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dc.contributor.authorBaena García, Andrés-
dc.contributor.authorLópez Agudelo, Víctor Alonso-
dc.contributor.authorBarrera Robledo, Luis Fernando-
dc.contributor.authorRíos Estepa, Rigoberto-
dc.contributor.authorWu, HuiHai-
dc.contributor.authorLaing, Emma-
dc.contributor.authorBeste, Dany-
dc.contributor.authorMendum, Tom-
dc.date.accessioned2024-09-11T19:08:29Z-
dc.date.available2024-09-11T19:08:29Z-
dc.date.issued2020-
dc.identifier.citationLópez-Agudelo VA, Mendum TA, Laing E, Wu H, Baena A, Barrera LF, Beste DJV, Rios-Estepa R. A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks. PLoS Comput Biol. 2020 Jun 15;16(6):e1007533. doi: 10.1371/journal.pcbi.1007533.spa
dc.identifier.issn1553-734X-
dc.identifier.urihttps://hdl.handle.net/10495/42028-
dc.description.abstractABSTRACT: Metabolism underpins the pathogenic strategy of the causative agent of TB, Mycobacterium tuberculosis (Mtb), and therefore metabolic pathways have recently re-emerged as attractive drug targets. A powerful approach to study Mtb metabolism as a whole, rather than just individual enzymatic components, is to use a systems biology framework, such as a Genome-Scale Metabolic Network (GSMN) that allows the dynamic interactions of all the components of metabolism to be interrogated together. Several GSMNs networks have been constructed for Mtb and used to study the complex relationship between the Mtb genotype and its phenotype. However, the utility of this approach is hampered by the existence of multiple models, each with varying properties and performances. Here we systematically evaluate eight recently published metabolic models of Mtb-H37Rv to facilitate model choice. The best performing models, sMtb2018 and iEK1011, were refined and improved for use in future studies by the TB research community.spa
dc.format.extent31 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.publisherPublic Library of Sciencespa
dc.type.hasversioninfo:eu-repo/semantics/publishedVersionspa
dc.rightsinfo:eu-repo/semantics/openAccessspa
dc.rights.urihttp://creativecommons.org/licenses/by/2.5/co/*
dc.titleA systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networksspa
dc.typeinfo:eu-repo/semantics/articlespa
dc.publisher.groupBioprocesosspa
dc.publisher.groupGrupo de Inmunología Celular e Inmunogenéticaspa
dc.identifier.doi10.1371/journal.pcbi.1007533-
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.rights.accessrightshttp://purl.org/coar/access_right/c_abf2spa
dc.identifier.eissn1553-7358-
oaire.citationtitlePLoS Computational Biologyspa
oaire.citationstartpage1spa
oaire.citationendpage31spa
oaire.citationvolume16spa
oaire.citationissue6spa
dc.rights.creativecommonshttps://creativecommons.org/licenses/by/4.0/spa
oaire.fundernameColombia. Ministerio de Ciencia, Tecnología e Innovación - MinCienciasspa
oaire.fundernameMedical Research Councilspa
dc.publisher.placeSan Francisco, Estados Unidosspa
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa
dc.type.redcolhttps://purl.org/redcol/resource_type/ARTspa
dc.type.localArtículo de investigaciónspa
dc.subject.decsTeorema de Bayes-
dc.subject.decsBayes Theorem-
dc.subject.decsBiomasa-
dc.subject.decsBiomass-
dc.subject.decsCarbono-
dc.subject.decsCarbon-
dc.subject.decsColesterol-
dc.subject.decsCholesterol-
dc.subject.decsMedios de Cultivo-
dc.subject.decsCulture Media-
dc.subject.decsReacciones Falso Positivas-
dc.subject.decsFalse Positive Reactions-
dc.subject.decsMycobacterium tuberculosis-
dc.subject.decsFenotipo-
dc.subject.decsPhenotype-
dc.subject.decsValor Predictivo de las Pruebas-
dc.subject.decsPredictive Value of Tests-
dc.subject.decsRedes y Vías Metabólicas-
dc.subject.decsMetabolic Networks and Pathways-
dc.subject.decsGlicerol-
dc.subject.decsGlycerol-
dc.subject.decsGenoma Bacteriano-
dc.subject.decsGenome, Bacterial-
dc.subject.decsBiología de Sistemas-
dc.subject.decsSystems Biology-
dc.description.researchgroupidCOL0008639spa
dc.description.researchgroupidCOL0023715spa
oaire.awardnumberMinCiencias 1115-5693-3520spa
oaire.awardnumberMR/K01224X/1spa
dc.subject.meshurihttps://id.nlm.nih.gov/mesh/D001499-
dc.subject.meshurihttps://id.nlm.nih.gov/mesh/D018533-
dc.subject.meshurihttps://id.nlm.nih.gov/mesh/D002244-
dc.subject.meshurihttps://id.nlm.nih.gov/mesh/D002784-
dc.subject.meshurihttps://id.nlm.nih.gov/mesh/D003470-
dc.subject.meshurihttps://id.nlm.nih.gov/mesh/D005189-
dc.subject.meshurihttps://id.nlm.nih.gov/mesh/D009169-
dc.subject.meshurihttps://id.nlm.nih.gov/mesh/D010641-
dc.subject.meshurihttps://id.nlm.nih.gov/mesh/D011237-
dc.subject.meshurihttps://id.nlm.nih.gov/mesh/D053858-
dc.subject.meshurihttps://id.nlm.nih.gov/mesh/D005990-
dc.subject.meshurihttps://id.nlm.nih.gov/mesh/D016680-
dc.subject.meshurihttps://id.nlm.nih.gov/mesh/D049490-
dc.relation.ispartofjournalabbrevPLoS Comput Biol.spa
oaire.funderidentifier.rorRoR:03fd5ne08-
oaire.funderidentifier.rorRoR:03x94j517-
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