Community metabolic modeling approaches to understanding the gut microbiome: Bridging biochemistry and ecology

Abstract

Interest in the human microbiome is at an all time high. The number of human microbiome studies is growing exponentially, as are reported associations between microbial communities and disease. However, we have not been able to translate the ever-growing amount of microbiome sequence data into better health. To do this, we need a practical means of transforming a disease-associated microbiome into a health-associated microbiome. This will require a framework that can be used to generate predictions about community dynamics within the microbiome under different conditions, predictions that can be tested and validated. In this review, using the gut microbiome to illustrate, we describe two classes of model that are currently being used to generate predictions about microbial community dynamics: ecological models and metabolic models. We outline the strengths and weaknesses of each approach and discuss the insights into the gut microbiome that have emerged from modeling thus far. We then argue that the two approaches can be combined to yield a community metabolic model, which will supply the framework needed to move from high-throughput omics data to testable predictions about how prebiotic, probiotic, and nutritional interventions affect the microbiome. We are confident that with a suitable model, researchers and clinicians will be able to harness the stream of sequence data and begin designing strategies to make targeted alterations to the microbiome and improve health.

Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss

“…These limitations hinder a systematic and comprehensive study of long term genomic reduction or identification of general principles in the emergence of species interaction. On the other hand, theoretical and computational models have been broadly useful for studying microbial communities [ 22 , 23 ], and indeed several recent studies have used computational models to specifically address the evolution of cooperation [ 24 , 25 ]. Such studies have allowed long time scales to be easily modeled and have produced useful insights into genetic and environmental determinates of cooperation, but tend to explicitly model interaction in a non-mechanistic manner.…”

A reverse metabolic approach to weaning: in silico identification of immune-beneficial infant gut bacteria, mining their metabolism for prebiotic feeds and sourcing these feeds in the natural product space

“…Our in silico study of this microbial community of probiotics identified a total of 632 unique exogenous metabolites which are potentially required for growth under normal circumstances [ 20 ]. Among them, 62 are essential for all strains, 200 are required by single microbes in the community, and 398 are shared between different strains.…”

AMON: Annotation of metabolite origins via networks to better integrate microbiome and metabolome data

“…Although our application is specifically designed to work with the KEGG database, similar logic could be used for other databases such as MetaCyc (Caspi et al , 2014). Our tool also does not apply methods such as gap-filling (Thiele et al , 2014; Orth and Palsson, 2010) and metabolic modeling (Orth et al , 2010; Mendes-Soares and Chia, 2017) in its estimates. The goal is not to produce precise measurements of the contributions of the microbiome and host to the abundance of a metabolite.…”