Evolutionary Consequences of Metabolic Competition and Cooperation in Microbial Communities

Abstract

Microbial communities are ubiquitous and perform functions fundamental to life on earth. The high density of organisms within them and the many ways in which these species interact with each other result in limitless opportunities for competition and cooperation to affect the fitness of individual species. Gaining a better understanding of the evolutionary process within such dense communities has important implications for both clarifying genomic variation observed in the present as well as predicting future changes in microbial communities of particular interest to human health and industry. Coevolution is challenging to study due to the high dimensionality of mutations interacting with ecological dynamics which further affect the fitness of future mutations arising in the same and other species. Previous studies of coevolution tend to use in vivo systems with limited scope or abstracted interactions and adaptations in theoretical or in silico systems to achieve broader scope. In this study I focus on one type of bacterial interaction, competition and cooperation over metabolic resources, and use a computational model that captures mechanistic detail of the genomic and metabolic basis of such interactions and yet is amenable to studying large scales of replicates and conditions. In chapter 1, I describe the behaviors and mechanisms through which bacteria interact, the current state of knowledge of coevolution, and the computational methods that have been applied to microbial ecology and evolution. In chapter 2, I consider the evolutionary dynamics that occur within the tightly coupled, reductive evolution regime of insect endosymbionts. Using a modeling approach, I simulate thousands of replicate evolutionary trajectories and identify relationships between changes to genome content and cooperative metabolic phenotypes. In chapter 3, I apply a similar modeling approach to study how the fitness landscape of a focal species changes depending on what other species live in the same community. By simulating over one thousand mutations and hundreds of community compositions I identify complex and prevalent relationships between mutations and community contexts. Finally, in chapter 4 I reflect on commonalities in the findings of these studies, future directions for similar research, and potential applications to synthetic biology.