How to train your microbe: methods for dynamically characterizing gene networks

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dc.citation.firstpage113en_US
dc.citation.journalTitleCurrent Opinion in Microbiologyen_US
dc.citation.lastpage123en_US
dc.citation.volumeNumber24en_US
dc.contributor.authorCastillo-Hair, Sebastian M.en_US
dc.contributor.authorIgoshin, Oleg A.en_US
dc.contributor.authorTabor, Jeffrey J.en_US
dc.contributor.orgCenter for Theoretical Biological Physicsen_US
dc.date.accessioned2016-08-30T20:50:15Zen_US
dc.date.available2016-08-30T20:50:15Zen_US
dc.date.issued2015en_US
dc.description.abstractGene networks regulate biological processes dynamically. However, researchers have largely relied upon static perturbations, such as growth media variations and gene knockouts, to elucidate gene network structure and function. Thus, much of the regulation on the path from DNA to phenotype remains poorly understood. Recent studies have utilized improved genetic tools, hardware, and computational control strategies to generate precise temporal perturbations outside and inside of live cells. These experiments have, in turn, provided new insights into the organizing principles of biology. Here, we introduce the major classes of dynamical perturbations that can be used to study gene networks, and discuss technologies available for creating them in a wide range of microbial pathways.en_US
dc.identifier.citationCastillo-Hair, Sebastian M., Igoshin, Oleg A. and Tabor, Jeffrey J.. "How to train your microbe: methods for dynamically characterizing gene networks." <i>Current Opinion in Microbiology,</i> 24, (2015) Elsevier: 113-123. http://dx.doi.org/10.1016/j.mib.2015.01.008.en_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.mib.2015.01.008en_US
dc.identifier.urihttps://hdl.handle.net/1911/91368en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier.en_US
dc.titleHow to train your microbe: methods for dynamically characterizing gene networksen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpost-printen_US
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