Models of evolutionary dynamics often treat the evolutionary parameters as fixed. Yet these parameters can themselves be modified by mutations, which are then acted on by natural selection, a phenomenon known as the evolution of evolvability. For example, mutator or antimutator alleles that increase or decrease mutation rates often spread through microbial populations, and mutations that open up or close off future adaptive trajectories can alter "adaptability". Despite extensive work, our understanding of the evolution of evolvability in microbial populations remains very limited. The basic problem is that in the large and mostly asexual populations characteristic of many microbes and viruses, evolution often acts in ways that challenge traditional models, because there is too much going on at once. Many mutations are often present simultaneously, and because recombination is limited, selection cannot act on each separately. Rather, mutations are constantly occurring in a variety of combinations linked together on physical chromosomes, and selection can only act on these combinations as a whole. These effects, known as interference selection, place enormous constraints on microbial evolution, and have made it difficult to analyze how natural selection acts on mutations that modify evolutionary parameters. The PI will address this question by using a combination of mathematical models and experimental evolution in budding yeast to analyze the evolution of evolvability in microbial populations where these interference selection effects are widespread. This project will produce extensive data describing fixed mutations, sequence diversity, and changes in fitness in sexual and asexual yeast populations, along with a corresponding library of strains, which will serve as a resource for the community. In the process, this work will provide broad interdisciplinary training in methods from evolutionary biology, molecular genetics, population genetics, and applied math for a postdoc and graduate and undergraduate students in the PI's lab. To help train the broader community of potential quantitative biologists in these diverse fields, the PI will develop outreach efforts to disseminate a new curriculum he has developed for introductory undergraduate biology, which provides an integrated introduction to biology, physics, chemistry, computer science, and mathematics. Finally, the PI will develop a new textbook providing an introduction to evolution and population genetics, aimed at mathematicians and physical scientists. This textbook will provide a mathematically sophisticated introduction to the field, without any biological prerequisites, based on materials the PI developed for an undergraduate course on this subject and on a summer course organized by the PI. The traveling wave framework described in this proposal is a key theoretical underpinning of much of this subject, and will play an important role in the book.

High-throughput experimental techniques and advances in sequencing technology now illustrate the pervasive importance of interference selection throughout microbial evolution. Motivated by this, much recent work has analyzed interference in large asexual populations. However, this work is limited to simple situations where the evolutionary parameters are fixed, and hence cannot explain the evolution of evolvability. In this project, the PI will address this question by combining novel theoretical methods inspired by statistical physics with direct observation of evolution in experimental populations of budding yeast. The PI will focus on analyzing alleles that modify mutation rates, adaptability, and robustness. The central goals are to predict (1) how interference selection affects the evolutionary dynamics and fates of these modifier alleles and hence the long-term evolution of evolvability in microbial populations, (2) how the modifiers of evolvability affect the dynamics of sequence diversity within the population, and (3) how recombination affects the evolution of evolvability.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Agency
National Science Foundation (NSF)
Institute
Division of Physics (PHY)
Application #
1914916
Program Officer
Krastan Blagoev
Project Start
Project End
Budget Start
2020-08-15
Budget End
2025-07-31
Support Year
Fiscal Year
2019
Total Cost
$159,554
Indirect Cost
Name
Harvard University
Department
Type
DUNS #
City
Cambridge
State
MA
Country
United States
Zip Code
02138