Metabolic modelling approaches for describing and engineering microbial communities

Abstract

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.

Keywords: Computational methods; Design; Engineering; Genome-scale metabolic modelling; Microbial community; Optimization; Synthetic microbial consortia.

Graphical abstract

Fig. 1

A, Schematic representation of the basic ecological interactions between the microbial strains in co-culture, green positive and red negative interactions. B, Schematic representation of the SMCs categories. Black arrows stability interactions and green arrows functionality interactions. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Microbial community optimization/design goal categories. Section 5 describes each category in detail. Table 4 shows detailed applications and methods of these different categories. A. Optimize production of a metabolite of interest (red circle) depending on community parameters. B. Optimize distribution of the reactions within a metabolic pathway among different strains. C. Optimize individual strain growth to reach a stable community over time. D. Optimize concentration of nutrients (circles) available in the microbial community medium. E. Optimize physical distribution of the strains in the community. The flexible optimization category covers all the optimization goals. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)