  An active area of research interest is the inference of ecological models of
complex microbial communities. Inferring such ecological models entails
understanding the interactions between microbes and how they affect each
other's growth. This dissertation employs a statistical perspective to
contribute further to the knowledge currently addressing this problem. Part I
explains how high-throughput droplet-based microfluidics technology can be used
to screen for microbial interactions. An explicit, statistical framework is
motivated and developed that can guide the analysis of data from such
experiments. Part II explains how it might be possible to predict, based on the
experimental setup, how much data will be produced to infer given microbial
interactions. Running the experiment once without incubating the droplets turns
out to be necessary to make such predictions. Part III demonstrates the
feasibility of inferring microbial interactions from the data produced by these
experiments. Relevant ideas from the microbiological and ecological literature
are recast into an explicit, statistical framework.
