期刊
FEMS MICROBIOLOGY REVIEWS
卷 40, 期 6, 页码 961-979出版社
OXFORD UNIV PRESS
DOI: 10.1093/femsre/fuw024
关键词
synthetic ecology; microbial ecology; population dynamics; microbial interactions; community assembly
类别
资金
- Swiss National Science Foundation [31003A 149304]
- SystemsX.ch, the Swiss Initiative in Systems Biology (MicroscapesX)
- Swiss National Science Foundation (SNF) [31003A_149304] Funding Source: Swiss National Science Foundation (SNF)
Assemblages of microbial genotypes growing together can display surprisingly complex and unexpected dynamics and result in community-level functions and behaviors that are not readily expected from analyzing each genotype in isolation. This complexity has, at least in part, inspired a discipline of synthetic microbial ecology. Synthetic microbial ecology focuses on designing, building and analyzing the dynamic behavior of 'ecological circuits' (i.e. a set of interacting microbial genotypes) and understanding how community-level properties emerge as a consequence of those interactions. In this review, we discuss typical objectives of synthetic microbial ecology and the main advantages and rationales of using synthetic microbial assemblages. We then summarize recent findings of current synthetic microbial ecology investigations. In particular, we focus on the causes and consequences of the interplay between different microbial genotypes and illustrate how simple interactions can create complex dynamics and promote unexpected community-level properties. We finally propose that distinguishing between active and passive interactions and accounting for the pervasiveness of competition can improve existing frameworks for designing and predicting the dynamics of microbial assemblages.Interactions between different microbial genotypes can generate complex dynamics and promote community-level functions that might not be readily predicted from investigating each genotype in isolation.Interactions between different microbial genotypes can generate complex dynamics and promote community-level functions that might not be readily predicted from investigating each genotype in isolation.
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