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dc.contributor.authorGarcía-Jiménez, Beatrizes_ES
dc.contributor.authorGarcía, José Luises_ES
dc.contributor.authorNogales, Juanes_ES
dc.date.accessioned2019-10-10T12:35:49Z-
dc.date.available2019-10-10T12:35:49Z-
dc.date.issued2018-09-01-
dc.identifier.citationBioinformatics 34 (17) i954–i963 (2018)es_ES
dc.identifier.issn1367-4803-
dc.identifier.urihttp://hdl.handle.net/10261/192442-
dc.description10 p.-5 fig.-2 tab.es_ES
dc.description.abstractMotivation Synthetic microbial communities begin to be considered as promising multicellular biocatalysts having a large potential to replace engineered single strains in biotechnology applications, in pharmaceutical, chemical and living architecture sectors. In contrast to single strain engineering, the effective and high-throughput analysis and engineering of microbial consortia face the lack of knowledge, tools and well-defined workflows. This manuscript contributes to fill this important gap with a framework, called FLYCOP (FLexible sYnthetic Consortium OPtimization), which contributes to microbial consortia modeling and engineering, while improving the knowledge about how these communities work. FLYCOP selects the best consortium configuration to optimize a given goal, among multiple and diverse configurations, in a flexible way, taking temporal changes in metabolite concentrations into account.es_ES
dc.description.abstractResults In contrast to previous systems optimizing microbial consortia, FLYCOP has novel characteristics to face up to new problems, to represent additional features and to analyze events influencing the consortia behavior. In this manuscript, FLYCOP optimizes a Synechococcus elongatus-Pseudomonas putida consortium to produce the maximum amount of bio-plastic (PHA, polyhydroxyalkanoate), and highlights the influence of metabolites exchange dynamics in a four auxotrophic Escherichia coli consortium with parallel growth. FLYCOP can also provide an explanation about biological evolution driving evolutionary engineering endeavors by describing why and how heterogeneous populations emerge from monoclonal ones.es_ES
dc.description.abstractAvailability and implementation Code reproducing the study cases described in this manuscript are available on-line: https://github.com/beatrizgj/FLYCOPes_ES
dc.description.abstractSupplementary information Supplementary data are available at Bioinformatics online.es_ES
dc.description.abstractIssue Section: SYSTEMSes_ES
dc.description.sponsorshipThis work was supported from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement no 686585, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN).es_ES
dc.language.isoenges_ES
dc.publisherOxford University Presses_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/686585es_ES
dc.relationMINECO/ICTI2013-2016/BIO2014-59528-JINes_ES
dc.relation.isversionofPublisher's versiones_ES
dc.rightsopenAccesses_ES
dc.titleFLYCOP: metabolic modeling-based analysis and engineering microbial communitieses_ES
dc.typeartículoes_ES
dc.identifier.doihttp://dx.doi.org/10.1093/bioinformatics/bty561-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttps://doi.org/10.1093/bioinformatics/bty561es_ES
dc.identifier.e-issn1460-2059-
dc.rights.licensehttps://creativecommons.org/licenses/by/4.0/es_ES
dc.contributor.funderEuropean Commissiones_ES
dc.contributor.funderMinisterio de Economía y Competitividad (España)es_ES
dc.relation.csices_ES
oprm.item.hasRevisionno ko 0 false*
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000780es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100003329es_ES
dc.contributor.orcidGarcía, José Luis [0000-0002-9238-2485]es_ES
dc.contributor.orcidNogales, Juan [0000-0002-4961-0833]es_ES
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