Inferring composition and function of the human gut microbiome in time and space: A review of genome-scale metabolic modelling tools

• Main Authors: Álvaro Altamirano, Pedro A. Saa, Daniel Garrido
• Format: Article
• Language: English
• Published: Elsevier 2020-01-01
• Series: Computational and Structural Biotechnology Journal
• Online Access: http://www.sciencedirect.com/science/article/pii/S200103702030502X

The human gut hosts a complex community of microorganisms that directly influences gastrointestinal physiology, playing a central role in human health. Because of its importance, the metabolic interplay between the gut microbiome and host metabolism has gained special interest. While there has been great progress in the field driven by metagenomics and experimental studies, the mechanisms underpinning microbial composition and interactions in the microbiome remain poorly understood. Genome-scale metabolic models are mathematical structures capable of describing the metabolic potential of microbial cells. They are thus suitable tools for probing the metabolic properties of microbial communities. In this review, we discuss the most recent and relevant genome-scale metabolic modelling tools for inferring the composition, interactions, and ultimately, biological function of the constituent species of a microbial community with special emphasis in the gut microbiota. Particular attention is given to constraint-based metabolic modelling methods as well as hybrid agent-based methods for capturing the interactions and behavior of the community in time and space. Finally, we discuss the challenges hindering comprehensive modelling of complex microbial communities and its application for the in-silico design of microbial consortia with therapeutic functions.