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<table width'"100%"><tr><td><div style="float: right; margin:10px;"><img style="border-color: black;  border-style: solid; border-width: 3px; height=350 width=266"  src="%PUBURL%/%WEB%/FrontPageImages/Marker_Divergence.png"><br><div align=center><b>Quantifying Bacterial Evolvability</b></div></div><div style="text-align:justify">

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News: Two undergraduates I mentor win first-place prizes at the 2010 [[http://urca.msu.edu/news/archive/2010/04/21/uuraf-award-winners][UURAF]] for their posters! Congratulations to *James Dittmar* for _Mutator Alleles May Still Fix Late in Evolution_ and *Mark Kauth* for _Evolution of E. coli in Changing Environments_. 
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Presentation: <b>"Re-sequencing hundreds of <i>E. coli</i> genomes: finding non-SNP mutations, analyzing mixed populations, and knowing what you don't know"</b> given at MSU for the [[http://www.bch.msu.edu/GEDD/][GEDD]] next-gen sequencing seminar series on 2010-03-19. [[%PUBURL%/%WEB%/PresentationList/2010-03-17_MSU_GEDD_Next_Gen.pdf][%ICON{pdf}%]] 
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I am broadly interested in understanding evolution as a creative force. 

How does new information about the environment become fixed in genomes over evolutionary time? How do different kinds of mutations affect the ability to further adapt? What limits the speed of adaptation in a given population? What does the organization of information in a genome tell us about its past? Can we map fitness landscapes and chart what evolutionary trajectories are likely to be realized? How are microbial genomes evolving in the wild? Can we harness evolutionary processes to generate organisms or molecules that do useful tasks? Can we anticipate and frustrate the unfortunate evolution of microbial pathogens and cancers? Can we predict what mutations will sabotage the ingenious man-made contraptions of synthetic biology?

Answering these questions requires a systems level knowledge that spans the disciplines of evolutionary biology, microbiology, molecular biology, biochemistry, and computer science. My approach is to use several model systems that allow one to manipulate the evolutionary process and follow the dynamics of evolution in real time: (1) experiments with digital organisms (Avida), (2) in vitro selection and evolution experiments with functional nucleic acids (DNA and RNA), and (3) experiments with bacteria (_E. coli_). There is an element of chance in evolution, and these systems allow one to compare many replicate experiments to quantitatively understand the diversity of possible outcomes. They also allow perfect control of the environment, so that the influences of various factors on evolutionary paths and the biochemical roles of individual mutations can be unambiguously teased apart. 

Currently, I am reconstructing the fine-scale dynamics of mutations within _E. coli_ populations from a 20-year evolution experiment using next-generation DNA sequencing and high-throughput genotyping methods, modeling these dynamics to understand the evolutionary forces at work, and searching for examples where bacteria with lower fitness than other contending lineages in the same population eventually prevail because they have greater evolutionary potential. 

_I am a postdoctoral fellow in the laboratories of [[http://www.msu.edu/~lenski/][Richard Lenski]] and [[http://www.cse.msu.edu/~ofria/][Charles Ofria]] at [[http://www.msu.edu][Michigan State University]]._
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This topic: Lab > WebHome
Topic revision: r38 - 2010-04-21 - JeffreyBarrick