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Metabolic modelling approaches for describing and engineering microbial communities.
García-Jiménez, Beatriz; Torres-Bacete, Jesús; Nogales, Juan.
Afiliación
  • García-Jiménez B; Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), 28049 Madrid, Spain.
  • Torres-Bacete J; Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM), Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223-Pozuelo de Alarcón, Madrid, Spain.
  • Nogales J; Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), 28049 Madrid, Spain.
Comput Struct Biotechnol J ; 19: 226-246, 2021.
Article en En | MEDLINE | ID: mdl-33425254
Microbes do not live in isolation but in microbial communities. The relevance of microbial communities is increasing due to growing awareness of their influence on a huge number of environmental, health and industrial processes. Hence, being able to control and engineer the output of both natural and synthetic communities would be of great interest. However, most of the available methods and biotechnological applications involving microorganisms, both in vivo and in silico, have been developed in the context of isolated microbes. In vivo microbial consortia development is extremely difficult and costly because it implies replicating suitable environments in the wet-lab. Computational approaches are thus a good, cost-effective alternative to study microbial communities, mainly via descriptive modelling, but also via engineering modelling. In this review we provide a detailed compilation of examples of engineered microbial communities and a comprehensive, historical revision of available computational metabolic modelling methods to better understand, and rationally engineer wild and synthetic microbial communities.
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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Comput Struct Biotechnol J Año: 2021 Tipo del documento: Article País de afiliación: España

Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Comput Struct Biotechnol J Año: 2021 Tipo del documento: Article País de afiliación: España