def classify_m_ids(m_ids, m_id2chebi_id,
chebi_classes=(CHEBI_AMINO_ACID, CHEBI_LIPID, CHEBI_SALT, CHEBI_HEME, CHEBI_VITAMIN), chebi=None):
if not chebi:
chebi = parse(CHEBI)
terms = set()
chebi_id2m_ids = defaultdict(set)
for m_id in m_ids:
if m_id in m_id2chebi_id:
term = chebi.get_term(m_id2chebi_id[m_id])
if term:
terms.add(term)
chebi_id2m_ids[m_id2chebi_id[m_id]].add(m_id)
t2chebi_ids = {t: {it.get_id() for it in chebi.get_sub_tree(t, relationships=EQUIVALENT_RELATIONSHIPS) & terms}
for t in (chebi.get_term(t_id) for t_id in chebi_classes)}
return {t.get_name(): reduce(lambda s1, s2: s1 | s2, (chebi_id2m_ids[ch_id] for ch_id in ch_ids), set())
for (t, ch_ids) in t2chebi_ids.items() if ch_ids}
def multimodel_pipeline(sbml2parameters, res_dir, treeefm_path, max_efm_number=1000, rewrite=True, org=None):
create_dirs(res_dir, rewrite)
get_f_path = lambda f: os.path.join('..', os.path.relpath(f, res_dir)) if f else None
tab2html = {}
model_id2sbml, model_id2S, model_id2efm_id2pws = {}, {}, {}
name2pw = get_name2pw()
pts = parse_simple(get_pts())
root_ids = {t.get_id() for t in pts.get_roots()}
chebi = parse(CHEBI)
ub_ch_ids = get_ubiquitous_chebi_ids(add_common=True, add_cofactors=True, chebi=chebi)
efm_id2pws = {}
model_id2cofactors = {}
modeld_id2m_id2chebi_id = {}
for sbml, (r_id2rev, r_id2rev_banned) in sbml2parameters.items():
doc = libsbml.SBMLReader().readSBML(sbml)
model = doc.getModel()
model_name = get_model_name(model=model)
short_model_name = model_name
if len(model_name) > 12:
short_model_name = model_name[:10].strip('-_ ')
if len(short_model_name) == 10:
short_model_name += '...'
safe_m_name = ''.join(ch for ch in short_model_name.replace(' ', '_') if ch.isalnum() or '_' == ch)
logging.info('Analysing %s...' % model_name)
# create directories to store results
logging.info("Preparing directories...")
m_dir = os.path.join(res_dir, safe_m_name)
create_dirs(m_dir, rewrite)
# exchange_rs = get_exchange_reactions(model)
# csv = '%s/%s.exchanges.csv' % (m_dir, safe_m_name)
# df2csv(reactions2df(model, exchange_rs), csv)
cofactors = select_metabolite_ids_by_term_ids(model, ub_ch_ids)
if r_id2rev:
constraint_exchange_reactions(model, forsed_r_id2rev=r_id2rev, prohibited_r_id2rev=r_id2rev_banned,
cofactors=cofactors if not r_id2rev_banned else None)
logging.info("Annotating the model...")
annotate(model, org=org, reactions=False, pathways=False, chebi=chebi)
m_id2ch_id = get_species_id2chebi_id(model)
# copy our model in the result directory
sbml = os.path.join(m_dir, '%s.constrained.xml' % safe_m_name)
libsbml.SBMLWriter().writeSBMLToFile(doc, sbml)
description = model_serializer.serialize(sbml, model, model_name, r_id2rev, m_dir, get_f_path)
pw2rs = get_pathways(model, pts, name2pw, root_ids)
logging.info("Performing EFMA...")
efma_dir = os.path.join(m_dir, 'efma')
create_dirs(efma_dir, rewrite)
S, efm_id2pws = analyse_model_efm(model, efma_dir, r_id2rev, tree_efm_path=treeefm_path,
max_efm_number=max_efm_number, rewrite=rewrite, pw2rs=pw2rs)
for serializer in (efm_serializer.serialize, coupled_reaction_group_serializer.serialize):
description += \
serializer(model=model, path=efma_dir, get_f_path=get_f_path, S=S, model_name=model_name)
if S.gr_id2r_id2c:
sbml = os.path.join(efma_dir, '%s.folded.xml' % safe_m_name)
create_folded_model(S, model)
libsbml.SBMLWriter().writeSBMLToFile(doc, sbml)
if not S or not S.efm_id2i:
description += describe('nothing_found.html')
model_id2sbml[safe_m_name] = sbml
model_id2S[safe_m_name] = S
model_id2efm_id2pws[safe_m_name] = efm_id2pws
model_id2cofactors[safe_m_name] = cofactors
modeld_id2m_id2chebi_id[safe_m_name] = m_id2ch_id
tab2html['Analysis of %s' % short_model_name] = description, None
cofactors = set()
m_id2ch_id = {}
if len(model_id2sbml) > 1:
mm_dir = os.path.join(res_dir, 'merged_model')
create_dirs(mm_dir)
sbml, S, model_id2id2id, common_ids, model_id2dfs, mappings = combine_models(model_id2sbml, model_id2S, mm_dir)
for model_id in model_id2sbml.keys():
efm_id2pws.update({model_id2id2id[model_id][efm_id]: pws
for (efm_id, pws) in model_id2efm_id2pws[model_id].items()
if efm_id in model_id2id2id[model_id]})
cofactors |= {model_id2id2id[model_id][m_id] for m_id in model_id2cofactors[model_id]
if m_id in model_id2id2id[model_id]}
m_id2ch_id.update({model_id2id2id[model_id][m_id]: ch_id
for (m_id, ch_id) in modeld_id2m_id2chebi_id[model_id].items()
if m_id in model_id2id2id[model_id]})
tab2html['Model comparison'] = mapping_serializer.serialize(model_id2dfs, *mappings, mm_dir, get_f_path), None
title = 'Combined model analysis'
else:
model_id, sbml = next(model_id2sbml.items())
efm_id2pws = model_id2efm_id2pws[model_id]
cofactors = model_id2cofactors[model_id]
m_id2ch_id = modeld_id2m_id2chebi_id[model_id]
S = model_id2S[model_id].get_main_S()
info, title, id2color = '', 'Model analysis', None
# Communities
logging.info("Analysing communities...")
comm_dir = os.path.join(res_dir, 'communities')
create_dirs(comm_dir, rewrite)
# id2cluster = detect_communities_by_inputs_of_type(S, 'AMINO ACID', m_id2ch_id, chebi)
id2cluster = detect_communities_by_boundary_metabolites(S, cofactors=cofactors, threshold=50)
if id2cluster:
doc = libsbml.SBMLReader().readSBML(sbml)
model = doc.getModel()
description = \
community_serializer.serialize(model, S, id2cluster, comm_dir, get_f_path, m_id2ch_id, chebi)
if len(model_id2sbml) > 1:
tab2html['Model comparison'] = tab2html['Model comparison'][0] + description, None
else:
tab2html['EFM communities'] = description, None
serialize(res_dir, tab2html, title)