(r55) Barrick Lab

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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;" width=270 height=270  src="%PUBURL%/%WEB%/FrontPageImages/Muller_plot_mug.jpg"><br><div align=center><b>Evolving bacterial population simulation<br>[[http://cafepress.com/barricklab][&laquo;Support the Barrick lab coffee fund&raquo;]]</b></div></div><div style="text-align:justify">



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<img src="%PUBURL%/%WEB%/WebLeftBar/beacon_icon.png"> Read about our research in an article on the BEACON blog. [[http://beacon-center.org/blog/2011/09/12/beacon-researchers-at-work-lessons-in-bacterial-evolvability-from-eventual-winners/][Lessons in bacterial evolvability from eventual winners]] 
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News: The Barrick lab is involved in two projects funded by the [[http://beacon-center.org/][NSF BEACON Center for the Study of Evolution in Action]]. <b>Genomic mechanisms underlying increased virulence in <i>Campylobacter jejuni</i></b> with Titus Brown and Linda Mansfield at Michigan State University and <b>Contemporary evolution of cyanobacteria and viruses: implications for marine nutrient recycling</b> with Jay Lennon and Chris Klausmeier at the MSU Kellogg Biological Station. </td></tr></table>

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News: The Barrick Lab has completed its move to The University of Texas at Austin. Visit us in MBB 1.436. [[%WIKIHOMEURL%/%WEB%/ContactInformation][&laquo;Full Contact Information&raquo;]]
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[[%WIKIHOMEURL%/%WEB%/OldNews][Archived news items &raquo;]]

We are 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. Our 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, we are 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. 
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Barrick Lab > WebHome
Topic revision: r55 - 2011-10-11 - 17:08:01 - Main.CraigBarnhart