UC San Diego

Evolution underlies the entirety of Earth’s biodiversity, as all species diverged from the last universal common ancestor billions of years ago. Although able to effect incredible changes over long periods, the need for multiple generations of mutation and competition renders evolution nearly imperceptible, at the timescale of human observation, for all but the most quickly reproducing organisms. Thus microbial adaptation, given microbes’ rapid generation time and enormous population sizes, is perhaps most pressing to understand. This unavoidable evolutionary process facilitates the rise and spread of antibiotic resistance, and frequently countervails attempts to genetically engineer organisms for human purposes.

The bacterium Escherichia coli, easily the most highly studied microbe to date, is an ideal model by which to investigate evolution. With both clinical and biotechnological relevance, a thorough understanding of the adaptive principles governing E. coli evolution is of great importance. In this dissertation I seek to probe the adaptive capabilities of E. coli using custom robotics systems that function as ‘evolution machines.’ Enabled by this automation, adaptive walks along the fitness landscape can be tracked in real-time with experimental throughput, data quality, and environmental control impossible to replicate manually.

I subject E. coli to stressful perturbations and analyze the mechanisms by which it evolves to restore robust growth, using data types such as phenotypic characterization, whole genome sequencing, and transcriptomics. I demonstrate the remarkable adaptive flexibility of E. coli as it readily evolves to tolerate elevated temperatures, altered isotopic composition, rapidly fluctuating growth environments, and even replacement of important native genes with foreign DNA. Overall, these studies establish condition-specific evolutionary responses, general mechanisms for growth rate improvement, and guiding principles for the successful use of laboratory evolution experiments as a tool for biological discovery and engineering.