(r68) Barrick Lab :: UT Austin

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<table width'"100%"><tr><td><div style="float: right; margin:10px;"><a href="%WIKIHOMEURL%/%WEB%/FormerFrontPageImages"><img style="border-color: black;  border-style: solid; border-width: 3px;" width=270 height=270  src="%PUBURL%/%WEB%/FrontPageImages/Grid_Plate_Small.png"></a><br><div align=center><b>Tangled universe of microbial evolution<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/alife13.png"> *July 2012:* Work by programmer Aaron Reba and Ph.D student Austin Meyer receives best paper award in the [[http://alife13.org/][ALife 13]] Synthetic Biology track: [[http://mitpress.mit.edu/books/chapters/Alife13/ch062.asp]["Computational tests of a thermal cycling strategy to isolate more complex functional nucleic acid motifs from random sequence pools by in vitro selection"]] 
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<img src="%PUBURL%/%WEB%/WebLeftBar/oww_icon.png"> *May 2012:* Dr. Barrick and Dr. Hunicke-Smith with Geoff Colburn, Aaron Reba, Vinicio Reynoso, Anna Battenhouse, and Daechan Park teach [[https://wikis.utexas.edu/display/bioiteam/SSC+Intro+to+NGS+Bioinformatics+Course][Intro to Next-Gen Sequencing Bioinformatics Course]] as part of the [[http://ssc.utexas.edu/programs/summer-statistics-institute/courses][Summer Statistics Institute]]. [[https://wikis.utexas.edu/display/bioiteam/SSC+Intro+to+NGS+Bioinformatics+Course][Visit the class Wiki for tutorials and information.]]
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%ICON{changes}% *May 2012:* Graduate students Neil Gottel, Vinicio Reynoso, and Alvaro Rodriguez join the lab.
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%ICON{choice-yes}% *April 2012:* Graduate students Mike Hammerling and Brian Renda pass Part I qualifying exams.
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<img src="%PUBURL%/%WEB%/WebLeftBar/welch_foundation.png"> *April 2012:* [[http://www.welch1.org/][Welch Foundation]] funds 3-year research project: "Discovering functional nucleic acid families by deep sequencing and fold sampling"
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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
Contributors to this topic
JeffreyBarrick, PeterThoeny, MichaelHammerling, CraigBarnhart, TWikiContributor, AustinMeyer, GabrielSuarez, AurkoDasgupta, BrianRenda, AlvaroRodriguez, IsaacGifford, KateElston
Topic revision: r68 - 2012-07-24 - 22:24:43 - Main.JeffreyBarrick