def serialize(model, path, get_f_path, **kwargs):
model_name, S = kwargs['model_name'], kwargs['S']
pathways_txt, (efm_num, pw_num) = serialize_efms(model, path, S)
limit, efm_data = serialize_n_shortest_efms(model, path, S, n=3)
fm_block = describe('fm_block.html', element_num=limit, characteristics='shortest',
element_name='EFM',
fms=[(efm_id, efm_len, get_f_path(efm_txt)) for (efm_id, efm_len, efm_txt) in efm_data],
all=limit == efm_num)
# pw_matrix_html = pathway_matrix_serializer.serialize(S, model, in_m_id, out_m_id, model_name, main_dir)
return describe('efms.html', efm_num=efm_num, fm_num=pw_num, description_filepath=get_f_path(pathways_txt),
selected_efm_block=fm_block, pw_matrix_html=None)
def serialize(model, path, get_f_path, **kwargs):
rgroups_txt, rg_num = serialize_coupled_reaction_groups(model, path, **kwargs)
limit, rg_data = serialize_n_longest_coupled_reaction_groups(model, path, n=5, **kwargs)
fm_block = describe(
"fm_block.html",
element_num=limit,
characteristics="longest",
element_name="reaction group",
fms=[(rg_id, rg_len, get_f_path(rg_txt)) for (rg_id, rg_len, rg_txt) in rg_data],
all=limit == rg_num,
)
return describe(
"cliques.html", clique_num=rg_num, description_filepath=get_f_path(rgroups_txt), selected_clique_block=fm_block
)
def get_colors(common_ids, model_id2id2id, model_ids):
colors = get_n_colors(len(model_ids) + 1, 0.5, 0.8)
mixed_color = None
description = ""
if common_ids:
mixed_color = colors[0]
r, g, b = mixed_color
description = describe("color.html", r=r, g=g, b=b, name="common reactions/metabolites")
model_id2color = dict(zip(model_ids, colors[1:]))
id2color = {}
for model_id, id2id in model_id2id2id.items():
color = model_id2color[model_id]
r, g, b = color
id2color.update({t_id: color if t_id not in common_ids else mixed_color for t_id in id2id.values()})
description += describe("color.html", r=r, g=g, b=b, name=model_id)
return id2color, description
def serialize(cobra_model, opt_val, r_id2bounds, objective_sense, out_r_id, path, get_f_path, sbml):
ess_rn_num, var_rn_num, fva_file = 0, 0, None
if opt_val:
fva_file = os.path.join(path, 'fva.txt')
ess_rn_num, var_rn_num = serialize_fva(cobra_model, r_id2bounds, fva_file, objective_sense, out_r_id)
return describe('fva.html', optimal_value=opt_val, ess_r_num=ess_rn_num, var_r_num=var_rn_num,
description_filepath=get_f_path(fva_file), sbml_filepath=get_f_path(sbml))
def to_tr(efm_id, header_class='header', color_class='pw', super_class='', hidden=False):
if efm_id in efm_id2tr:
return
if efm_id in S.gr_id2efm_ids:
for sub_efm_id in S.gr_id2efm_ids[efm_id]:
to_tr(sub_efm_id, header_class='sub_%s' % header_class, super_class='sub_%s' % efm_id,
color_class='sub_%s' % color_class, hidden=True)
efm_id2tr[efm_id] = describe('foldable_rows.html', S=S, efm_id=efm_id, header_class=header_class,
super_class=super_class, efm_id2tr=efm_id2tr, color_class=color_class,
p_id2tooltip=p_id2tooltip, hidden=hidden)
def serialize(model_id2dfs, model_id2c_id_groups, model_id2m_id_groups, model_id2r_id_groups, path, get_f_path):
comp_csv, m_csv, r_csv = serialize_common_elements_to_csv(model_id2dfs, model_id2c_id_groups,
model_id2m_id_groups, model_id2r_id_groups,
os.path.join(path, 'Model_comparison_')) \
if model_id2c_id_groups else (None, None, None)
logging.info('Serialized the mappings.')
return describe('model_comparison.html',
c_num=len(model_id2c_id_groups), m_num=len(model_id2m_id_groups), r_num=len(model_id2r_id_groups),
c_csv=get_f_path(comp_csv), m_csv=get_f_path(m_csv), r_csv=get_f_path(r_csv))
def serialize(model, S, id2cluster, path, get_f_path, m_id2ch_id, chebi):
logging.info('Serializing communities...')
communities_txt = serialize_communities(model, S, id2cluster, path, m_id2ch_id, chebi=chebi)
# limit, community_data = \
# serialize_n_longest_communities(len(id2cluster), id2cluster, id2intersection, id2imp_rns, model, path)
# fm_block = describe('fm_block.html', element_num=limit, characteristics='longest',
# element_name='pathway community',
# fms=[(cl_id, cl_len, get_f_path(cl_txt)) for (cl_id, cl_len, cl_txt) in community_data],
# all=limit == len(id2cluster))
return describe('communities.html', community_num=len(id2cluster), description_filepath=get_f_path(communities_txt),
selected_community_block=None)
def serialize(sbml, model, m_name, r_id2rev, path, get_f_path):
logging.info("Serializing model info...")
info_prefix = os.path.join(path, 'model_info_')
c_csv, m_csv, r_csv = serialize_model_info(model, info_prefix)
r_string = lambda r, rev: '<b>%s</b>%s: %s' % (r.getId(), ' (reversed)' if rev else '',
get_sbml_r_formula(model, r, show_metabolite_ids=False))
return describe('input_data.html', model_name=m_name, sbml_filepath=get_f_path(sbml),
c_csv=get_f_path(c_csv), m_csv=get_f_path(m_csv), r_csv=get_f_path(r_csv),
in_rn_len=len(r_id2rev),
in_rns=[r_string(model.getReaction(r_id), rev)
for (r_id, rev) in r_id2rev.items()] if r_id2rev else [])
def serialize(S, model, in_m_id, out_m_id, model_name, main_dir):
def format_r(r_id):
r2st, p2st = S.st_matrix.get_inputs_outputs(r_id)
formatter = lambda m_id, st: '%s %s' % ("" if st == 1 else ("%g" % st),
format_m_name(model.getSpecies(m_id), model, show_id=False))
rs, ps = sorted(formatter(it) for it in r2st.items()), sorted(formatter(it) for it in p2st.items())
r = model.getReaction(r_id)
reversible = r.getReversible() if r else True
return " + ".join(rs) + (" ↔ " if reversible else " → ") + " + ".join(ps)
def format_p(p_id):
fm_yield = S.get_yield(p_id, in_m_id, out_m_id)
if fm_yield is not None:
return 'Yield: %g' % round_value(fm_yield)
return ''
r_id2tooltip = {r_id: format_r(r_id) for r_id in S.r_id2i.keys()}
p_id2tooltip = {p_id: format_p(p_id) for p_id in S.efm_id2i.keys()}
efm_id2tr = {}
def to_tr(efm_id, header_class='header', color_class='pw', super_class='', hidden=False):
if efm_id in efm_id2tr:
return
if efm_id in S.gr_id2efm_ids:
for sub_efm_id in S.gr_id2efm_ids[efm_id]:
to_tr(sub_efm_id, header_class='sub_%s' % header_class, super_class='sub_%s' % efm_id,
color_class='sub_%s' % color_class, hidden=True)
efm_id2tr[efm_id] = describe('foldable_rows.html', S=S, efm_id=efm_id, header_class=header_class,
super_class=super_class, efm_id2tr=efm_id2tr, color_class=color_class,
p_id2tooltip=p_id2tooltip, hidden=hidden)
for efm_id in S.efm_ids:
to_tr(efm_id)
content = describe('V.html', S=S, efm_id2tr=efm_id2tr, r_id2tooltip=r_id2tooltip, model_name=model_name)
html_file_name = '%s_pathways.html' % model_name
with io.open(os.path.join(main_dir, html_file_name), 'w+', encoding='utf-8') as f:
f.write(content)
return html_file_name
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 analyse_model(
sbml,
out_r_id,
out_rev,
res_dir,
in_m_id,
out_m_id,
in_r_id2rev=None,
threshold=ZERO_THRESHOLD,
do_fva=True,
do_fba=True,
do_efm=True,
max_efm_number=1000,
mask_shift=4,
get_f_path=None,
tree_efm_path=TREEEFM_PATH,
main_dir=None,
rewrite=True,
):
model_name = get_model_name(sbml)
logging.info("Analysing %s..." % model_name)
# create directories to store results
logging.info("Preparing directories...")
res_dir = os.path.join(res_dir, "".join(ch for ch in model_name.replace(" ", "_") if ch.isalnum() or "_" == ch))
create_dirs(res_dir, False)
if not get_f_path:
get_f_path = lambda f: os.path.join("..", os.path.relpath(f, res_dir))
doc = libsbml.SBMLReader().readSBML(sbml)
model = doc.getModel()
if in_r_id2rev:
constraint_exchange_reactions(model, forsed_r_id2rev=in_r_id2rev)
libsbml.SBMLWriter().writeSBMLToFile(doc, sbml)
# copy our model in the result directory
if os.path.normpath(res_dir) != os.path.normpath(os.path.dirname(sbml)):
shutil.copy(sbml, res_dir)
sbml = os.path.join(res_dir, os.path.basename(sbml))
r_id2rev = dict(in_r_id2rev)
r_id2rev[out_r_id] = out_rev
description = model_serializer.serialize(sbml, model, model_name, r_id2rev, res_dir, get_f_path)
r_id2mask, layer2mask, vis_r_ids, main_layer = defaultdict(lambda: 0), {}, set(), None
cobra_model, opt_val, objective_sense = None, None, MINIMIZE if out_rev else MAXIMIZE
if do_fva:
cur_dir = _prepare_dir(res_dir, "fva", "Performing FVA...")
cobra_model = create_cobra_model_from_sbml_file(sbml) if not cobra_model else cobra_model
r_id2bounds, opt_val = analyse_by_fva(
cobra_model=cobra_model, bm_r_id=out_r_id, objective_sense=objective_sense, threshold=threshold
)
if opt_val:
mask_shift = update_vis_layers(
(r_id for (r_id, (l, u)) in r_id2bounds.items() if l * u > 0),
"FVA essential",
r_id2mask,
layer2mask,
mask_shift,
vis_r_ids,
)
main_layer = "FVA essential"
fva_sbml = os.path.join(cur_dir, "Model_FVA.xml")
sbml = create_fva_model(sbml, r_id2bounds, fva_sbml)
description += fva_serializer.serialize(
cobra_model, opt_val, r_id2bounds, objective_sense, out_r_id, cur_dir, get_f_path, sbml
)
if do_fba:
cur_dir = _prepare_dir(res_dir, "fba", "Performing FBA...")
cobra_model = create_cobra_model_from_sbml_file(sbml) if not cobra_model else cobra_model
r_id2val, opt_val = analyse_by_fba(
cobra_model, bm_r_id=out_r_id, objective_sense=objective_sense, threshold=threshold
)
if opt_val:
mask_shift = update_vis_layers(r_id2val.keys(), "FBA", r_id2mask, layer2mask, mask_shift, vis_r_ids)
main_layer = "FBA"
description += fba_serializer.serialize(
cobra_model, opt_val, r_id2val, objective_sense, out_r_id, cur_dir, get_f_path
)
S = None
if do_efm:
cur_dir = _prepare_dir(res_dir, "efma", "Performing EFMA...", rewrite=rewrite)
doc = libsbml.SBMLReader().readSBML(sbml)
model = doc.getModel()
name2pw = get_name2pw()
pts = parse_simple(get_pts())
root_ids = {t.get_id() for t in pts.get_roots()}
pw2rs = get_pathways(model, pts, name2pw, root_ids)
S, efm_id2pws = analyse_model_efm(
model,
cur_dir,
r_id2rev,
tree_efm_path=tree_efm_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=cur_dir,
get_f_path=get_f_path,
in_m_id=in_m_id,
out_m_id=out_m_id,
out_r_id=out_r_id,
S=S,
model_name=model_name,
main_dir=main_dir,
)
if S.gr_id2r_id2c:
clique_merged_sbml = os.path.join(cur_dir, "Model_folded.xml")
r_id2new_r_id = create_folded_model(S, model)
libsbml.SBMLWriter().writeSBMLToFile(doc, clique_merged_sbml)
sbml = clique_merged_sbml
vis_r_ids |= {cl_id for (r_id, cl_id) in r_id2new_r_id.items() if r_id in vis_r_ids}
for r_id, new_r_id in r_id2new_r_id.items():
if r_id in r_id2mask:
r_id2mask[new_r_id] |= r_id2mask[r_id]
if not opt_val and (not S or not S.efm_id2i):
description += describe("nothing_found.html")
return model_name, S, sbml, vis_r_ids, description, mask_shift, r_id2mask, layer2mask, main_layer
