Microbial dynamics of human obesity

The human body plays host to thousands of bacterial species in a variety of ecosystems. Until recently, microbial communities have been impossible to investigate thoroughly, as the vast majority of bacteria cannot be cultured through laboratory techniques. New technologies (e.g. high-throughput sequencing, 16S rRNA surveys) allow us to deeply sample the genetic content of a microbial environment in order to estimate its overall composition and functional capacity. Recent studies in this context have revealed that human obesity has a microbial component: obese gut microbiomes are distinct from the lean population. This result indicates potential therapeutic approaches to treating obesity by manipulating gut microflora. However, our limited knowledge of the microbial interactions in the gut hinders our ability to design future experiments or effective treatments. Using 16S rRNA time-series sequence data from obese individuals on a one-year diet, we employ mathematical modeling to study microbial population dynamics in the human gut. The model indicates several interspecific interactions in this microbial community, and we investigate the impact of prebiotic and probiotic therapies for obesity through simulation. To our knowledge, this is the first model to quantitatively describe the in vivo microbial dynamics of the human gut using sequence data, and we hope this application illustrates the insight mathematical modeling can bring to the field of metagenomics.