def create_folded_model(S, model): r_id2new_r_id = {} initial_r_ids = {r_id for r_id in S.r_id2i.keys() if r_id not in S.gr_id2r_id2c} r_ids_to_remove = [r.getId() for r in model.getListOfReactions() if r.getId() not in S.r_ids] def map2new_id(r_id, new_id): if r_id not in S.gr_id2r_id2c: r_id2new_r_id[r_id] = new_id else: for sub_r_id in S.gr_id2r_id2c[r_id].keys(): map2new_id(sub_r_id, new_id) for mr_id in S.r_ids - initial_r_ids: cl_id2c = S.gr_id2r_id2c[mr_id] name = 'lumped reaction %s' % mr_id id_ = mr_id r_id2st, p_id2st = S.st_matrix.get_inputs_outputs(mr_id) new_r_id = create_reaction(model, r_id2st, p_id2st, reversible=True, id_=id_, name=name).getId() for r_id in cl_id2c: map2new_id(r_id, new_r_id) for r_id in r_ids_to_remove: model.removeReaction(r_id) for r in model.getListOfReactions(): for i in range(0, r.getNumModifiers()): r.removeModifier(0) remove_unused_species(model) return r_id2new_r_id
def simple_merge_models(S, model_id2c_id2group, model_id2dfs, out_sbml): doc = libsbml.SBMLDocument(2, 4) model = doc.createModel() model.setId('merged_model') model_id2id2id = defaultdict(dict) common_ids = set() c_group2id = {} new_m_id2i, new_r_id2i, new_efm_id2i, new_boundary_m_ids = {}, {}, {}, [] for model_id, [_, _, df] in model_id2dfs.items(): for index, row in df.iterrows(): c_id, name = row['Id'], row['Name'] if model_id in model_id2c_id2group and c_id in model_id2c_id2group[model_id]: group = model_id2c_id2group[model_id][c_id] if group in c_group2id: new_id = c_group2id[group] else: new_id = create_compartment(model, name=name, id_='merged_%s_%s' % (model_id, c_id)).getId() c_group2id[group] = new_id common_ids.add(new_id) else: new_id = create_compartment(model, name=name, id_='%s_%s' % (model_id, c_id)).getId() model_id2id2id[model_id][c_id] = new_id id2id = {} m_id_group_ids = set(S.m_id2gr_id.values()) for (model_id, m_id), i in ((it, i) for (it, i) in S.m_id2i.items() if it not in m_id_group_ids): c_id = model_id2dfs[model_id][0].at[m_id, 'Compartment'] c_id = model_id2id2id[model_id][c_id] name = model_id2dfs[model_id][0].at[m_id, 'Name'] is_boundary = (model_id, m_id) in S.boundary_m_ids new_id = create_species(model, compartment_id=c_id, name=name, bound=is_boundary, id_='%s_%s' % (model_id, m_id)).getId() model_id2id2id[model_id][m_id] = new_id id2id[(model_id, m_id)] = new_id new_m_id2i[new_id] = i if is_boundary: new_boundary_m_ids.append(new_id) for it in m_id_group_ids: model_id, m_ids = next(iter(it)) m_id = next(iter(m_ids)) is_boundary = it in S.boundary_m_ids new_id = \ create_species(model, compartment_id=model_id2id2id[model_id][model_id2dfs[model_id][0].at[m_id, 'Compartment']], name=model_id2dfs[model_id][0].at[m_id, 'Name'], bound=is_boundary, id_='merged_%s_%s' % (model_id, m_id)).getId() for model_id, m_ids in it: model_id2id2id[model_id].update({m_id: new_id for m_id in m_ids}) id2id[it] = new_id new_m_id2i[new_id] = S.m_id2i[it] if is_boundary: new_boundary_m_ids.append(new_id) common_ids.add(new_id) for ((model_id, r_id), i) in ((it, i) for (it, i) in S.r_id2i.items() if it not in S.gr_id2r_id2c.keys()): r_id2st, p_id2st = S.st_matrix.get_inputs_outputs((model_id, r_id)) new_id = create_reaction(model, {id2id[m_id]: st for (m_id, st) in r_id2st.items()}, {id2id[m_id]: st for (m_id, st) in p_id2st.items()}, model_id2dfs[model_id][1].at[r_id, 'Name'], reversible=True, id_='%s_%s' % (model_id, r_id)).getId() model_id2id2id[model_id][r_id] = new_id new_r_id2i[new_id] = i for gr, it2c in S.gr_id2r_id2c.items(): model_id, r_id = next(iter(it2c.keys())) r_id2st, p_id2st = S.st_matrix.get_inputs_outputs(gr) new_id = \ create_reaction(model, {id2id[m_id]: st for (m_id, st) in r_id2st.items()}, {id2id[m_id]: st for (m_id, st) in p_id2st.items()}, model_id2dfs[model_id][1].at[r_id, 'Name'], reversible=True, id_='merged_%s_%s' % (model_id, r_id)).getId() for model_id, r_id in it2c.keys(): model_id2id2id[model_id][r_id] = new_id new_r_id2i[new_id] = S.r_id2i[gr] common_ids.add(new_id) for ((model_id, efm_id), i) in ((it, i) for (it, i) in S.efm_id2i.items() if it not in S.gr_id2efm_ids.keys()): new_id = '%s_%s' % (model_id, efm_id) new_efm_id2i[new_id] = i model_id2id2id[model_id][efm_id] = new_id for gr, efm_ids in S.gr_id2efm_ids.items(): model_id, efm_id = next(efm_ids) new_id = 'merged_%s_%s' % (model_id, efm_id) new_efm_id2i[new_id] = S.r_id2i[gr] for model_id, efm_id in efm_ids: model_id2id2id[model_id][efm_id] = new_id libsbml.writeSBMLToFile(doc, out_sbml) return model_id2id2id, common_ids, System(m_id2i=new_m_id2i, r_id2i=new_r_id2i, efm_id2i=new_efm_id2i, N=S.N, V=S.V, boundary_m_ids=new_boundary_m_ids)
def save_as_comp_generalized_sbml(input_model, out_sbml, groups_sbml, r_id2clu, clu2s_ids, ub_sps, onto): logging.info("serializing generalization") s_id_increment, r_id_increment = 0, 0 if groups_sbml: doc = convert_to_lev3_v1(input_model) groups_model = doc.getModel() groups_plugin = groups_model.getPlugin("groups") if groups_plugin: logging.info(" saving ubiquitous species annotations") s_group = groups_plugin.createGroup() s_group.setId("g_ubiquitous_sps") s_group.setKind(libsbml.GROUP_KIND_COLLECTION) s_group.setSBOTerm(SBO_CHEMICAL_MACROMOLECULE) s_group.setName("ubiquitous species") for s_id in ub_sps: member = s_group.createMember() member.setIdRef(s_id) add_annotation(s_group, libsbml.BQB_IS_DESCRIBED_BY, GROUP_TYPE_UBIQUITOUS) if out_sbml: # generalized model generalized_doc = libsbml.SBMLDocument(input_model.getSBMLNamespaces()) generalized_model = generalized_doc.createModel() copy_elements(input_model, generalized_model) r_id2g_eq, s_id2gr_id = {}, {} if not clu2s_ids: logging.info(" nothing to serialize") else: clu2r_ids = invert_map(r_id2clu) logging.info(" creating species groups") for ((c_id, t), s_ids) in clu2s_ids.items(): comp = input_model.getCompartment(c_id) if len(s_ids) > 1: t = onto.get_term(t) t_name, t_id = (t.get_name(), t.get_id()) if t \ else (' or '.join(input_model.getSpecies(s_id).getName() for s_id in s_ids), None) if not t_id: t = t_name if out_sbml: new_species = create_species(model=generalized_model, compartment_id=comp.getId(), type_id=None, name="{0} ({1}) [{2}]".format(t_name, len(s_ids), comp.getName())) add_annotation(new_species, libsbml.BQB_IS, t_id, CHEBI_PREFIX) new_s_id = new_species.getId() else: s_id_increment += 1 new_s_id = generate_unique_id(input_model, "s_g_", s_id_increment) for s_id in s_ids: s_id2gr_id[s_id] = new_s_id, t if groups_sbml and groups_plugin: # save as a group s_group = groups_plugin.createGroup() s_group.setId(new_s_id) s_group.setKind(libsbml.GROUP_KIND_CLASSIFICATION) s_group.setSBOTerm(SBO_CHEMICAL_MACROMOLECULE) g_name = "{0} [{1}]".format(t_name, comp.getName()) s_group.setName(g_name) # logging.info("%s: %d" % (g_name, len(s_ids))) if t_id: add_annotation(s_group, libsbml.BQB_IS, t_id, CHEBI_PREFIX) for s_id in s_ids: member = s_group.createMember() member.setIdRef(s_id) add_annotation(s_group, libsbml.BQB_IS_DESCRIBED_BY, GROUP_TYPE_EQUIV) generalize_species = lambda species_id: s_id2gr_id[species_id][0] if (species_id in s_id2gr_id) else species_id s_id_to_generalize = set(s_id2gr_id.keys()) logging.info(" creating reaction groups") for clu, r_ids in clu2r_ids.items(): representative = input_model.getReaction(list(r_ids)[0]) r_name = "generalized %s" % representative.getName() if out_sbml: reactants = dict(get_reactants(representative, stoichiometry=True)) products = dict(get_products(representative, stoichiometry=True)) if (len(r_ids) == 1) and \ not ((set(reactants.keys()) | set(products.keys())) & s_id_to_generalize): generalized_model.addReaction(representative) continue r_id2st = {generalize_species(it): st for (it, st) in reactants.items()} p_id2st = {generalize_species(it): st for (it, st) in products.items()} reversible = next((False for r_id in r_ids if not input_model.getReaction(r_id).getReversible()), True) new_r_id = create_reaction(generalized_model, r_id2st, p_id2st, name=r_name, reversible=reversible, id_=representative.getId() if len(r_ids) == 1 else None).getId() elif len(r_ids) > 1: r_id_increment += 1 new_r_id = generate_unique_id(input_model, "r_g_", r_id_increment) if len(r_ids) > 1: for r_id in r_ids: r_id2g_eq[r_id] = new_r_id, r_name if groups_sbml and groups_plugin: # save as a group r_group = groups_plugin.createGroup() r_group.setId(new_r_id) r_group.setKind(libsbml.GROUP_KIND_COLLECTION) r_group.setSBOTerm(SBO_BIOCHEMICAL_REACTION) r_group.setName(r_name) for r_id in r_ids: member = r_group.createMember() member.setIdRef(r_id) add_annotation(r_group, libsbml.BQB_IS_DESCRIBED_BY, GROUP_TYPE_EQUIV) if out_sbml: remove_unused_elements(generalized_model) save_as_sbml(generalized_model, out_sbml) if groups_sbml and groups_model: save_as_sbml(groups_model, groups_sbml) logging.info("serialized to " + groups_sbml) return r_id2g_eq, s_id2gr_id
def save_as_comp_generalized_sbml(input_model, out_sbml, groups_sbml, r_id2clu, clu2s_ids, ub_sps, onto): logging.info("serializing generalization") s_id_increment, r_id_increment = 0, 0 if groups_sbml: doc = convert_to_lev3_v1(input_model) groups_model = doc.getModel() groups_plugin = groups_model.getPlugin("groups") if groups_plugin: logging.info(" saving ubiquitous species annotations") s_group = groups_plugin.createGroup() s_group.setId("g_ubiquitous_sps") s_group.setKind(libsbml.GROUP_KIND_COLLECTION) s_group.setSBOTerm(SBO_CHEMICAL_MACROMOLECULE) s_group.setName("ubiquitous species") for s_id in ub_sps: member = s_group.createMember() member.setIdRef(s_id) add_annotation(s_group, libsbml.BQB_IS_DESCRIBED_BY, GROUP_TYPE_UBIQUITOUS) if out_sbml: # generalized model generalized_doc = convert_to_lev3_v1(input_model) generalized_model = generalized_doc.getModel() for _ in range(0, generalized_model.getNumReactions()): generalized_model.removeReaction(0) r_id2g_eq, s_id2gr_id = {}, {} if not clu2s_ids: logging.info(" nothing to serialize") else: clu2r_ids = invert_map(r_id2clu) logging.info(" creating species groups") for ((c_id, t), s_ids) in clu2s_ids.items(): comp = input_model.getCompartment(c_id) if len(s_ids) > 1: t = onto.get_term(t) t_name, t_id = (t.get_name(), t.get_id()) if t \ else (' or '.join(input_model.getSpecies(s_id).getName() for s_id in s_ids), None) if not t_id: t = t_name if out_sbml: new_species = create_species(model=generalized_model, compartment_id=comp.getId(), type_id=None, name="{0} ({1}) [{2}]".format(t_name, len(s_ids), comp.getName())) add_annotation(new_species, libsbml.BQB_IS, t_id, CHEBI_PREFIX) new_s_id = new_species.getId() else: s_id_increment += 1 new_s_id = generate_unique_id(input_model, "s_g_", s_id_increment) for s_id in s_ids: s_id2gr_id[s_id] = new_s_id, t if groups_sbml and groups_plugin: # save as a group s_group = groups_plugin.createGroup() s_group.setId(new_s_id) s_group.setKind(libsbml.GROUP_KIND_CLASSIFICATION) s_group.setSBOTerm(SBO_CHEMICAL_MACROMOLECULE) g_name = "{0} [{1}]".format(t_name, comp.getName()) s_group.setName(g_name) # logging.info("%s: %d" % (g_name, len(s_ids))) if t_id: add_annotation(s_group, libsbml.BQB_IS, t_id, CHEBI_PREFIX) for s_id in s_ids: member = s_group.createMember() member.setIdRef(s_id) add_annotation(s_group, libsbml.BQB_IS_DESCRIBED_BY, GROUP_TYPE_EQUIV) generalize_species = lambda species_id: s_id2gr_id[species_id][0] if (species_id in s_id2gr_id) else species_id s_id_to_generalize = set(s_id2gr_id.keys()) logging.info(" creating reaction groups") for clu, r_ids in clu2r_ids.items(): representative = input_model.getReaction(list(r_ids)[0]) r_name = "generalized %s" % representative.getName() if out_sbml: reactants = dict(get_reactants(representative, stoichiometry=True)) products = dict(get_products(representative, stoichiometry=True)) if (len(r_ids) == 1) and \ not ((set(reactants.keys()) | set(products.keys())) & s_id_to_generalize): create_reaction(generalized_model, reactants, products, name=representative.getName(), reversible=representative.getReversible(), id_=representative.getId()) continue r_id2st = {generalize_species(it): st for (it, st) in reactants.items()} p_id2st = {generalize_species(it): st for (it, st) in products.items()} reversible = next((False for r_id in r_ids if not input_model.getReaction(r_id).getReversible()), True) new_r_id = create_reaction(generalized_model, r_id2st, p_id2st, name=r_name, reversible=reversible, id_=representative.getId() if len(r_ids) == 1 else None).getId() elif len(r_ids) > 1: r_id_increment += 1 new_r_id = generate_unique_id(input_model, "r_g_", r_id_increment) if len(r_ids) > 1: for r_id in r_ids: r_id2g_eq[r_id] = new_r_id, r_name if groups_sbml and groups_plugin: # save as a group r_group = groups_plugin.createGroup() r_group.setId(new_r_id) r_group.setKind(libsbml.GROUP_KIND_COLLECTION) r_group.setSBOTerm(SBO_BIOCHEMICAL_REACTION) r_group.setName(r_name) for r_id in r_ids: member = r_group.createMember() member.setIdRef(r_id) add_annotation(r_group, libsbml.BQB_IS_DESCRIBED_BY, GROUP_TYPE_EQUIV) if out_sbml: remove_unused_elements(generalized_model) save_as_sbml(generalized_model, out_sbml) if groups_sbml and groups_model: save_as_sbml(groups_model, groups_sbml) logging.info("serialized to " + groups_sbml) return r_id2g_eq, s_id2gr_id