Simplification and control of microbial ecosystems in theory and experiment
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.