Affiliation:
1. Archaeal Biology Center Institute for Advanced Study Shenzhen University Shenzhen 518060 China
2. Shenzhen Key Laboratory of Marine Microbiome Engineering Institute for Advanced Study Shenzhen University Shenzhen 518060 China
Abstract
BackgroundSynthetic microbial communities, with different strains brought together by balancing their nutrition and promoting their interactions, demonstrate great advantages for exploring complex performance of communities and for further biotechnology applications. The potential of such microbial communities has not been explored, due to our limited knowledge of the extremely complex microbial interactions that are involved in designing and controlling effective and stable communities.ResultsGenome‐scale metabolic models (GEM) have been demonstrated as an effective tool for predicting and guiding the investigation and design of microbial communities, since they can explicitly and efficiently predict the phenotype of organisms from their genotypic data and can be used to explore the molecular mechanisms of microbe‐habitats and microbe‐microbe interactions. In this work, we reviewed two main categories of GEM‐based approaches and three uses related to design of synthetic microbial communities: predicting multi‐species interactions, exploring environmental impacts on microbial phenotypes, and optimizing community‐level performance.ConclusionsAlthough at the infancy stage, GEM‐based approaches exhibit an increasing scope of applications in designing synthetic microbial communities. Compared to other methods, especially the use of laboratory cultures, GEM‐based approaches can greatly decrease the trial‐and‐error cost of various procedures for designing synthetic communities and improving their functionality, such as identifying community members, determining media composition, evaluating microbial interaction potential or selecting the best community configuration. Future efforts should be made to overcome the limitations of the approaches, ranging from quality control of GEM reconstructions to community‐level modeling algorithms, so that more applications of GEMs in studying phenotypes of microbial communities can be expected.
Funder
National Natural Science Foundation of China
Subject
Applied Mathematics,Computer Science Applications,Biochemistry, Genetics and Molecular Biology (miscellaneous),Modeling and Simulation
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献