Metagenome-scale community metabolic modelling for understanding the role of gut microbiota in human health.

Metabolic activities of the microbial population are important to maintain the balance of almost all the ecosystems on earth. In the human gut environment, these microbial communities play essential roles in digestion and help to maintain biochemical homeostasis by synthesizing several vital metabolic compounds. Imbalance in the microbial abundance and community structure in the human gut microbiota leads to different diseases and metabolic disorders. Studying the metabolic interplay between the microbial consortia within the host environment is the key to exploring the cause behind the development of various diseases condition. However, mapping the entire biochemical characteristic of human gut microbiota may not be feasible only through experimental approaches. Therefore, the advanced systems biology approach, i.e., metagenome-scale community metabolic modelling, is introduced for understanding the metabolic role and interaction pattern of the entire microbiome. This in silico method directly uses the metagenomic information to model the microbial communities, which mimic the metabolic behavior of the human gut microbiome. This review discusses the recent development of metagenome-scale community metabolic model reconstruction tools and their application in studying the inter-link between the human gut microbiome and health. The application of the community metabolic models to study the metabolic profile of the human gut microbiome has also been investigated. Alteration of the metabolic fluxes associated with different biochemical activities in type 1 diabetics, type 2 diabetics, inflammatory bowel diseases (IBD), gouty arthritis, colorectal cancer (CRC), etc., has also been assessed with the metagenome-scale models. Thus, modelling the microbial communities combined with advanced experimental design may lead to novel therapeutic approaches like personalized microbiome modelling for treating human disease.