Research Interests

Overview

I am broadly interested in understanding evolution as a creative force. My laboratory will use experiments with populations of bacteria, biomolecules, and digital organisms to study the fundamental constraints and opportunities common to evolving systems. We will formulate and test new biological organizing principles from a systems perspective that integrates ecology, population genetics, genomics, molecular biology, biochemistry, and computer science. Ultimately, I will apply these principles to study microbial populations and genome architectures in the wild and in clinical settings, and to manipulate evolution to engineer solutions to medical and biotechnological challenges.

Research Proposal Summaries

1. Identifying mutations that promote microbial evolvability «PDF»
2. Experimental evolution of bacterial restraint by selecting for growth yield «PDF»
3. Deep sequencing of in vitro nucleic acid selection experiments «PDF»

Identifying Mutations that Promote Microbial Evolvability

Evolvability is the capacity of an organism to generate descendants with increased fitness. Evolvability is a complex trait. It depends not only on how developmental and regulatory processes render underlying genetic changes into phenotypic variation, but also on the dynamics of how beneficial mutations arise and compete within a population. Mutator strains of microorganisms, which have elevated genomic mutation rates due to defects in DNA proofreading or repair pathways, can sometimes take over chronic pathogen populations because they promote evolvability. However, little is currently known about how mutations affecting other cellular processes impact microbial evolvability or about how evolvability varies at each step in a typical adaptive trajectory.

With ongoing support from an NIH Pathway to Independence grant, my lab will continue projects initiated during my postdoc that aim to identify new classes of mutations that promote bacterial evolvability. I am currently reconstructing the order of fixation of hundreds of mutations in several lineages from a 20-year long-term E. coli evolution experiment. The tempo of genomic evolution may reveal genetic changes that were not immediately beneficial, but were ultimately successful because of their effects on further evolvability. My laboratory will dissect the biochemical and evolutionary effects of these adaptations and seek a systems level understanding of bacterial fitness landscapes.

Another specific aim of this work is to use E. coli gene deletion and overexpression libraries to recover strains carrying genetic changes that make them more evolvable in many different environments. When mixtures of these strains are propagated in long-term experiments for many generations, more evolvable strains are expected to reproducibly increase in frequency at later times, after beneficial mutations begin to sweep to fixation in a given population. I have recently submitted a manuscript that demonstrates a method for analyzing neutral genetic marker divergence trajectories to quantify bacterial evolvability on multiple timescales. That study establishes important null expectations for these experiments and a technique that will allow us to directly test whether mutations found by these functional genomic screens increase evolvability. With these projects, I eventually hope to find ways to engineer more evolvable bacterial strains for biotechnology applications and to better understand the overall prevalence and importance of mutations that increase evolvability in natural microbial populations.

Experimental Microbial and Molecular Population Genomics

Unprecedented quantities of DNA sequence information can be collected using next-generation technologies. With deep enough sequencing of all genomes in an evolving population, every mutation is potentially its own marker, allowing the identities and fitness effects of many competing beneficial mutations to be determined in parallel from a time-course of allele frequencies. With this information it will be possible to identify rare mutations that transiently accumulate in a population until an alternate mutational path becomes dominant, to follow the ebb and flow of standing genetic variation as mutations arise and selective sweeps purge diversity, and to identify lineages whose over-specialization in an ecological niche destines them for extinction.

I have developed computation tools during my postdoc for discovering mutations in next-generation whole-genome re-sequencing data from individual genomes and for quantifying allele frequencies in mixed-population samples. My laboratory will use these approaches and develop new ones to follow the dynamics of genome evolution in experimental microbial populations at a new level of resolution. Our initial goals will be to understand the relative importance of clonal interference between asexual lineages, epistatic interactions between mutations, and mechanisms generating genetic diversity in determining population dynamics and limiting the actualized rate of adaptation. We know very little about evolution in natural microbial populations and how it relates to their ecology, and I am also extremely interested in applying similar approaches to the increasing number of metagenomic datasets available from different environments.

Building on my graduate and undergraduate experiences with in vitro protein and nucleic acid selection, I also intend to begin research projects that use ultra-deep sequencing to study these experimental populations of biomolecules. One question that has yet to be addressed is if next-generation technologies can be used to identify extremely rare functional sequences in these populations, essentially using brute force to overcome the "tragedy of the commons", where folds that are common in sequence space are the only ones that can be recovered from these selection experiments. Many random sequences only fold into functional conformations some small fraction of the time. Therefore, I am interested in testing if having each sequence sample multiple conformations by thermocycling within each selection cycle will also increase the effective library size and allow new families of functional sequences to be discovered.

Evolution Experiments with Digital Organisms

Populations of self-replicating computer programs mutate and compete for CPU cycles in the Avida artificial life system. This environment for digital organisms has been used to ask fundamental questions that transcend the substrate that is evolving. The speed of digital evolution experiments, transparency of the underlying mechanics, and ability to manipulate and record every aspect of the evolutionary process make it possible to more readily explore cause and effect relationships in Avida than in natural systems. I have extensive experience with Avida that is not yet reflected in my publication record. My lab will use Avida to complement studies in biological systems: both as a way of more clearly illustrating and exhaustively characterizing phenomena of interest and as a source of new candidate laws and insights to motivate new lines of experimental inquiry.

Collaboration Interests

In addition to these specific plans for future research, I have long-standing interests in RNA biochemistry, riboswitches and other noncoding RNAs in bacteria, comparative microbial genomics, and in using group selection to evolve cooperating microbial consortia that optimize yield over growth rate. I hope to be a resource to colleagues who are interested in adding an evolutionary, informatics, or genomics dimension to their studies. Whenever possible, I would like to investigate my basic research questions about evolution in systems where there is additional interest in the underlying molecules, microbes, and mutations. I am eager to link up with researchers who have relevant problems in medicine and biotechnology to find these opportunities and anticipate that this kind of project will become an important component of my research.