Model-guided design of the diversity of a synthetic human gut community

Abstract

Microbial communities have tremendous potential as therapeutics. However, a major bottleneck is manufacturing high-diversity microbial communities with desired species compositions. We develop a two-stage, model-guided framework to produce microbial communities with target species compositions. We apply this method to optimize the diversity of a synthetic human gut community. The first stage exploits media components to enable uniform growth responses of individual species and the second stage uses a design-test-learn cycle with initial species abundance as a control point to manipulate community composition. Our designed culture conditions yield 91% of the maximum possible diversity. Leveraging these data, we construct a dynamic ecological model to guide the design of lower-order communities with desired temporal properties over a longer timescale. In sum, a deeper understanding of how microbial community assembly responds to changes in environmental factors, initial species abundances, and inter-species interactions can enable the predictable design of community dynamics.