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Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.

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dc.contributor.authorÇubuk, Cankut
dc.contributor.authorHidalgo, Marta R
dc.contributor.authorAmadoz, Alicia
dc.contributor.authorRian, Kinza
dc.contributor.authorSalavert, Francisco
dc.contributor.authorPujana, Miguel A
dc.contributor.authorMateo, Francesca
dc.contributor.authorHerranz, Carmen
dc.contributor.authorCarbonell-Caballero, Jose
dc.contributor.authorDopazo, Joaquín
dc.date.accessioned2024-02-10T20:00:53Z
dc.date.available2024-02-10T20:00:53Z
dc.date.issued2019-03-01
dc.description.abstractIn spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.
dc.format.page7es_ES
dc.format.volume5es_ES
dc.identifier.doi10.1038/s41540-019-0087-2
dc.identifier.e-issn2056-7189es_ES
dc.identifier.journalNPJ systems biology and applicationses_ES
dc.identifier.otherhttp://hdl.handle.net/10668/13684
dc.identifier.pubmedID30854222es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/17824
dc.language.isoeng
dc.rights.accessRightsopen accesses_ES
dc.rights.licenseAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.meshComputational Biology
dc.subject.meshComputer Simulation
dc.subject.meshDrug Discovery
dc.subject.meshGene Regulatory Networks
dc.subject.meshHumans
dc.subject.meshInternet
dc.titleDifferential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.
dc.typeresearch article
dc.type.hasVersionVoR
dspace.entity.typePublication
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IBIMA-Plataforma BIONAND - Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (Andalucía)
IDIBELL - Instituto de Investigación Biomédica de Bellvitge (Cataluña)