I am a postdoc in the Desai Lab at the Department of Organismic and Evolutionary Biology at Harvard University. I am trying to uncover regularities in how microorganisms adapt to their environment and understand how these regularities emerge from the biology of the cell.
I use experiments in budding yeast, theory, and data from natural populations to address the following questions:
I am currently working on three main projects.
Microbes rapidly adapt to their environment, allowing them to escape the immune system, resist drugs, and undermine performance of synthetically engineered circuits. Predicting evolutionary trajectories is therefore a major problem in biology, with wide-ranging practical implications. Several recent studies (e.g., Burch and Chao 2000, Blount et al 2008, Bloom et al 2010, Woods et al 2011, Hayden et al 2011) have shown that epistatic interactions among mutations (i.e., when the effect of a mutation depends on the presence of other mutations in the genome) dramatically affect the course of adaptation implying that evolution may be essentially unpredictable. On the other hand, evolutionary outcomes may be statistically predictable if mutations leading to extreme and irregular changes in adaptability (also called evolvability) are rare, while mutations leading to small and regular changes in adaptability are common. We directly tested this hypothesis by measuring the variation in adaptability between related genotypes in laboratory yeast populations.
The paper describing these results is now published in Science. An open access version will be posted soon. An earlier version is available on bioRxiv here. This is work with Dan Rice, Elizabeth Jerison, and Michael Desai.
Phenotypic effects of mutations often depend on the presence of other mutations in the genome (this dependence is termed epistasis). While we naturally expect to observe epistasis between some classes of mutations (e.g., between mutations that affect the same protein), the genome-wide patterns of epistasis are far less clear. In fact, there is a lot of confusion in the literature whether observing epistasis between two mutations is something that we should be surprised about at all.
The figure above is from the classic paper by Daniel Dykhuizen, Tony Dean and Dan Hartl called "Metabolic flux and fitness" where the authors lay out the foundation for understanding microbial fitness from biochemical principles. Building on their ideas as well as on bacterial proteome partitioning laws recently discovered by Terry Hwa's group, I formulated a basic coarse-grained theory of epistasis that makes qualitative predictions for the types of epistasis that one should expect to find between mutations affecting various biological processes. My theory predicts that even mutations that do not exhibit epistasis at lower levels of biological organization will likely exhibit epistasis at higher levels of organization. For example, two mutation that affect two different enzymes in the same pathaway would exhibit no genetic interaction at the level of enzyme activities, but they would exhibit epistasis at the level of metabolic flux through the pathway. Second, epistasis that arise at lower levels of biological organization (e.g., at the level of flux through a pathway) will propgate to higher levels of biological organization (e.g., fitness). Thus, we expect to observe more and more epistasis at higher and higher levels of biological organization.
When applied more specifically to exponentially growing cells, my model predicts that positive epistasis should be common among mutations that affect different biological processes, for example, between a mutation that affects amino acid synthesis and a mutation that affects protein synthesis. This model also offers a potential qualitative explanation for the global diminishing returns epistasis that we observed among beneficial mutations in our evolution experiment described above. I am currently writing up these results in a paper that I titled "Metabolic flux and epistasis" as tribute to Dykhuizen, Dean and Hartl's great work.
To be updated soon.