def get_geneIDsAndRxnIDsAndMetIDs_modelsBioCycAndModelsCOBRA(
self,
pathways):
#initialize supporting objects
cobra01 = models_COBRA_query(self.session,self.engine,self.settings);
cobra01.initialize_supportedTables();
cobra_dependencies = models_BioCyc_dependencies();
#query the pathways
biocyc_pathways = self.getParsed_genesAndPathwaysAndReactions_namesAndDatabase_modelsBioCycPathways(
names_I=pathways,
database_I='ECOLI',
query_I={},
output_O='listDict',
dictColumn_I=None);
genes = list(set([g['gene'] for g in biocyc_pathways if g['gene']!='']));
#join list of genes with alternative identifiers
biocyc_genes = self.getParsed_genesAndAccessionsAndSynonyms_namesAndDatabase_modelsBioCycPolymerSegments(
names_I=genes,
database_I='ECOLI',
query_I={},
output_O='listDict',
dictColumn_I=None);
gene_ids = list(set(genes + [g['synonym'] for g in biocyc_genes if g['synonym']]));
accession_1 = list(set([g['accession_1'] for g in biocyc_genes if g['accession_1']!='']));
#Join accession_1 with COBRA reactions
cobra_rxnIDs = cobra01.get_rows_modelIDAndOrderedLocusNames_dataStage02PhysiologyModelReactions(
model_id_I='150526_iDM2015',
ordered_locus_names_I=accession_1,
query_I={},
output_O='listDict',
dictColumn_I=None)
rxn_ids = list(set([g['rxn_id'].replace('_reverse','') for g in cobra_rxnIDs if g['rxn_id']!='']));
#COBRA metabolites
met_ids = list(set([p for g in cobra_rxnIDs if g['products_ids'] for p in g['products_ids']]+\
[p for g in cobra_rxnIDs if g['reactants_ids'] for p in g['reactants_ids']]));
#deformat met_ids
from SBaaS_models.models_COBRA_dependencies import models_COBRA_dependencies
cobra_dependencies = models_COBRA_dependencies();
met_ids_deformated = list(set([cobra_dependencies.deformat_metid(m).replace('13dpg','23dpg')\
.replace('3pg','Pool_2pg_3pg')\
.replace('glycogen','adpglc')\
.replace('uacgam','udpglcur') for m in met_ids]));
#return values
return gene_ids,rxn_ids,met_ids,met_ids_deformated;
def convertAndMap_BioCycTranscriptionFactor2COBRA(
self,
BioCyc2COBRA_regulation_I,
BioCyc_polymerSegments_I = None,
BioCyc_compounds_I = None,
COBRA_metabolites_I = None,
chebi2inchi_I = None,
):
'''Convert and map BioCyc Transcription factor (ligand-binding) reactions
to COBRA model ids
INPUT:
BioCyc2COBRA_regulation_I = output from convertAndMap_BioCycRegulation2COBRA
BioCyc_polymerSegments_I = (TODO) listDict of models_BioCyc_polymerSegments
BioCyc_compounds_I = listDict of models_BioCyc_compounds
COBRA_metabolites_I = listDict of models_COBRA_metabolites
chebi2inchi_I = listDict of CHEBI_ID to InCHI
OUTPUT:
'''
from SBaaS_models.models_BioCyc_dependencies import models_BioCyc_dependencies
BioCyc_dependencies = models_BioCyc_dependencies();
if not BioCyc2COBRA_regulation_I is None and BioCyc2COBRA_regulation_I:
BioCyc2COBRA_regulators = list(set([r['regulator'] for r in BioCyc2COBRA_regulation_I \
if 'DNA-binding transcriptional dual regulator' in r['regulator']]));
else:
BioCyc2COBRA_regulators=BioCyc2COBRA_regulation_I;
if not chebi2inchi_I is None and chebi2inchi_I:
chebi2inchi_dict_I = {r['CHEBI_ID']:r['InChI'] for r in chebi2inchi_I}
else:
chebi2inchi_dict_I=chebi2inchi_I;
if not BioCyc_compounds_I is None and BioCyc_compounds_I:
#BioCyc_compounds_dict_I = {r['name']:r for r in BioCyc_compounds_I}
BioCyc_compounds_dict_I = {}
for row in BioCyc_compounds_I:
keys = [];
keys.append(row['name'])
keys = list(set([k for k in keys if k!='']))
for k in keys:
if not k in BioCyc_compounds_dict_I.keys():
BioCyc_compounds_dict_I[k]=[];
if not row in BioCyc_compounds_dict_I[k]:
BioCyc_compounds_dict_I[k].append(row);
else:
BioCyc_compounds_dict_I=BioCyc_compounds_I;
if not BioCyc_polymerSegments_I is None and BioCyc_polymerSegments_I:
BioCyc_polymerSegments_dict_I = {}
for r in BioCyc_polymerSegments_I:
products = models_BioCyc_dependencies.convert_bioCycList2List(r['product'])
for p in products:
if not p in BioCyc_polymerSegments_dict_I.keys():
BioCyc_polymerSegments_dict_I[p]=[];
else:
BioCyc_polymerSegments_dict_I[p].append(r);
else:
BioCyc_polymerSegments_dict_I = BioCyc_polymerSegments_I
BioCyc2COBRA_regulators_O = {}
for e in BioCyc2COBRA_regulators:
BioCyc2COBRA_regulators_O[e]=[];
#spot checks:
if e == 'Cra DNA-binding transcriptional dual regulator':
#error mapping fdp_c
print('check');
elif e == 'GalR DNA-binding transcriptional dual regulator':
#gene is being identified as a TU
print('check');
elif e == 'β-D-galactose':
#not a transcription factor
print('check');
tmp = self.get_rows_substratesAndParentClassesAndDatabase_modelsBioCycReactions(
e,
database_I='ECOLI',
query_I={},
output_O='listDict',
dictColumn_I=None
);
for t in tmp:
ligands = {'COBRA_met_id': [],
'BioCyc_name': []};
genes = [];
tus = [];
#parse left and right
left = BioCyc_dependencies.convert_bioCycList2List(t['left'])
right = BioCyc_dependencies.convert_bioCycList2List(t['right'])
#check for tus
if e in left:
tus.append(e);
mode = '("-")';
elif e in right:
tus.append(e);
mode = '("+")';
else:
continue;
#query proteins to look up the gene
#query compounds to look up the ligands
for l in left:
proteins,compounds = [],[];
proteins = self.get_rows_nameAndDatabase_modelsBioCycProteins(
l,database_I = 'ECOLI'
);
compounds = self.get_rows_nameAndDatabase_modelsBioCycCompounds(
l,database_I = 'ECOLI'
);
if proteins:
for p in proteins:
#1. parse genes directly
genes.extend(BioCyc_dependencies.convert_bioCycList2List(p['gene']));
#2. if genes are not specified (i.e., protein complex) query and parse polymerSegments
names = BioCyc_dependencies.convert_bioCycList2List(p['names'])
for n in names:
##TODO: test
#if n in BioCyc_polymerSegments_dict_I.keys():
# for row in BioCyc_polymerSegments_dict_I[n]:
# genes.append(row['name'])
rows = self.get_rows_productAndDatabase_modelsBioCycPolymerSegments(
n,database_I = 'ECOLI');
genes.extend(r['name'] for r in rows);
elif compounds:
#map the ligand names...
original,converted = BioCyc_dependencies.map_BioCyc2COBRA(
compounds,
#[c['name'] for c in compounds],
BioCyc_components_dict_I=BioCyc_compounds_dict_I,
BioCyc2COBRA_func_I=BioCyc_dependencies.map_BioCycCompound2COBRA,
BioCyc2COBRA_params_I={
'COBRA_metabolites_I':COBRA_metabolites_I,
'chebi2inchi_dict_I':chebi2inchi_dict_I,
}
);
ligands['BioCyc_name'].extend([c['name'] for c in original])
#ligands['BioCyc_name'].extend(original)
ligands['COBRA_met_id'].extend(converted)
for r in right:
proteins,compounds = [],[];
proteins = self.get_rows_nameAndDatabase_modelsBioCycProteins(
r,database_I = 'ECOLI'
);
compounds = self.get_rows_nameAndDatabase_modelsBioCycCompounds(
r,database_I = 'ECOLI'
);
if proteins:
for p in proteins:
#1. parse genes directly
genes.extend(BioCyc_dependencies.convert_bioCycList2List(p['gene']));
#2. if genes are not specified (i.e., protein complex) query and parse polymerSegments
names = BioCyc_dependencies.convert_bioCycList2List(p['names'])
for n in names:
##TODO: test
#if n in BioCyc_polymerSegments_dict_I.keys():
# for row in BioCyc_polymerSegments_dict_I[n]:
# genes.append(row['name'])
rows = self.get_rows_productAndDatabase_modelsBioCycPolymerSegments(
n,database_I = 'ECOLI');
genes.extend(r['name'] for r in rows);
elif compounds:
original,converted = BioCyc_dependencies.map_BioCyc2COBRA(
compounds,
#[c['name'] for c in compounds],
BioCyc_components_dict_I=BioCyc_compounds_dict_I,
BioCyc2COBRA_func_I=BioCyc_dependencies.map_BioCycCompound2COBRA,
BioCyc2COBRA_params_I={
'COBRA_metabolites_I':COBRA_metabolites_I,
'chebi2inchi_dict_I':chebi2inchi_dict_I,
}
);
ligands['BioCyc_name'].extend([c['name'] for c in original])
#ligands['BioCyc_name'].extend(original)
ligands['COBRA_met_id'].extend(converted)
genes = list(set([g for g in genes if g!='']))
#check that there is only 1 tu:
assert(len(tus)==1); #only 1 tu
tu = tus[0];
#NOTE: there can be multiple ligands/genes associated with the tu
BioCyc2COBRA_regulators_O[e].append({
'ligands':ligands,
'genes':genes,
'tu':tu,
'regulator':e,
'mode':mode,
});
return BioCyc2COBRA_regulators_O;
def convertAndMap_BioCycRegulation2COBRA(
self,
BioCyc_regulation_I,
BioCyc_reactions_I = None,
BioCyc_enzymaticReactions2PolymerSegments_I = None,
BioCyc_compounds_I = None,
COBRA_reactions_I = None,
COBRA_metabolites_I = None,
chebi2inchi_I = None,
#chebi2database_I = None,
MetaNetX_reactions_I = None,
MetaNetX_metabolites_I = None,):
'''Convert and map BioCyc Regulation
to COBRA model ids
INPUT:
BioCyc_regulation_I = listDict
BioCyc_reactions_I = listDict of models_BioCyc_reactions
BioCyc_enzymaticReactions2PolymerSegments_I = listDict of
join between models_BioCyc_enzymaticReactions and
models_BioCyc_polymerSegments
(getJoin_genes_namesAndDatabase_modelsBioCycEnzymaticReactionsAndPolymerSegments)
BioCyc_compounds_I = listDict of models_BioCyc_compounds
COBRA_reactions_I = listDict of models_COBRA_reactions
COBRA_metabolites_I = listDict of models_COBRA_metabolites
chebi2inchi_I = listDict of CHEBI_ID to InCHI
MetaNetX_reactions_I = listDict of MetaNetX reaction xrefs
MetaNetX_metabolites_I = listDict of MetaNetX chemical xrefs
OUTPUT:
'''
BioCyc_dependencies = models_BioCyc_dependencies();
#reformat input into a dict for fast traversal
if not chebi2inchi_I is None and chebi2inchi_I:
chebi2inchi_dict_I = {r['CHEBI_ID']:r['InChI'] for r in chebi2inchi_I}
else:
chebi2inchi_dict_I=chebi2inchi_I;
#if not chebi2database_I is None and chebi2database_I:
# chebi2database_dict_I = {r['CHEBI_ID']:r['InChI'] for r in chebi2database_I}
#else:
# chebi2database_dict_I=chebi2database_I;
if not BioCyc_compounds_I is None and BioCyc_compounds_I:
#BioCyc_compounds_dict_I = {r['name']:r for r in BioCyc_compounds_I}
BioCyc_compounds_dict_I = {}
for row in BioCyc_compounds_I:
keys = [];
keys.append(row['name'])
keys = list(set([k for k in keys if k!='']))
for k in keys:
if not k in BioCyc_compounds_dict_I.keys():
BioCyc_compounds_dict_I[k]=[];
if not row in BioCyc_compounds_dict_I[k]:
BioCyc_compounds_dict_I[k].append(row);
else:
BioCyc_compounds_dict_I=BioCyc_compounds_I;
if not BioCyc_reactions_I is None and BioCyc_reactions_I:
BioCyc_reactions_dict_I = {}
for row in BioCyc_reactions_I:
keys = [];
keys.append(row['common_name'])
keys.extend(BioCyc_dependencies.convert_bioCycList2List(row['enzymatic_reaction']))
keys = list(set([k for k in keys if k!='']))
for k in keys:
if not k in BioCyc_reactions_dict_I.keys():
BioCyc_reactions_dict_I[k]=[];
if not row in BioCyc_reactions_dict_I[k]:
BioCyc_reactions_dict_I[k].append(row);
else:
BioCyc_reactions_dict_I=BioCyc_reactions_I;
if not BioCyc_enzymaticReactions2PolymerSegments_I is None and BioCyc_enzymaticReactions2PolymerSegments_I:
BioCyc_enzymaticReactions_dict_I = {}
for row in BioCyc_enzymaticReactions2PolymerSegments_I:
try:
if not row['name'] in BioCyc_enzymaticReactions_dict_I.keys():
BioCyc_enzymaticReactions_dict_I[row['name']]={
'name':'',
'enzyme':[],
'gene_ids':[],
'accession_1':[],
}
BioCyc_enzymaticReactions_dict_I[row['name']]['name']=row['name'];
BioCyc_enzymaticReactions_dict_I[row['name']]['enzyme'].append(row['enzyme']);
BioCyc_enzymaticReactions_dict_I[row['name']]['gene_ids'].extend(row['gene_ids']);
BioCyc_enzymaticReactions_dict_I[row['name']]['accession_1'].extend(row['accession_1']);
except Exception as e:
print(e)
else:
BioCyc_enzymaticReactions_dict_I=BioCyc_enzymaticReactions2PolymerSegments_I;
if not MetaNetX_reactions_I is None and MetaNetX_reactions_I:
MetaNetX_reactions_dict_I = {}
for row in MetaNetX_reactions_I:
try:
if not row['MNX_ID'] in MetaNetX_reactions_dict_I.keys():
MetaNetX_reactions_dict_I[row['MNX_ID']]={}
key_value = row['#XREF'].split(':')
MetaNetX_reactions_dict_I[row['MNX_ID']][key_value[0]]=key_value[1];
except Exception as e:
print(e)
#print(row)
else:
MetaNetX_reactions_dict_I=MetaNetX_reactions_I;
if not MetaNetX_metabolites_I is None and MetaNetX_metabolites_I:
MetaNetX_metabolites_dict_I = {}
for row in MetaNetX_metabolites_I:
try:
if not row['MNX_ID'] in MetaNetX_metabolites_dict_I.keys():
MetaNetX_metabolites_dict_I[row['MNX_ID']]={}
key_value = row['#XREF'].split(':')
MetaNetX_metabolites_dict_I[row['MNX_ID']][key_value[0]]=key_value[1];
except Exception as e:
print(e)
#print(row)
else:
MetaNetX_metabolites_dict_I=MetaNetX_metabolites_I;
regulation_O = [];
for i,reg in enumerate(BioCyc_regulation_I):
#if reg['name'] == 'Regulation of galSp by GalR DNA-binding transcriptional dual regulator':
# print('check')
#elif reg['name'] == 'Regulation of ribonucleoside-diphosphate reductase by dATP':
# print('check');
#elif reg['regulated_entity_enzymaticReaction'] == 'formate dehydrogenase':
# print('check');
unique = {
'regulator':reg['regulator'],
'regulated_entity':reg['regulated_entity'],
'mode':reg['mode'],
'mechanism':reg['mechanism'],
'name':reg['name'],
'parent_classes':reg['parent_classes']
}
tmp = {
'regulators_EcoCyc':[],
'regulators_COBRA':[],
'regulated_entities_EcoCyc':[],
'regulated_entities_COBRA':[],
}
#convert the regulators
if reg['regulator_gene']:
original,converted = BioCyc_dependencies.map_BioCyc2COBRA(
reg['regulator_gene'],
BioCyc2COBRA_func_I=None,
BioCyc2COBRA_params_I={}
);
tmp['regulators_EcoCyc']=original;
tmp['regulators_COBRA']=converted;
elif reg['regulator_protein']:
original,converted = BioCyc_dependencies.map_BioCyc2COBRA(
reg['regulator_protein'],
BioCyc2COBRA_func_I=None,
BioCyc2COBRA_params_I={}
);
tmp['regulators_EcoCyc']=original;
tmp['regulators_COBRA']=converted;
elif reg['regulator_RNA']:
original,converted = BioCyc_dependencies.map_BioCyc2COBRA(
reg['regulator_RNA'],
BioCyc2COBRA_func_I=None,
BioCyc2COBRA_params_I={}
);
tmp['regulators_EcoCyc']=original;
tmp['regulators_COBRA']=converted;
elif reg['regulator_compound']:
original,converted = BioCyc_dependencies.map_BioCyc2COBRA(
reg['regulator_compound'],
BioCyc_components_dict_I=BioCyc_compounds_dict_I,
BioCyc2COBRA_func_I=BioCyc_dependencies.map_BioCycCompound2COBRA,
BioCyc2COBRA_params_I={
'COBRA_metabolites_I':COBRA_metabolites_I,
'chebi2inchi_dict_I':chebi2inchi_dict_I,
'MetaNetX_metabolites_dict_I':MetaNetX_metabolites_dict_I,
}
);
tmp['regulators_EcoCyc']=original;
tmp['regulators_COBRA']=converted;
#convert the regulated_entities
if reg['regulated_entity_gene']:
original,converted = BioCyc_dependencies.map_BioCyc2COBRA(
reg['regulated_entity_gene'],
BioCyc2COBRA_func_I=None,
BioCyc2COBRA_params_I={}
);
tmp['regulated_entities_EcoCyc']=original;
tmp['regulated_entities_COBRA']=converted;
elif reg['regulated_entity_enzymaticReaction']:
original,converted = BioCyc_dependencies.map_BioCyc2COBRA(
reg['regulated_entity_enzymaticReaction'],
BioCyc_components_dict_I=BioCyc_reactions_dict_I,
BioCyc2COBRA_func_I=BioCyc_dependencies.map_BioCycReaction2COBRA,
BioCyc2COBRA_params_I={
'COBRA_reactions_I':COBRA_reactions_I,
'MetaNetX_reactions_dict_I':MetaNetX_reactions_dict_I,
'BioCyc_reaction2Genes_dict_I':BioCyc_enzymaticReactions_dict_I,
}
);
tmp['regulated_entities_EcoCyc']=original;
tmp['regulated_entities_COBRA']=converted;
elif reg['regulated_entity_promoter']:
original,converted = BioCyc_dependencies.map_BioCyc2COBRA(
reg['regulated_entity_promoter'],
BioCyc2COBRA_func_I=None,
BioCyc2COBRA_params_I={}
);
tmp['regulated_entities_EcoCyc']=original;
tmp['regulated_entities_COBRA']=converted;
elif reg['regulated_entity_product']:
original,converted = BioCyc_dependencies.map_BioCyc2COBRA(
reg['regulated_entity_product'],
BioCyc2COBRA_func_I=None,
BioCyc2COBRA_params_I={}
);
tmp['regulated_entities_EcoCyc']=original;
tmp['regulated_entities_COBRA']=converted;
elif reg['regulated_entity_protein']:
original,converted = BioCyc_dependencies.map_BioCyc2COBRA(
reg['regulated_entity_protein'],
BioCyc2COBRA_func_I=None,
BioCyc2COBRA_params_I={}
);
tmp['regulated_entities_EcoCyc']=original;
tmp['regulated_entities_COBRA']=converted;
#check that mappings/conversions took place
if not tmp['regulators_EcoCyc'] or not tmp['regulated_entities_EcoCyc'] or \
not tmp['regulators_COBRA'] or not tmp['regulated_entities_COBRA']:
continue;
#flatten
EcoCyc_flattened = BioCyc_dependencies.crossMultiple_2lists(
tmp['regulators_EcoCyc'],
tmp['regulated_entities_EcoCyc'],
'regulators_EcoCyc',
'regulated_entities_EcoCyc',
)
COBRA_flattened = BioCyc_dependencies.crossMultiple_2lists(
tmp['regulators_COBRA'],
tmp['regulated_entities_COBRA'],
'regulators_COBRA',
'regulated_entities_COBRA',
)
for i in range(len(EcoCyc_flattened)):
tmp1 = {};
tmp1.update(EcoCyc_flattened[i])
tmp1.update(COBRA_flattened[i])
tmp1.update(unique)
regulation_O.append(tmp1);
#remove duplicate entries
#(NOTE: only works because each dictionary is constructed identically)
data_O = [];
for row in regulation_O:
if not row in data_O:
data_O.append(row);
return data_O;
#make the COBRA table
from SBaaS_models.models_COBRA_execute import models_COBRA_execute
cobra01 = models_COBRA_execute(session,engine,pg_settings.datadir_settings);
cobra01.initialize_supportedTables()
cobra01.initialize_tables()
#make the BioCyc table
from SBaaS_models.models_BioCyc_execute import models_BioCyc_execute
biocyc01 = models_BioCyc_execute(session,engine,pg_settings.datadir_settings);
biocyc01.initialize_supportedTables()
biocyc01.initialize_tables()
#BioCyc dependencies
from SBaaS_models.models_BioCyc_dependencies import models_BioCyc_dependencies
biocyc01_dep = models_BioCyc_dependencies();
#BioCyc dependencies
from SBaaS_models.models_COBRA_dependencies import models_COBRA_dependencies
cobra01_dep = models_COBRA_dependencies();
sys.path.append(pg_settings.datadir_settings['workspace']+'/sbaas_shared')
from ALEsKOs01_shared.ALEsKOs01_commonRoutines import *
iobase = base_importData();
iobase.read_json(
pg_settings.datadir_settings['workspace_data']+\
'/_output/BioCyc_regulation.json');
regulation_O = iobase.data;
#protein-mediated-translation-regulation not annotated
