How fast do microbes adapt? While this question has been explored in well-mixed populations with great effort and quantitative success, there is a large gap between theory and experiments for natural populations exhibiting spatial structure. This leaves us at a serious loss in understanding the evolutionary response of, e.g., microbial colonies or biofilms, and makes it impossible to predict the pace of drug-resistance evolution. The P.I. has demonstrated in his past research that theory and experiments can be joined effectively to dissect the evolutionary processes induced by spatial structure. This effprt puts this work onto a new level by the use of innovative experimental designs such as engineered microbial systems to track the spontaneous emergence of selectable mutant clones, and theoretical concepts that allow dealing with fluctuations and eco-evolutionary feedbacks exactly. The results of this project will advance our understanding of spatio-temporal aspects of adaptation, and elucidate specifically how populations respond to environmental deterioration, which is key to efforts ranging from the rational design of strategies to conserve species in the face of environmental change to mitigating drug resistance evolution. The data set generated by this experimental research, combined with novel theory and simulations, will allow the community to reassess established paradigms about adaptation of microbial populations. The research will invite novel interdisciplinary activities in the quest to understand, predict and control adaptation of spatially-structured microbial populations. The interdisciplinary research is closely orchestrated with broad educational efforts designed to promote crossing of traditional disciplinary boundaries to achieve new ways of thinking about evolutionary processes, such as drug resistance evolution, that transcend the limitations of the standard mean-field theories of evolution. At the graduate and undergraduate level, the research will be accompanied by a new interdisciplinary course, Statistical Biophysics of Cells and Populations, that the PI offers to students of physics, chemistry, biology and mathematics. In addition, the PI has established a firm collaboration with the Lawrence Hall of Science at Berkeley to prepare novel educational resources for high-school students and undergraduates to explore the ubiquity and evolutionary potential of microbial populations and to establish the crucial link between ecology and evolution. These resources, which include a learning module and hands-on activities, will be documented in write-ups and made freely available online through existing NSF-funded cyber-infrastructure platforms.
Demography and evolution are tightly intertwined. The distribution of individuals in space determines the influx of new mutations, the strength of genetic drift and the competition between genotypes. Evolution on the other hand, influences the sizes and densities populations can attain, how individuals migrate and interact with each other and the environment and how they reproduce and die. The feedback between population dynamics and evolution is absent in standard models of evolving well-mixed populations. Yet, it constrains the pace of adaptation, the predictability of evolutionary outcomes and the evolutionary response of spatially-structured populations. The objective of this project is to fill this gap by quantifying the associated eco-evolutionary feedback in microbial populations and its consequences for adaptation. To this end, the P.I. proposes tightly-controlled microbial evolution experiments to quantify how population dynamics alters patterns of molecular evolution, and new population genetics theory that bridges the gap in spatio-temporal scales between laboratory experiments and natural populations. The specific aims are: 1. Determine how the stochastic dynamics of how cells generate population-level patterns of genetic drift and selection and how these patterns control the fate of beneficial mutations. Generalize well-established theories of evolution in well-mixed populations to capture the eco-evolutionary feedback dynamics of the experiments. 2. Determine how spatially-structured populations adapt to environmental challenges (i) via pre-existing mutations and (ii) via the accumulation of new mutations. 3. Establish a new course, a workshop on unintentional biases and undergraduate mentoring to increase diversity in the STEM fields at UC Berkeley.
This project is being jointly supported by the Physics of Living Systems program in the Division of Physics and the Cellular Cluster and the Systems and Synthetic Biology clusters in the Division of Molecular and Cellular Biosciences.
