Project Summary
Mutations are the ultimate source of genetic diversity upon which other evolutionary forces—natural se-
lection, genetic drift, recombination—act. As such, mutations impose fundamental constraints on what
phenotypes can evolve, but we lack a theory for understanding these constraints. Two overarching goals
of my lab’s research are to (i) develop a theory for predicting the phenotypic and fitness effects of new
mutations across genotypes and environments and (ii) understand how evolution proceeds on the emerging
“fitness landscapes”. We take three complementary approaches to achieve these goals. Our first approach
is theoretical. Even the simplest unicellular organisms are complex biochemical machines, but it is unclear
how this biochemical architecture constrains the structure of the resulting fitness landscapes and the rules
by which mutations “move” the organism on this landscape. We are developing mathematical theory for
understanding these constraints, focusing initially on fitness landscapes that emerge in complex metabolic
networks. Our second approach is empirical. While we construct the theory from first principles, we will
also use tractable experimental systems, such as the yeast Saccharomyces cerevisiae and the bacterium
Escherichia coli to directly measure the effects of mutations on some cellular phenotypes that are im-
portant for microbial fitness, e.g., the transcriptome. Our goal here is to identify broad and hopefully
general statistical patterns in how the effects of mutations vary across genetic backgrounds and environ-
ments that will help us narrow down and parameterize our theory. One practically important outcome of
this work that we hope to achieve is the ability to predict the evolution of collateral antibiotic resistance.
Our third approach is exploratory. If our theoretical and empirical efforts mentioned above are successful,
they will give us a handle on predicting evolutionary trajectories and outcomes in controlled laboratory
conditions. However, by necessity, these conditions lack many of the complexities of natural environ-
ments. We would like to understand how the evolutionary process in such more complex environments
differs from what we can observe and potentially predict in simple controlled laboratory conditions. To
this end, we will apply some of the cutting-edge experimental tools, such as whole-genome time-course
sequencing, to observe evolutionary dynamics in microbial systems of intermediate complexity, in partic-
ular, in a two-species microbial community that we already developed and in a semi-natural marine mi-
crobial system that we plan to develop with our collaborators.
