title: 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.
authors.affiliation: Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA.
authors.forename: Helena
authors.initials: H
authors.lastname: Mendes-Soares
authors.affiliation: Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA; Department of Bioengineering and Physiology, College of Medicine, Mayo Clinic, Rochester, MN 55905, USA. Electronic address: chia.nicholas@mayo.edu.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Chia
mesh_terms: D000818:Animals; D000069196:Gastrointestinal Microbiome; D041981:Gastrointestinal Tract / Q000382:microbiology; D006801:Humans; D053858:Metabolic Networks and Pathways; D056265:Microbial Interactions; D008954:Models, Biological
keywords: Community metabolic models; Ecological models; Metabolic models; Microbiome