Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Metabolic Modelling Approaches for Describing and Engineering Microbial Communities

Version 1 : Received: 22 September 2020 / Approved: 23 September 2020 / Online: 23 September 2020 (09:52:17 CEST)

A peer-reviewed article of this Preprint also exists.

García-Jiménez, B.; Torres-Bacete, J.; Nogales, J. Metabolic Modelling Approaches for Describing and Engineering Microbial Communities. Computational and Structural Biotechnology Journal, 2021, 19, 226–246. https://doi.org/10.1016/j.csbj.2020.12.003. García-Jiménez, B.; Torres-Bacete, J.; Nogales, J. Metabolic Modelling Approaches for Describing and Engineering Microbial Communities. Computational and Structural Biotechnology Journal, 2021, 19, 226–246. https://doi.org/10.1016/j.csbj.2020.12.003.

Abstract

Microbes do not live in isolation but in microbial communities. The relevance of microbial communities is increasing due to the awareness about their biotechnological influences in 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 (both in vivo and in silico) have been developed in the context of isolated microbes. In vivo microbial consortia development, i.e. to reproduce the community life conditions in the wet-lab, is extremely difficult and expensive requiring of computational approaches to advance knowledge about microbial communities, mainly with descriptive modelling, and further with engineering modelling. In this review we provide a detailed compilation of available examples of engineered microbial communities as a launch pad for an exhaustive and historical revision of those computational methods devoted so far toward the better understanding, and rational engineering of natural and synthetic microbial communities.

Keywords

genome-scale metabolic model; microbial community; optimization; design; engineering; computational methods; synthetic microbial consortia

Subject

Biology and Life Sciences, Biology and Biotechnology

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