Microbial communities as predictors of outcomes in industrial and clinical applications

Abstract

Environmental and host associated microbial communities provide an extensive reservoir of genetic and functional diversity. This diversity represents a wealth of potential for applications in many fields. To harness this potential for engineering applications, the impact of temporal dynamics need to be better understood. Yet most of the data we have are in the form of static surveys of diversity. In this thesis, I use 16S rRNA sequencing analysis to measure community composition across time series to predict outcomes for three applications: bioreactor function; a non-invasive diagnostic of endometriosis; and commercial chicken rearing. I identify bacteria that exhibit distinct temporal dynamics within each application, and discuss the implications of those dynamics in the context of each application. Despite the diverse communities covered in this work, temporal dynamics emerge as a common theme that can impact these engineering applications which rely on stable and predictable community performance.