Spring 2013 Seminar Schedule
2 May -- Nick Leiby (Harvard, Marx Lab) Metabolic Tradeoffs and Specialization in Escherichia coli Long-Term Evolution: Evolutionary adaptation to a new environment is often accompanied by specialization and tradeoffs, whereby there is reduced fitness in other environments. We studied strains of E. coli evolved in parallel for 50,000 generations in a stable glucose environment, a scenario where specialization and tradeoffs are expected. However, direct measurements of growth rate contradict this expectation. We found that tradeoffs were neither as widespread nor as parallel as previously reported, and surprisingly, there are approximately as many performance increases as decreases on non-glucose substrates. I will discuss these data and their implications, as well as my ongoing project designed to specifically address the evolutionary canalization of metabolic function.
16 May -- Kevin Esvelt (Wyss Institute)
30 May -- Tami Lieberman (HMS, Kishony Lab)
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18 April -- Julien Ayroles (Harvard) - Using Experimental Evolution to Study the Temporal Dynamics of Allele Frequency Change Over Time: For nearly a century, experimental evolution has been a favorite tool of biologists seeking to test various evolutionary models. This approach can provide an unprecedented understanding of the dynamics of evolutionary change. What could we learn if one could track changes over the entire genome? We sequenced DNA pools from lines of flies selected for starvation resistance and compared them to control lines every generation. After only 15 generations of selection, we observed a strong phenotypic response associated with changes in allele frequencies in the selected lines. I will discuss how such data can be used to describe the temporal dynamics of allele frequency change. I will also contrast the complex genetic architecture of starvation resistance in the unselected population to the surprisingly simple genetic architecture associated with lines selected for increase starvation resistance.
11 April -- John Barton (MIT, Chakraborty Lab) - From HIV sequence data to fitness landscapes: Development of an effective vaccine against HIV, the greatest hope for controlling the epidemic, has been hindered by the extreme mutability of the virus. To identify regions of vulnerability in the viral proteome, we have constructed a computational model to infer fitness landscapes for HIV proteins from publicly available sequence data. I will discuss experimental tests of these proposed fitness landscapes, and early applications of this technique in understanding intra-host viral evolution.
28 March -- Anna Selmecki (HMS) - How polyploidy and aneuploidy impact the speed of adaptation: Polyploidy, having more than a diploid set of chromosomes, is frequently found in nature and has played an important role in the evolution of new species. Theoretical work indicates that ploidy will affect adaptation, and experimental work in haploids and diploids has supported much of this theory, but little is known about the rate and molecular basis of polyploid adaptation. We have compared the evolutionary trajectories of isogenic haploid, diploid, and tetraploid yeast and found that tetraploids adapted more rapidly than lower ploidy cells. Our whole genome analyses indicate that the adaptation was driven by an increase in acquired mutations, polymorphisms and chromosome level changes.
14 March -- Daniel Rice (Harvard, Desai Lab) Sequence-level dynamics of adapting yeast populations: The dynamics of adaptation determines which mutations fix in a population, and hence how reproducible evolution will be. High time resolution sequence data can shed light on these dynamics. I will present whole-genome whole-population sequence data of 40 replicate laboratory yeast populations. I will discuss how patterns of sequence evolution are driven by a balance between chance hitchhiking and interference on one hand, which increase stochastic variation in evolutionary outcomes, and the deterministic action of selection on individual mutations, which favors parallel evolutionary solutions in replicate populations.