def save_reduced_json_me_model(me0, file_name):
"""
Save a stripped-down JSON version of the ME-model. This will exclude all of
ME-Model information except the reaction stoichiometry information and the
reaction bounds. Saving/loading a model in this format will thus occur much
quicker, but limit the ability to edit the model and use most of its
features.
Parameters
----------
me0 : :class:`cobrame.core.model.MEModel`
A full ME-model
file_name : str or file-like object
Filename of the JSON output
"""
me = copy.deepcopy(me0)
for rxn in me.reactions:
for met in rxn.metabolites:
s = rxn._metabolites[met]
if isinstance(s, Basic):
rxn._metabolites[met] = str(s)
if isinstance(rxn.lower_bound, Basic):
rxn.lower_bound = str(rxn.lower_bound)
if isinstance(rxn.upper_bound, Basic):
rxn.upper_bound = str(rxn.upper_bound)
for met in me.metabolites:
if isinstance(met._bound, Basic):
met._bound = str(met._bound)
save_json_model(me, file_name)
def export_model(self,model_id_I,filename_I,filetype_I = 'sbml',ko_list=[],flux_dict={}):
'''export a cobra model
INPUT:
model_id_I = model id
filename_I = filename
filetype_I = if specified, the model will be written to the desired filetype
default: model file in the database
'''
# get the model
cobra_model_sbml = {};
cobra_model_sbml = self.get_row_modelID_dataStage02IsotopomerModels(model_id_I);
if not cobra_model_sbml:
print('model not found');
return;
# load the model
cobra_model = self.writeAndLoad_modelTable(cobra_model_sbml);
# Constrain the model
self.constrain_modelModelVariables(cobra_model=cobra_model,ko_list=ko_list,flux_dict=flux_dict);
# export the model
if filetype_I == 'sbml':
cobra_model = self.writeAndLoad_modelTable(cobra_model_sbml);
#export the model to sbml
write_cobra_model_to_sbml_file(cobra_model,filename_I);
elif filetype_I == 'json':
cobra_model = self.writeAndLoad_modelTable(cobra_model_sbml);
#export the model to json
save_json_model(cobra_model,filename_I);
else:
print('file_type not supported')
def save_json_me(me0, file_name, pretty=False):
"""
Save ME model as json
model : :class:`~cobrame.core.MEModel.MEmodel` object
file_name : str or file-like object
"""
me = copy.deepcopy(me0)
for rxn in me.reactions:
for met in rxn.metabolites:
s = rxn._metabolites[met]
if isinstance(s, Basic):
rxn._metabolites[met] = str(s)
if isinstance(rxn.lower_bound, Basic):
rxn.lower_bound = str(rxn.lower_bound)
if isinstance(rxn.upper_bound, Basic):
rxn.upper_bound = str(rxn.upper_bound)
for met in me.metabolites:
if isinstance(met._bound, Basic):
met._bound = str(met._bound)
save_json_model(me, file_name)
def make_modelFromRxnsAndMetsTables(self,model_id_I=None,model_id_O=None,date_O=None,ko_list=[],flux_dict={},description=None):
'''make/update the model using the modelReactions and modelMetabolites table'''
if model_id_I and model_id_O: #make a new model based off of a modification of an existing model in the database
# get the model reactions and metabolites from the database
reactions = [];
metabolites = [];
reactions = self.get_rows_modelID_dataStage02IsotopomerModelReactions(model_id_I);
metabolites = self.get_rows_modelID_dataStage02IsotopomerModelMetabolites(model_id_I);
# creat the model
cobra_model = self.create_modelFromReactionsAndMetabolitesTables(reactions,metabolites)
# Constrain the model
self.constrain_modelModelVariables(cobra_model=cobra_model,ko_list=ko_list,flux_dict=flux_dict);
# Change description, if any:
if description:
cobra_model.description = description;
# test the model
if self.test_model(cobra_model):
# write the model to a temporary file
save_json_model(cobra_model,'cobra_model_tmp.json')
# add the model information to the database
dataStage02IsotopomerModelRxns_data = [];
dataStage02IsotopomerModelMets_data = [];
dataStage02IsotopomerModels_data,\
dataStage02IsotopomerModelRxns_data,\
dataStage02IsotopomerModelMets_data = self._parse_model_json(model_id_O, date_O, 'cobra_model_tmp.json')
self.add_data_stage02_isotopomer_models(dataStage02IsotopomerModels_data);
elif model_id_I and not model_id_O: #update an existing model in the database
# get the model reactions and metabolites from the database
reactions = [];
metabolites = [];
reactions = self.get_rows_modelID_dataStage02IsotopomerModelReactions(model_id_I);
metabolites = self.get_rows_modelID_dataStage02IsotopomerModelMetabolites(model_id_I);
# creat the model
cobra_model = self.create_modelFromReactionsAndMetabolitesTables(reactions,metabolites)
# Constrain the model
self.constrain_modelModelVariables(cobra_model=cobra_model,ko_list=ko_list,flux_dict=flux_dict);
# Change description, if any:
if description:
cobra_model.description = description;
# test the model
if self.test_model(cobra_model):
# write the model to a temporary file
save_json_model(cobra_model,'cobra_model_tmp.json')
# upload the model to the database
# add the model information to the database
dataStage02IsotopomerModelRxns_data = [];
dataStage02IsotopomerModelMets_data = [];
dataStage02IsotopomerModels_data,\
dataStage02IsotopomerModelRxns_data,\
dataStage02IsotopomerModelMets_data = self._parse_model_json(model_id_I, date_O, 'cobra_model_tmp.json')
self.update_data_stage02_isotopomer_models(dataStage02IsotopomerModels_data);
else:
print('need to specify either an existing model_id!')
return
for other_id, mnx_id in reac_xref[['XREF', 'MNX_ID']].values:
cleaned_key = _apply_sanitize_rules(
_apply_sanitize_rules(other_id.split(':')[1], REVERSE_ID_SANITIZE_RULES_SIMPHENY),
ID_SANITIZE_RULES_TAB_COMPLETION)
all2mnx[cleaned_key] = mnx_id
metanetx['all2mnx'] = all2mnx
# with open('../cameo/data/metanetx.pickle', 'wb') as f:
# pickle.dump(metanetx, f)
# generate universal reaction models
db_combinations = [('bigg',), ('rhea',), ('bigg', 'rhea'), ('bigg', 'rhea', 'kegg'),
('bigg', 'rhea', 'kegg', 'brenda')]
for db_combination in db_combinations:
universal_model = construct_universal_model(db_combination)
# The following is a hack; uncomment the following
from cobra.io.json import _REQUIRED_REACTION_ATTRIBUTES
_REQUIRED_REACTION_ATTRIBUTES.add('annotation')
# d_model = _to_dict(universal_model)
with open('../cameo/models/universal_models/{model_name}.json'.format(model_name=universal_model.id), 'w') as f:
save_json_model(universal_model, f)
# json.dump(d_model, f)
# save_json_model(universal_model, '../cameo/models/universal_models/{model_name}.json'.format(model_name=universal_model.id))
chem_prop_filtered = chem_prop[
[any([source.startswith(db) for db in ('bigg', 'rhea', 'kegg', 'brenda', 'chebi')]) for source in
chem_prop.source]]
chem_prop_filtered = chem_prop_filtered.dropna(subset=['name'])
# with gzip.open('../cameo/data/metanetx_chem_prop.pklz', 'wb') as f:
# pickle.dump(chem_prop_filtered, f)
def make_modelAndMappingFromRxnsAndMetsTables(self,model_id_I=None,model_id_O=None,mapping_id_I=None,mapping_id_O=None,date_O=None,ko_list=[],flux_dict={},description=None):
'''make/update the model AND model mappings using the modelReactions and modelMetabolites table'''
if model_id_I and model_id_O and mapping_id_I and mapping_id_O: #make a new model based off of a modification of an existing model in the database
# get the model reactions and metabolites from the database
reactions = [];
metabolites = [];
reactions = self.get_rows_modelID_dataStage02IsotopomerModelReactions(model_id_I);
metabolites = self.get_rows_modelID_dataStage02IsotopomerModelMetabolites(model_id_I);
reactions_mappings = [];
metabolites_mappings = [];
reactions_mappings = self.get_rows_mappingID_dataStage02IsotopomerAtomMappingReactions(mapping_id_I);
metabolites_mappings = self.get_rows_mappingID_dataStage02IsotopomerAtomMappingMetabolites(mapping_id_I);
# rename the reactions and metabolite model_ids
for rxn_cnt,rxn in enumerate(reactions):
reactions[rxn_cnt]['model_id'] = model_id_O;
for met_cnt,met in enumerate(metabolites):
metabolites[met_cnt]['model_id'] = model_id_O;
# rename the reactions and metabolite mapping_ids
for rxn_cnt,rxn in enumerate(reactions_mappings):
reactions_mappings[rxn_cnt]['mapping_id'] = mapping_id_O;
for met_cnt,met in enumerate(metabolites_mappings):
metabolites_mappings[met_cnt]['mapping_id'] = mapping_id_O;
# create the model
cobra_model = self.create_modelFromReactionsAndMetabolitesTables(reactions,metabolites)
# Apply KOs, if any:
for ko in ko_list:
cobra_model.reactions.get_by_id(ko).lower_bound = 0.0;
cobra_model.reactions.get_by_id(ko).upper_bound = 0.0;
# Apply flux constraints, if any:
for rxn,flux in flux_dict.items():
cobra_model.reactions.get_by_id(rxn).lower_bound = flux['lb'];
cobra_model.reactions.get_by_id(rxn).upper_bound = flux['ub'];
# Change description, if any:
if description:
cobra_model.description = description;
# test the model
if self.test_model(cobra_model):
# write the model to a temporary file
save_json_model(cobra_model,'cobra_model_tmp.json')
# add the model information to the database
dataStage02IsotopomerModelRxns_data = [];
dataStage02IsotopomerModelMets_data = [];
dataStage02IsotopomerModels_data,\
dataStage02IsotopomerModelRxns_data,\
dataStage02IsotopomerModelMets_data = self._parse_model_json(model_id_O, date_O, 'cobra_model_tmp.json')
self.add_data_stage02_isotopomer_models(dataStage02IsotopomerModels_data);
# add the metabolite and reaction information to the database
self.add_data_stage02_isotopomer_modelReactions(reactions);
self.add_data_stage02_isotopomer_modelMetabolites(metabolites);
# add the metabolites and reactions mappings to the database
self.add_data_dataStage02IsotopomerAtomMappingReactions(reactions_mappings);
self.add_data_dataStage02IsotopomerAtomMappingMetabolites(metabolites_mappings);
elif model_id_I and not model_id_O and mapping_id_I and not mapping_id_O: #update an existing model in the database
# get the model reactions and metabolites from the database
reactions = [];
metabolites = [];
reactions = self.get_rows_modelID_dataStage02IsotopomerModelReactions(model_id_I);
metabolites = self.get_rows_modelID_dataStage02IsotopomerModelMetabolites(model_id_I);
# creat the model
cobra_model = self.create_modelFromReactionsAndMetabolitesTables(reactions,metabolites)
# Apply KOs, if any:
for ko in ko_list:
cobra_model.reactions.get_by_id(ko).lower_bound = 0.0;
cobra_model.reactions.get_by_id(ko).upper_bound = 0.0;
# Apply flux constraints, if any:
for rxn,flux in flux_dict.items():
cobra_model.reactions.get_by_id(rxn).lower_bound = flux['lb'];
cobra_model.reactions.get_by_id(rxn).upper_bound = flux['ub'];
# Change description, if any:
if description:
cobra_model.description = description;
# test the model
if self.test_model(cobra_model):
# write the model to a temporary file
save_json_model(cobra_model,'cobra_model_tmp.json')
# upload the model to the database
# add the model information to the database
dataStage02IsotopomerModelRxns_data = [];
dataStage02IsotopomerModelMets_data = [];
dataStage02IsotopomerModels_data,\
dataStage02IsotopomerModelRxns_data,\
dataStage02IsotopomerModelMets_data = self._parse_model_json(model_id_I, date_I, 'cobra_model_tmp.json')
self.update_data_stage02_isotopomer_models(dataStage02IsotopomerModels_data);
else:
print('need to specify an existing model_id/mapping_id!')
return
db_combinations = [('bigg', ), ('rhea', ), ('bigg', 'rhea'),
('bigg', 'rhea', 'kegg'),
('bigg', 'rhea', 'kegg', 'brenda')]
largest_universal_model = None
for db_combination in db_combinations:
universal_model = construct_universal_model(db_combination, reac_xref,
reac_prop, chem_prop)
if largest_universal_model is None:
largest_universal_model = universal_model
else:
if len(set(largest_universal_model.metabolites)) < len(
set(universal_model.metabolites)):
largest_universal_model = universal_model
# The following is a hack; uncomment the following
from cobra.io.json import _REQUIRED_REACTION_ATTRIBUTES
_REQUIRED_REACTION_ATTRIBUTES.add('annotation')
with open(
'../cameo/models/universal_models/{model_name}.json'.format(
model_name=universal_model.id), 'w') as f:
save_json_model(universal_model, f)
metabolite_ids_in_largest_model = [
m.id for m in largest_universal_model.metabolites
]
chem_prop_filtered = chem_prop.dropna(subset=['name'])
chem_prop_filtered = chem_prop_filtered[chem_prop_filtered.index.isin(
metabolite_ids_in_largest_model)]
with gzip.open('../cameo/data/metanetx_chem_prop.json.gz', 'wt') as f:
chem_prop_filtered.to_json(f, force_ascii=True)
def make_modelFromRxnsAndMetsTables(self,
model_id_I=None,model_id_O=None,date_O=None,description=None,
ko_list=[],flux_dict={},rxn_include_list=[],
convert2irreversible_I=False,revert2reversible_I=False,
convertPathway2individualRxns_I=False,
model_id_template_I='iJO1366',pathway_model_id_I=None
):
'''make/update the model using the modelReactions and modelMetabolites table
INPUT:
model_id_I = existing model_id (if specified, an existing model in the database will be retrieved and modified)
model_file_name_I = new model from file (if specified, a new model form file will be retrieved and modified)
model_id_O = new model_id
date_O = date the model was made
description = description for the model
INPUT (constraints in order):
ko_list = list of reactions to constrain to zero flux
flux_dict = dictionary of fluxes to constrain the model
INPUT (model manipulation in order):
rxn_include_list = list of reactions to include in the new model
convertPathway2individualRxns_I = boolean, if True, lumped rxns will be converted to individual rxns
template_model_id_I = model_id to use as a template when adding in new reactions
pathway_model_id_I = model_id for the pathway
convert2irreversible_I = boolean, if True, the model will be converted to irreversible from reversible
revert2reversible_I = boolean, if True, the model will be revert to reversible from irreversible
'''
# get the model reactions and metabolites from the database
reactions = [];
metabolites = [];
reactions = self.get_rows_modelID_dataStage02PhysiologyModelReactions(model_id_I);
metabolites = self.get_rows_modelID_dataStage02PhysiologyModelMetabolites(model_id_I);
# break apart lumped reactions
if convertPathway2individualRxns_I:
metabolites_O = [];
reactions,metabolites_O = self.convert_lumpedRxns2IndividualRxns(
model_id_template_I=model_id_template_I,
pathway_model_id_I=pathway_model_id_I,
reactions_I=reactions);
# add in any new metabolites that may have been found
met_ids = [x['met_id'] for x in metabolites];
for met in metabolites_O:
if not met['met_id'] in met_ids:
metabolites.append(met);
# create the model
cobra_model = self.create_modelFromReactionsAndMetabolitesTables(reactions,metabolites)
# Constrain the model
self.constrain_modelModelVariables(cobra_model,
ko_list=ko_list,flux_dict=flux_dict);
# Use only a subset of reactions, if specified
if rxn_include_list:
remove_reactions=[];
for rxn in cobra_model.reactions:
if not rxn.id in rxn_include_list:
remove_reactions.append(rxn);
cobra_model.remove_reactions(remove_reactions,delete=True,remove_orphans=True);
if convert2irreversible_I: convert_to_irreversible(cobra_model);
if revert2reversible_I:
self.repair_irreversibleModel(cobra_model);
self.revert2reversible(cobra_model);
# Change description, if any:
if description:
cobra_model.description = description;
# test the model
if self.test_model(cobra_model):
# write the model to a temporary file
save_json_model(cobra_model,'cobra_model_tmp.json')
# add the model information to the database
dataStage02PhysiologyModelRxns_data = [];
dataStage02PhysiologyModelMets_data = [];
dataStage02PhysiologyModels_data,\
dataStage02PhysiologyModelRxns_data,\
dataStage02PhysiologyModelMets_data = self._parse_model_json(model_id_O, date_O, 'cobra_model_tmp.json')
if model_id_I and model_id_O: #make a new model based off of a modification of an existing model in the database
self.add_dataStage02PhysiologyModelMetabolites(dataStage02PhysiologyModelMets_data);
self.add_dataStage02PhysiologyModelReactions(dataStage02PhysiologyModelRxns_data);
self.add_dataStage02PhysiologyModels(dataStage02PhysiologyModels_data);
elif model_id_I and not model_id_O: #update an existing model in the database
self.update_data_stage02_isotopomer_models(dataStage02PhysiologyModels_data);
else:
print('need to specify either an existing model_id!')
return
