{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Pdf":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1601050000?filename=MODEL1601050000.pdf"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1601050000?filename=MODEL1601050000-biopax3.owl","https://www.ebi.ac.uk/biomodels/model/download/MODEL1601050000?filename=MODEL1601050000-biopax2.owl"],"Svg":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1601050000?filename=MODEL1601050000.svg"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1601050000?filename=MODEL1601050000_url.xml","https://www.ebi.ac.uk/biomodels/model/download/MODEL1601050000?filename=MODEL1601050000_urn.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1601050000?filename=MODEL1601050000.sci","https://www.ebi.ac.uk/biomodels/model/download/MODEL1601050000?filename=MODEL1601050000.png","https://www.ebi.ac.uk/biomodels/model/download/MODEL1601050000?filename=MODEL1601050000.vcml"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"submitter":["Miguel Ponce-de-Leon"],"curationStatus":["Non-curated"],"modellingApproach":["ordinary differential equation model"],"levelVersion":["L2V1"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/MODEL1601050000"],"publication_pubmed":["26629901"],"isPrivate":["false"],"repository":["BioModels"],"modelFormat":["SBML"],"omics_type":["Models"],"tokenised_name":["Ponce de Leon2015   Genome Scale Model of Bacterial Metabolism (MM130)"],"publication_year":["2015"],"submissionId":["MODEL1601050000"],"modelFlag":["Non Kinetic"],"publication_authors":["Miguel Ponce-de-León, Jorge Calle-Espinosa, Juli Peretó, Francisco Montero"],"first_author":["Miguel Ponce-de-León"],"publication":["26629901,\n                            Genome-scale metabolic models usually contain inconsistencies that manifest as blocked reactions and gap metabolites. With the purpose to detect recurrent inconsistencies in metabolic models, a large-scale analysis was performed using a previously published dataset of 130 genome-scale models. The results showed that a large number of reactions (~22%) are blocked in all the models where they are present. To unravel the nature of such inconsistencies a metamodel was construed by joining the 130 models in a single network. This metamodel was manually curated using the unconnected modules approach, and then, it was used as a reference network to perform a gap-filling on each individual genome-scale model. Finally, a set of 36 models that had not been considered during the construction of the metamodel was used, as a proof of concept, to extend the metamodel with new biochemical information, and to assess its impact on gap-filling results. The analysis performed on the metamodel allowed to conclude: 1) the recurrent inconsistencies found in the models were already present in the metabolic database used during the reconstructions process; 2) the presence of inconsistencies in a metabolic database can be propagated to the reconstructed models; 3) there are reactions not manifested as blocked which are active as a consequence of some classes of artifacts, and; 4) the results of an automatic gap-filling are highly dependent on the consistency and completeness of the metamodel or metabolic database used as the reference network. In conclusion the consistency analysis should be applied to metabolic databases in order to detect and fill gaps as well as to detect and remove artifacts and redundant information.. 12, 10.\n                            Departamento de Bioquímica y Biología Molecular I, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, Ciudad Universitaria, Madrid 28045, Spain."],"submitter_mail":["migponce@ucm.es"],"submitter_affiliation":["Universidad Complutense de Madrid"],"pubmed_abstract":["Genome-scale metabolic models usually contain inconsistencies that manifest as blocked reactions and gap metabolites. With the purpose to detect recurrent inconsistencies in metabolic models, a large-scale analysis was performed using a previously published dataset of 130 genome-scale models. The results showed that a large number of reactions (~22%) are blocked in all the models where they are present. To unravel the nature of such inconsistencies a metamodel was construed by joining the 130 models in a single network. This metamodel was manually curated using the unconnected modules approach, and then, it was used as a reference network to perform a gap-filling on each individual genome-scale model. Finally, a set of 36 models that had not been considered during the construction of the metamodel was used, as a proof of concept, to extend the metamodel with new biochemical information, and to assess its impact on gap-filling results. The analysis performed on the metamodel allowed to conclude: 1) the recurrent inconsistencies found in the models were already present in the metabolic database used during the reconstructions process; 2) the presence of inconsistencies in a metabolic database can be propagated to the reconstructed models; 3) there are reactions not manifested as blocked which are active as a consequence of some classes of artifacts, and; 4) the results of an automatic gap-filling are highly dependent on the consistency and completeness of the metamodel or metabolic database used as the reference network. In conclusion the consistency analysis should be applied to metabolic databases in order to detect and fill gaps as well as to detect and remove artifacts and redundant information."],"pubmed_title":["Consistency Analysis of Genome-Scale Models of Bacterial Metabolism: A Metamodel Approach."],"pubmed_authors":["Ponce-de-Leon Miguel M, Calle-Espinosa Jorge J, Peretó Juli J, Montero Francisco F"],"name_synonyms":["biochemical pathways, scale tissue, multicellular organism metabolic process, Metabolic Process, scale, biodegradation, Genomes, Metabolic, degradation, Process, catabolism, Processes, plant peltate hair, peltate hair, metabolism resulting in cell growth, Metabolic Concepts, Metabolic Concept, metabolic process resulting in cell growth, whole genome, Metabolic Processes, Anabolism., Concept, Metabolic Phenomena, Metabolism Concepts, Metabolism, Phenomena, Concepts, biotransformation, secretion, scales, Metabolism Concept, Phenomenon, Metabolism Phenomena, Catabolism, metabolism, Metabolic Phenomenon"],"pubmed_abstract_synonyms":["extent, scale tissue, IPP2A2, rasGAP, determination, acetylglucosaminyltransferase-like protein, GAPDH II, peltate hair, number, 10538, Mbp1, GTPase-activating protein, secondary metabolites, Gapdh13F, GAP1, Ximpact, LARGE1, presence, froggy, PHAPII, Gyltl1a, protrusion, 5730420M11Rik, GADPH, primary metabolites, CG8893, gap1, PKWS, myd, FBgn 32821, imprinted and ancient gene protein, SET, like-acetylglucosaminyltransferase, Genomes, RasGAP, TAF-I, MDDGB6, plant peltate hair, Gapd, GA3PDH, ipp2a2, Gap 1, blocked, Mbp-1, 2pp2a, mip, LARGE, Artefacts, GAPDH2, Gaps, CG10574, DmelCG4299, BPFD#36, d-CdGAPr, IGAAD, set, Gapdh-2, 2PP2A, DmelCG10574, taf-ibeta, Artifact, dSET, dSet, CM-AVM, CG6721, DmelCG8893, phapii, anatomical protrusion, Data Set, Artefact, gyltl1b-b, completeness, Artifact., igaad, StF-IT-1, RASGAP, RASA, results, impact-a, group, count in organism, Prgs, I-2PP2A, Gapdh, MDDGA6, mKIAA0609, sxt, chemical analysis, Dm I-2, I2PP2A, imprinted and ancient gene protein homolog, IMPACT, rI533, scales, GAP, Gap, p120GAP, p120RASGAP, KIAA0609, acetylglucosaminyltransferase-like 1A, GAPDH, Data Base, fg, Ras-GAP, CG10538, DmelCG6721, gyltl1b, HLA-DR-associated protein II, scale, ensemble, DI-2, I-2Dm, DmelCG10538, mdc1d, metabolite, whole genome, gap, CG4299, CMAVM, LARGE_HUMAN, I-2PP1, MDC1D, dSET/TAF-Ibeta, 2610030F17Rik, TAF-IBETA, like-glycosyltransferase, enr, spine, Ras p21 protein activator, metabolites, cardinality, TAF-Ibeta, assay, AA407739, E430016J11Rik, i2pp2a, RWDD5, glycosyltransferase-like protein LARGE1"],"description_synonyms":["Desc, DESCR., Description, Descriptive, Descriptor, description, Product Description/Appearance"],"pubmed_title_synonyms":["biochemical pathways, scale tissue, multicellular organism metabolic process, Metabolic Process, scale, biodegradation, determination, Genomes, Metabolic, degradation, Process, catabolism, Processes, plant peltate hair, peltate hair, metabolism resulting in cell growth, Metabolic Concepts, Metabolic Concept, metabolic process resulting in cell growth, whole genome, Metabolic Processes, Anabolism., Concept, Metabolic Phenomena, Metabolism Concepts, Metabolism, chemical analysis, Phenomena, Concepts, biotransformation, secretion, assay, scales, Metabolism Concept, Phenomenon, Metabolism Phenomena, Catabolism, metabolism, Metabolic Phenomenon"],"additional_accession":[]},"is_claimable":false,"name":"Ponce-de-Leon2015 - Genome-Scale Model of Bacterial Metabolism (MM130)","description":"No description","dates":{"last_modification":"2016-02-08","publication":"2016-02-08","submission":"2016-01-05"},"accession":"MODEL1601050000","cross_references":{"pubmed":["26629901"],"biomodels__db":["MODEL1601050000"]}}