Coarse-grained model of serial dilution dynamics in synthetic human gut microbiome

Many microbial communities in nature are complex, with hundreds of coexisting strains and the resources they consume. We currently lack the ability to assemble and manipulate such communities in a predictable manner in the lab. Here, we take a first step in this direction by introducing and studying a simplified consumer resource model of such complex communities in serial dilution experiments. The main assumption of our model is that during the growth phase of the cycle, strains share resources and produce metabolic byproducts in proportion to their average abundances and strain-specific consumption/production fluxes. We fit the model to describe serial dilution experiments in hCom2, a defined synthetic human gut microbiome with a steady-state diversity of 63 species growing on a rich media, using consumption and production fluxes inferred from metabolomics experiments. The model predicts serial dilution dynamics reasonably well, with a correlation coefficient between predicted and observed strain abundances as high as 0.8. We applied our model to: (i) calculate steady-state abundances of leave-one-out communities and use these results to infer the interaction network between strains; (ii) explore direct and indirect interactions between strains and resources by increasing concentrations of individual resources and monitoring changes in strain abundances; (iii) construct a resource supplementation protocol to maximally equalize steady-state strain abundances.Author summary: Complex microbial communities, such as those in the human gut, are diverse ecosystems made up of hundreds of coexisting microbial strains that grow on a variety of nutrients. These communities often exist in environments characterized by “boom-and-bust” cycles, where nutrients are supplied in large batches before microbes undergo dilution or die-off. Traditional consumer-resource models struggle to capture the assembly dynamics of these complex communities, where interactions like resource competition and cross-feeding play a significant role. In our study, we addressed these challenges by developing a simplified consumer-resource model, which we tested on a synthetic human gut community (hCom2) containing 63 microbial species in serial dilution experiments. Using this model, we accurately predicted microbial population dynamics based on nutrient consumption and production data derived from metabolomics experiments. This approach enabled us to investigate how individual strains interact, assess the community’s response to nutrient changes, and identify ways to balance species abundances by adjusting nutrient levels. Our model presents a valuable tool for understanding and potentially managing complex microbial communities.