Mechanistic models for predicting the dynamics of microbial communities

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Project summary Microbial communities within the human gut broadly and significantly affect host health. Engineering the dynamics of microbial communities is therefore a promising direction for new therapeutics. However, microbes within a community affect one another’s growth through a wide variety of mechanisms whose relative importance remain unclear, hindering the predictive capability of existing models for community dynamics. To address this knowledge gap, I propose experimental and mathematical modeling methods to disentangle and measure the strengths of the various interaction mechanisms. Key to my proposal is our lab’s powerful set of communities and microbial isolates derived from mice stool that have similar compositions in laboratory cultures as in the gut of gnotobiotic mice. They enable me to assemble and perturb the communities in lab cultures while mimicking behaviors relevant to host health. Guided by mathematical models that represent microbes as consumers and producers of environmental resources, as well as agents of other potential interaction mechanisms, I will assemble different combinations of the isolates and measure their growth properties to quantify their interaction mechanisms. For example, the amount of growth of one species in the medium spent by the growth of another species reflects the amount of overlap in the resources consumed by these two species. I will infer interaction mechanisms from two additional perspectives by quantifying environmental metabolites during growth of the communities, and investigating the statistics of fluctuations in species abundances over time in vivo. These three approaches will integrate high throughput experiments with mathematical modeling to systematically measure the importance of various interaction mechanisms, and generate a framework to do so for any microbial community. Together, the outcomes will ground species interactions mechanistically, empowering the engineering of microbial communities. My interdisciplinary proposal leverages my PhD training in physics, particularly statistical physics and the modeling of complex systems, and bacterial physiology. It will also train me in high-throughput phenotyping (next-generation sequencing and mass spectrometry metabolomics) of microbial communities, which will help me achieve my career goal to lead a laboratory that engineer microbial communities to benefit society. My sponsoring scientist Dr. Kerwyn Casey Huang in the Stanford Department of Bioengineering is an excellent mentor for the plan. His interdisciplinary lab bridges phenomena from single molecules to the multi-species scale using physical and biological techniques, and collaborates intimately with leading labs in microbiota research at Stanford. Thus, it is the ideal environment to pursue the ideas in my proposal. I will also actively engage undergraduate and graduate students in my proposed projects.