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 multimodel_pipeline(
sbml2parameters,
res_dir,
do_fva=True,
do_fba=True,
do_efm=True,
max_efm_number=1000,
rewrite=True,
treeefm_path=TREEEFM_PATH,
):
vis_dir = os.path.join(res_dir, "visualization")
create_dirs(vis_dir, rewrite)
get_f_path = lambda f: os.path.join("..", os.path.relpath(f, res_dir)) if f else None
mask_shift, tab2html, layer2mask, invisible_layers = 4, {}, {}, []
model_id2id2mask, model_id2vis_r_ids = {}, {}
model_id2sbml, model_id2S = {}, {}
for model_id, (out_r_id, out_rev, in_r_id2rev, in_m_id, out_m_id) in sbml2parameters.items():
name, model_id2S[name], model_id2sbml[name], model_id2vis_r_ids[
name
], description, mask_shift, model_id2id2mask[name], cur_layer2mask, main_layer = analyse_model(
sbml=model_id,
out_r_id=out_r_id,
out_rev=out_rev,
in_r_id2rev=in_r_id2rev,
in_m_id=in_m_id,
out_m_id=out_m_id,
res_dir=res_dir,
do_fva=do_fva,
do_fba=do_fba,
do_efm=do_efm,
mask_shift=mask_shift,
get_f_path=get_f_path,
max_efm_number=max_efm_number,
main_dir=vis_dir,
rewrite=rewrite,
tree_efm_path=treeefm_path,
)
tab2html["Analysis of %s" % name] = description, None
layer2mask.update({"%s: %s" % (name, layer): mask for (layer, mask) in cur_layer2mask.items()})
invisible_layers.extend(["%s: %s" % (name, layer) for layer in cur_layer2mask.keys() if layer != main_layer])
if len(model_id2sbml) > 1:
mm_dir = os.path.join(res_dir, "merged_model")
create_dirs(mm_dir)
print(model_id2S)
sbml, S, model_id2id2id, common_ids, model_id2dfs, mappings = combine_models(model_id2sbml, model_id2S, mm_dir)
tab2html["Model comparison"] = mapping_serializer.serialize(model_id2dfs, *mappings, mm_dir, get_f_path), None
title = "Combined model analysis"
id2mask = defaultdict(lambda: 0)
vis_r_ids = set()
for model_id in model_id2sbml.keys():
id2mask.update(
{
model_id2id2id[model_id][s_id]: id2mask[model_id2id2id[model_id][s_id]] | mask
for (s_id, mask) in model_id2id2mask[model_id].items()
if s_id in model_id2id2id[model_id]
}
)
vis_r_ids |= {
model_id2id2id[model_id][r_id]
for r_id in model_id2vis_r_ids[model_id]
if r_id in model_id2id2id[model_id]
}
id2color, info = get_colors(common_ids, model_id2id2id, model_id2sbml.keys())
else:
model_id, sbml = next(model_id2sbml.items())
S = model_id2S[model_id].get_main_S()
vis_r_ids, id2mask, id2color = model_id2vis_r_ids[model_id], model_id2id2mask[model_id], None
info, title = "", "Model analysis"
# Communities
comm_dir = _prepare_dir(res_dir, "communities", "Analysing communities...")
id2cluster = detect_communities_by_boundary_metabolites(S)
id2intersection = {cl_id: S.get_efm_intersection(cluster) for (cl_id, cluster) in id2cluster.items()}
# id2imp_rns = {cl_id: {} for (cl_id, cluster) in id2cluster.items()}
# id2bounds = {cl_id: S.get_boundary_metabolite_distribution(cluster) for (cl_id, cluster) in id2cluster.items()}
# id2imp_rns = {cl_id: detect_reaction_community(S, cluster, id2intersection[cl_id])
# for (cl_id, cluster) in id2cluster.items()}
if id2cluster:
doc = libsbml.SBMLReader().readSBML(sbml)
model = doc.getModel()
description = "Hello!"
# 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["Pathway communities"] = description, None
for cl_id, r_id2c in id2intersection.items():
mask_shift = update_vis_layers(
r_id2c.keys(), "Pathway community %s" % cl_id, id2mask, layer2mask, mask_shift, vis_r_ids
)
invisible_layers.append("Pathway community %s" % cl_id)
visualize_model(sbml, vis_r_ids, id2mask, id2color, title, info, invisible_layers, layer2mask, res_dir, tab2html)