction(model, metabolites, react_name, coefficient_str=1):
print(react_name, ":", metabolites)
# Build a reaction
coefficient_str = str(coefficient_str)
reaction_str = coefficient_str + (" + " + coefficient_str).join(
metabolites) + " <==> " + react_name + "_c"
reaction = Reaction(react_name)
reaction.name = react_name
reaction.subsystem = 'Metabolomics integration'
# Add the reaction to the model
model.add_reactions([reaction])
reaction.build_reaction_from_string(reaction_str)
#print(reaction,":::::",reaction_str)
return reaction
def parse_xml_into_model(xml, number=float):
xml_model = xml.find(ns("sbml:model"))
if get_attrib(xml_model, "fbc:strict") != "true":
warn('loading SBML model without fbc:strict="true"')
model_id = get_attrib(xml_model, "id")
model = Model(model_id)
model.name = xml_model.get("name")
model.compartments = {c.get("id"): c.get("name") for c in
xml_model.findall(COMPARTMENT_XPATH)}
# add metabolites
for species in xml_model.findall(SPECIES_XPATH % 'false'):
met = get_attrib(species, "id", require=True)
met = Metabolite(clip(met, "M_"))
met.name = species.get("name")
annotate_cobra_from_sbml(met, species)
met.compartment = species.get("compartment")
met.charge = get_attrib(species, "fbc:charge", int)
met.formula = get_attrib(species, "fbc:chemicalFormula")
model.add_metabolites([met])
# Detect boundary metabolites - In case they have been mistakenly
# added. They should not actually appear in a model
boundary_metabolites = {clip(i.get("id"), "M_")
for i in xml_model.findall(SPECIES_XPATH % 'true')}
# add genes
for sbml_gene in xml_model.iterfind(GENES_XPATH):
gene_id = get_attrib(sbml_gene, "fbc:id").replace(SBML_DOT, ".")
gene = Gene(clip(gene_id, "G_"))
gene.name = get_attrib(sbml_gene, "fbc:name")
if gene.name is None:
gene.name = get_attrib(sbml_gene, "fbc:label")
annotate_cobra_from_sbml(gene, sbml_gene)
model.genes.append(gene)
def process_gpr(sub_xml):
"""recursively convert gpr xml to a gpr string"""
if sub_xml.tag == OR_TAG:
return "( " + ' or '.join(process_gpr(i) for i in sub_xml) + " )"
elif sub_xml.tag == AND_TAG:
return "( " + ' and '.join(process_gpr(i) for i in sub_xml) + " )"
elif sub_xml.tag == GENEREF_TAG:
gene_id = get_attrib(sub_xml, "fbc:geneProduct", require=True)
return clip(gene_id, "G_")
else:
raise Exception("unsupported tag " + sub_xml.tag)
bounds = {bound.get("id"): get_attrib(bound, "value", type=number)
for bound in xml_model.iterfind(BOUND_XPATH)}
# add reactions
reactions = []
for sbml_reaction in xml_model.iterfind(
ns("sbml:listOfReactions/sbml:reaction")):
reaction = get_attrib(sbml_reaction, "id", require=True)
reaction = Reaction(clip(reaction, "R_"))
reaction.name = sbml_reaction.get("name")
annotate_cobra_from_sbml(reaction, sbml_reaction)
lb_id = get_attrib(sbml_reaction, "fbc:lowerFluxBound", require=True)
ub_id = get_attrib(sbml_reaction, "fbc:upperFluxBound", require=True)
try:
reaction.upper_bound = bounds[ub_id]
reaction.lower_bound = bounds[lb_id]
except KeyError as e:
raise CobraSBMLError("No constant bound with id '%s'" % str(e))
reactions.append(reaction)
stoichiometry = defaultdict(lambda: 0)
for species_reference in sbml_reaction.findall(
ns("sbml:listOfReactants/sbml:speciesReference")):
met_name = clip(species_reference.get("species"), "M_")
stoichiometry[met_name] -= \
number(species_reference.get("stoichiometry"))
for species_reference in sbml_reaction.findall(
ns("sbml:listOfProducts/sbml:speciesReference")):
met_name = clip(species_reference.get("species"), "M_")
stoichiometry[met_name] += \
get_attrib(species_reference, "stoichiometry",
type=number, require=True)
# needs to have keys of metabolite objects, not ids
object_stoichiometry = {}
for met_id in stoichiometry:
if met_id in boundary_metabolites:
warn("Boundary metabolite '%s' used in reaction '%s'" %
(met_id, reaction.id))
continue
try:
metabolite = model.metabolites.get_by_id(met_id)
except KeyError:
warn("ignoring unknown metabolite '%s' in reaction %s" %
(met_id, reaction.id))
continue
object_stoichiometry[metabolite] = stoichiometry[met_id]
reaction.add_metabolites(object_stoichiometry)
# set gene reaction rule
gpr_xml = sbml_reaction.find(GPR_TAG)
if gpr_xml is not None and len(gpr_xml) != 1:
warn("ignoring invalid geneAssociation for " + repr(reaction))
gpr_xml = None
gpr = process_gpr(gpr_xml[0]) if gpr_xml is not None else ''
# remove outside parenthesis, if any
if gpr.startswith("(") and gpr.endswith(")"):
gpr = gpr[1:-1].strip()
gpr = gpr.replace(SBML_DOT, ".")
reaction.gene_reaction_rule = gpr
try:
model.add_reactions(reactions)
except ValueError as e:
warn(str(e))
# objective coefficients are handled after all reactions are added
obj_list = xml_model.find(ns("fbc:listOfObjectives"))
if obj_list is None:
warn("listOfObjectives element not found")
return model
target_objective_id = get_attrib(obj_list, "fbc:activeObjective")
target_objective = obj_list.find(
ns("fbc:objective[@fbc:id='{}']".format(target_objective_id)))
obj_direction_long = get_attrib(target_objective, "fbc:type")
obj_direction = LONG_SHORT_DIRECTION[obj_direction_long]
obj_query = OBJECTIVES_XPATH % target_objective_id
coefficients = {}
for sbml_objective in obj_list.findall(obj_query):
rxn_id = clip(get_attrib(sbml_objective, "fbc:reaction"), "R_")
try:
objective_reaction = model.reactions.get_by_id(rxn_id)
except KeyError:
raise CobraSBMLError("Objective reaction '%s' not found" % rxn_id)
try:
coefficients[objective_reaction] = get_attrib(
sbml_objective, "fbc:coefficient", type=number)
except ValueError as e:
warn(str(e))
set_objective(model, coefficients)
model.solver.objective.direction = obj_direction
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 test_iterative_gapfill_from_binary_phenotypes():
# load the universal model and a test model
universal = load_universal_modelseed()
model = load_modelseed_model('Staphylococcus aureus')
# Load the biolog composition to be used for gapfilling
biolog_base_composition = pd.read_csv(
'./medusa/test/data/biolog_base_composition.csv', sep=',')
biolog_base_dict = dict(zip(biolog_base_composition['ID'],\
[1000 for i in range(0,len(biolog_base_composition['ID']))]))
biolog_thresholded = pd.read_csv(
'./medusa/test/data/plata_thresholded.csv', sep='\t', index_col=0)
# extract the biolog conditions for Staphylococcus aureus
test_mod_pheno = biolog_thresholded.loc['Staphylococcus aureus']
test_mod_pheno = list(test_mod_pheno[test_mod_pheno == True].index)
# check for biolog base components in the model
add_mets = []
add_exchanges = []
for met in list(biolog_base_dict.keys()):
try:
model.metabolites.get_by_id(met)
except:
print('no ' + met)
add_met = universal.metabolites.get_by_id(met).copy()
add_mets.append(add_met)
model.add_metabolites(add_mets)
for met in list(biolog_base_dict.keys()):
# Search for exchange reactions
try:
model.reactions.get_by_id('EX_' + met)
except:
add_met = model.metabolites.get_by_id(met)
ex_rxn = Reaction('EX_' + met)
ex_rxn.name = "Exchange reaction for " + met
ex_rxn.lower_bound = -1000
ex_rxn.upper_bound = 1000
ex_rxn.add_metabolites({add_met: -1})
add_exchanges.append(ex_rxn)
model.add_reactions(add_exchanges)
# Find metabolites from the biolog data that are missing in the test model
# and add them from the universal
missing_mets = []
missing_exchanges = []
media_dicts = {}
for met_id in test_mod_pheno:
try:
model.metabolites.get_by_id(met_id)
except:
print(met_id +
" was not in model, adding met and exchange reaction")
met = universal.metabolites.get_by_id(met_id).copy()
missing_mets.append(met)
ex_rxn = Reaction('EX_' + met_id)
ex_rxn.name = "Exchange reaction for " + met_id
ex_rxn.lower_bound = -1000
ex_rxn.upper_bound = 1000
ex_rxn.add_metabolites({met: -1})
missing_exchanges.append(ex_rxn)
media_dicts[met_id] = biolog_base_dict.copy()
media_dicts[met_id] = {
'EX_' + k: v
for k, v in media_dicts[met_id].items()
}
media_dicts[met_id]['EX_' + met_id] = 1000
model.add_metabolites(missing_mets)
model.add_reactions(missing_exchanges)
# identify transporters for each biolog component in the universal model
# and pick one that will enable transport in the gapfilling problem.
transporters_in_universal = [
rxn for rxn in universal.reactions if len(rxn.compartments) > 1
]
for met in media_dicts.keys():
metabolite = model.metabolites.get_by_id(met)
base_met_id = met.split('_')[0]
rxns_with_metabolite = metabolite.reactions
transport = False
for rxn in rxns_with_metabolite:
metabolites = [met_in_rxn.id for met_in_rxn in rxn.metabolites]
if (base_met_id + '_e' in metabolites
and base_met_id + '_c' in metabolites):
transport = True
pick_transporter = {}
if not transport:
print("missing transporter for " + metabolite.name)
for rxn in transporters_in_universal:
metabolites = [met_in_rxn.id for met_in_rxn in rxn.metabolites]
if (base_met_id + '_e' in metabolites
and base_met_id + '_c' in metabolites):
pick_transporter[met] = rxn.id
# Add the transporters to the model
transporters_to_add = list(pick_transporter.values())
transporter_list = []
for rxn in transporters_to_add:
transporter_list.append(universal.reactions.get_by_id(rxn).copy())
model.add_reactions(transporter_list)
# remove the added transporters from the universal model
universal.remove_reactions(
[universal.reactions.get_by_id(rxn) for rxn in transporters_to_add])
# select a subset of the biolog conditions to perform gapfilling with
sources = list(media_dicts.keys())
sub_dict = {
sources[0]: media_dicts[sources[0]],
sources[1]: media_dicts[sources[1]],
sources[2]: media_dicts[sources[2]],
sources[3]: media_dicts[sources[3]],
sources[4]: media_dicts[sources[4]]
}
num_cycles = 5
lower_bound = 0.05
flux_cutoff = 1E-10
ensemble = expand.iterative_gapfill_from_binary_phenotypes(model,universal,sub_dict,num_cycles,\
lower_bound=lower_bound,\
inclusion_threshold=1E-10,\
exchange_reactions=False,\
demand_reactions=False,\
exchange_prefix='EX')
# the number of models in the ensemble should equal the number of cycles
# unless a duplicate solution was found; for this test case, this seems
# to happen ~25% of the time, so we'll loosen the restriction.
assert len(ensemble.members) > num_cycles / 2
# each member of the ensemble should be able to produce biomass in each
# biolog condition
ex_rxns = [rxn for rxn in ensemble.base_model.reactions \
if rxn.id.startswith('EX_')]
for source in sub_dict.keys():
# close all exchange reactions
for rxn in ex_rxns:
rxn.lower_bound = 0
ensemble.base_model.medium = sub_dict[source]
for member in ensemble.members:
ensemble.set_state(member)
# member should produce the minimum amount of required biomass
# flux or more
assert ensemble.base_model.slim_optimize() > lower_bound * 0.99
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
