Thesis Dissertation Defense

Characterizing how viruses interact with microbial hosts is critical to understanding microbial community structure and function. However, existing methods for quantifying bacteria-phage interactions are not widely applicable to natural communities. First, many bacteria are not culturable, preventing direct experimental testing. Second, “-omics” based methods, while high in accuracy and specificity, have been shown to be extremely low in power. Third, inference methods based on time-series or co-occurrence data, while promising, have for the most part not been rigorously tested. This thesis work focuses on this final category of quantification strategies: inference methods.

In this thesis, we further our understanding of both the potential and limitations of several inference methods, focusing primarily on time-series data with high time resolution. We emphasize the quantification of efficacy by using time-series data from multi-strain bacteria-phage communities with known infection networks. We employ both in silico simulated bacteria-phage communities as well as an in vitro community experiment. We review existing correlation-based inference methods, extend theory and characterize tradeoffs for model-based inference which uses convex optimization, characterize pairwise interactions in a 5x5 virus-microbe community experiment using Markov chain Monte Carlo, and present analytic tools for microbiome time-series analysis when a dynamical model is unknown. Together, these chapters bridge gaps in existing literature in inference of ecological interactions from time-series data.