Theses and Dissertations Collection

Recent advances in DNA sequencing have led to a boom in microbiome analyses to determinecommunity composition. Fitting such community composition data to mathematical models allows for the estimation of interspecies interactions within a microbial community. In this thesis, we explore the extent to which noise inherent to time-series microbiome data interferes with the inference of interspecies interactions. We first create a small synthetic test community with structure mimicking real microbial communities based on the generalized Lotka-Volterra (gLV) model, incorporating di↵ering levels of two types of noise, process and measurement noise. We then establish a method of parameter estimation for both the gLV and multivariate autoregressive (MAR) models, and apply the method to our synthetic dataset with varying levels of noise. We find that interspecies interactions can be well estimated even with moderate levels of process noise, but even modest amounts of measurement noise lead to poor estimates of interactions.