期刊
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
卷 19, 期 -, 页码 226-246出版社
ELSEVIER
DOI: 10.1016/j.csbj.2020.12.003
关键词
Genome-scale metabolic modelling; Microbial community; Optimization; Design; Engineering; Computational methods; Synthetic microbial consortia
资金
- European Union's Horizon 2020 Research and Innovation Programme [814650, 870294]
- Severo Ochoa Program for Centres of Excellence in R&D from the Agencia Estatal de Investigacion of Spain [SEV-2016-0672]
Microbes do not live in isolation but in microbial communities. Most methods and biotechnological applications involving microorganisms have been developed in the context of isolated microbes, making in vivo microbial consortia development extremely difficult and costly. Computational approaches provide a cost-effective alternative to study microbial communities via descriptive and engineering modelling.
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. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
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