UC Santa Barbara

Over the last two decades an association between microbiome composition and some human diseases has been unambiguously established, and this discovery has provoked clinical interest into microbiome-based medical therapies. The advent of high-throughput sequencing has produced vast amounts of microbial abundance data, but mechanistic models that describe and predict this complex microbial ecosystem are not yet established.

In my doctoral research I employ novel theoretical approaches and tractable experimental systems to study simplified instances of the complex microbial dynamics of the microbiome. This research is motivated by a desire to inform the mechanism of action and development of microbiome-based bacteriotherapies. We use generalized Lotka-Volterra (gLV) models, a class of model that exhibits prototypical ecological behaviors, as a theoretical proxy for true microbial dynamics. We develop a numerical framework to predict how introduced foreign microbes (direct bacteriotherapy) and a modified microbiome environment (indirect bacteriotherapy) affect the composition of a microbiome. Additionally, we derive the dimensionality-reduction technique "steady-state reduction" (SSR), which compresses bistable dynamics in a high-dimensional gLV system into a reduced two-dimensional system. The insights gained from this reduced system inform the microbial dynamics of high-dimensional gLV systems, and therefore contribute to our knowledge of how bacteriotherapies function.

Additionally, in experimental work I construct simple models from first principles that describe how the measured physiological traits depend on microbiome composition in the fruit fly Drosophila melanogaster. The fruit fly microbiome naturally hosts only a few species of commensal bacteria. In our research we cultivate a core group of five commensal bacteria, and associate each possible combination of the five bacteria with a set of germ-free flies. Since the flies were identically reared, the flies associated with each of these 32 bacterial combinations carry a distinct microbiome-affiliated phenotype (e.g. lifespan or fecundity) that is a function of the complex interactions in the microbiome. We found that the complexity of these fly phenotypes was often reducible: we could approximate the phenotype of flies associated with more than one bacterial species by averaging the phenotypes of the flies with the corresponding single-species associations. Thus, we found that simple models can describe complex behaviors in the fly microbiome.