Emergent Ecological Patterns and Modelling of Gut Microbiomes in Health and in Disease

Abstract

AbstractThe Human Gut Microbiome (HGM) harbors a huge diversity in terms of species. Recent work has shown the existence of macroecological laws describing the variation and diversity in microbial communities using 16s OTU data. Here we investigate whether such patterns could be used to discriminate between different models of population dynamics, and characterize gut microbiomes in healthy vs. disease states. In this work, by using shotgun metagenomics data, we first discuss how a simple scaling relation (Taylor’s law) plays a crucial role when building theoretical models for the gut microbiome. Moving from recent results, we then introduce theSymmetric Schlömlich Model, which is related to a widely used prior for relative species abundance (RSA), i.e. theDirichlet Model. We show that the RSA can be obtained by constraining the solution of theStochastic Logistic Growth Model, thus endowing all parameters with a clear ecological interpretation. Finally, we characterize the gut microbiomes from an ecological viewpoint, and compare how different models describe the emergent gut microbiome patterns in healthy and disease conditions.