Towards applications of genome-scale metabolic model-based approaches in designing synthetic microbial communities

Abstract

Background: Synthetic 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.

Results: Genome-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.

Conclusions: Although 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.

Author summary

The applications of computational tools have been demonstrated to increase the development of synthetic microbial communities which is an emerging field and can be used in various biotechnology applications. As one effective tool, genome-scale metabolic modeling helps to reconstruct testable metabolic networks from genomic information and can quantitatively simulate entire metabolic fluxes of communities in considering microbe-microbe and microbe-habitat interactions. In-depth study of underlying mechanisms of microbial interactions using metabolic models and of coupling the models with multi-omics data or machine learning can further extend applications in designing synthetic communities.

Graphical abstract

Keywords

genome-scale metabolic modeling / microbial community design / interspecies interaction / environmental impact / community-level performance

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ACKNOWLEDGEMENTS

This work was supported by grants from the National Natural Science Foundation of China (Nos. 92051102, 32200099, 32225003 and 31970105); the Innovation Team Project of Universities in Guangdong Province (No. 2020KCXTD023); and the Shenzhen Science and Technology Program (JCYJ20200109105010363). All sketches used in the Fig.1 and Fig.3 are obtained from vecteezy website.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Huan Du, Meng Li, Yang Liu declare that they have no conflict of interest or financial conflicts to disclose.

This review does not contain any studies with human or animal subjects performed by any of the authors.

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