4.7 Review

Metabolic modelling approaches for describing and engineering microbial communities

期刊

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

  1. European Union's Horizon 2020 Research and Innovation Programme [814650, 870294]
  2. Severo Ochoa Program for Centres of Excellence in R&D from the Agencia Estatal de Investigacion of Spain [SEV-2016-0672]

向作者/读者索取更多资源

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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据