def ubiquitize_model(in_sbml,
chebi,
groups_sbml,
ub_s_ids=None,
ub_chebi_ids=None):
"""
Infers and marks ubiquitous species in the model.
:param in_sbml: str, path to the input SBML file
:param chebi: mod_sbml.onto.obo_ontology.Ontology ChEBI ontology
:param groups_sbml: str, path to the output SBML file (with groups extension)
:param ub_s_ids: optional, ids of ubiquitous species (will be inferred if set to None)
:param ub_chebi_ids: optional, ids of ubiquitous ChEBI terms (will be inferred if set to None)
:return: tuple (s_id2chebi_id, ub_s_ids): dict {species_id: ChEBI_term_id}, collection of ubiquitous species_ids.
"""
input_doc = libsbml.SBMLReader().readSBML(in_sbml)
input_model = input_doc.getModel()
annotate_metabolites(input_model, chebi)
logging.info("mapping species to ChEBI")
s_id2chebi_id = get_species_id2chebi_id(input_model)
_, ub_s_ids = get_ub_elements(input_model, chebi, s_id2chebi_id,
ub_chebi_ids, ub_s_ids)
save_as_comp_generalized_sbml(input_model, None, groups_sbml, {}, {},
ub_s_ids, chebi)
return s_id2chebi_id, ub_s_ids
def generalize_model(in_sbml, chebi, groups_sbml, out_sbml, ub_s_ids=None, ub_chebi_ids=None, ignore_biomass=True):
"""
Generalizes a model.
:param in_sbml: str, path to the input SBML file
:param chebi: mod_sbml.onto.obo_ontology.Ontology ChEBI ontology
:param groups_sbml: str, path to the output SBML file (with groups extension)
:param out_sbml: str, path to the output SBML file (generalized)
:param ub_s_ids: optional, ids of ubiquitous species (will be inferred if set to None)
:param ub_chebi_ids: optional, ids of ubiquitous ChEBI terms (will be inferred if set to None)
:param ignore_biomass: boolean, whether to ignore the biomass reaction (and its stoichiometry preserving constraint)
:return: tuple (r_id2g_eq, s_id2gr_id, s_id2chebi_id, ub_s_ids):
dict {reaction_id: reaction_group_id}, dict {species_id: species_group_id}, dict {species_id: ChEBI_term_id},
collection of ubiquitous species_ids.
"""
# input_model
input_doc = libsbml.SBMLReader().readSBML(in_sbml)
input_model = input_doc.getModel()
r_ids_to_ignore = get_biomass_r_ids(input_model) if ignore_biomass else None
remove_is_a_reactions(input_model)
annotate_metabolites(input_model, chebi)
# TODO: fix comp separation
# separate_boundary_metabolites(input_model)
remove_unused_elements(input_model)
logging.info("mapping species to ChEBI")
s_id2chebi_id = get_species_id2chebi_id(input_model)
ub_chebi_ids, ub_s_ids = get_ub_elements(input_model, chebi, s_id2chebi_id, ub_chebi_ids, ub_s_ids)
terms = (t for t in (chebi.get_term(t_id) for t_id in s_id2chebi_id.values()) if t)
old_onto_len = len(chebi)
filter_ontology(chebi, terms, relationships=EQUIVALENT_RELATIONSHIPS, min_deepness=3)
logging.info('Filtered the ontology from %d terms to %d' % (old_onto_len, len(chebi)))
threshold = min(max(3, int(0.1 * input_model.getNumReactions())), UBIQUITOUS_THRESHOLD)
s_id2clu, ub_s_ids = generalize_species(input_model, s_id2chebi_id, ub_s_ids, chebi, ub_chebi_ids, threshold,
r_ids_to_ignore=r_ids_to_ignore)
logging.info("generalized species")
r_id2clu = generalize_reactions(input_model, s_id2clu, s_id2chebi_id, ub_chebi_ids,
r_ids_to_ignore=r_ids_to_ignore)
logging.info("generalized reactions")
clu2s_ids = {(c_id, term): s_ids for ((c_id, (term, )), s_ids) in invert_map(s_id2clu).items()}
r_id2g_eq, s_id2gr_id = save_as_comp_generalized_sbml(input_model, out_sbml, groups_sbml, r_id2clu, clu2s_ids,
ub_s_ids, chebi)
return r_id2g_eq, s_id2gr_id, s_id2chebi_id, ub_s_ids
def ubiquitize_model(in_sbml, chebi, groups_sbml, ub_s_ids=None, ub_chebi_ids=None):
"""
Infers and marks ubiquitous species in the model.
:param in_sbml: str, path to the input SBML file
:param chebi: mod_sbml.onto.obo_ontology.Ontology ChEBI ontology
:param groups_sbml: str, path to the output SBML file (with groups extension)
:param ub_s_ids: optional, ids of ubiquitous species (will be inferred if set to None)
:param ub_chebi_ids: optional, ids of ubiquitous ChEBI terms (will be inferred if set to None)
:return: tuple (s_id2chebi_id, ub_s_ids): dict {species_id: ChEBI_term_id}, collection of ubiquitous species_ids.
"""
input_doc = libsbml.SBMLReader().readSBML(in_sbml)
input_model = input_doc.getModel()
annotate_metabolites(input_model, chebi)
logging.info("mapping species to ChEBI")
s_id2chebi_id = get_species_id2chebi_id(input_model)
_, ub_s_ids = get_ub_elements(input_model, chebi, s_id2chebi_id, ub_chebi_ids, ub_s_ids)
save_as_comp_generalized_sbml(input_model, None, groups_sbml, {}, {}, ub_s_ids, chebi)
return s_id2chebi_id, ub_s_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)
def generalize_model(in_sbml,
chebi,
groups_sbml,
out_sbml,
ub_s_ids=None,
ub_chebi_ids=None,
ignore_biomass=True):
"""
Generalizes a model.
:param in_sbml: str, path to the input SBML file
:param chebi: mod_sbml.onto.obo_ontology.Ontology ChEBI ontology
:param groups_sbml: str, path to the output SBML file (with groups extension)
:param out_sbml: str, path to the output SBML file (generalized)
:param ub_s_ids: optional, ids of ubiquitous species (will be inferred if set to None)
:param ub_chebi_ids: optional, ids of ubiquitous ChEBI terms (will be inferred if set to None)
:param ignore_biomass: boolean, whether to ignore the biomass reaction (and its stoichiometry preserving constraint)
:return: tuple (r_id2g_eq, s_id2gr_id, s_id2chebi_id, ub_s_ids):
dict {reaction_id: reaction_group_id}, dict {species_id: species_group_id}, dict {species_id: ChEBI_term_id},
collection of ubiquitous species_ids.
"""
# input_model
input_doc = libsbml.SBMLReader().readSBML(in_sbml)
input_model = input_doc.getModel()
r_ids_to_ignore = get_biomass_r_ids(
input_model) if ignore_biomass else None
remove_is_a_reactions(input_model)
annotate_metabolites(input_model, chebi)
# TODO: fix comp separation
# separate_boundary_metabolites(input_model)
remove_unused_elements(input_model)
logging.info("mapping species to ChEBI")
s_id2chebi_id = get_species_id2chebi_id(input_model)
ub_chebi_ids, ub_s_ids = get_ub_elements(input_model, chebi, s_id2chebi_id,
ub_chebi_ids, ub_s_ids)
terms = (t for t in (chebi.get_term(t_id)
for t_id in s_id2chebi_id.values()) if t)
old_onto_len = len(chebi)
filter_ontology(chebi,
terms,
relationships=EQUIVALENT_RELATIONSHIPS,
min_deepness=3)
logging.info('Filtered the ontology from %d terms to %d' %
(old_onto_len, len(chebi)))
threshold = min(max(3, int(0.1 * input_model.getNumReactions())),
UBIQUITOUS_THRESHOLD)
s_id2clu, ub_s_ids = generalize_species(input_model,
s_id2chebi_id,
ub_s_ids,
chebi,
ub_chebi_ids,
threshold,
r_ids_to_ignore=r_ids_to_ignore)
logging.info("generalized species")
r_id2clu = generalize_reactions(input_model,
s_id2clu,
s_id2chebi_id,
ub_chebi_ids,
r_ids_to_ignore=r_ids_to_ignore)
logging.info("generalized reactions")
clu2s_ids = {(c_id, term): s_ids
for ((c_id, (term, )), s_ids) in invert_map(s_id2clu).items()}
r_id2g_eq, s_id2gr_id = save_as_comp_generalized_sbml(
input_model, out_sbml, groups_sbml, r_id2clu, clu2s_ids, ub_s_ids,
chebi)
return r_id2g_eq, s_id2gr_id, s_id2chebi_id, ub_s_ids
