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dc.contributor.advisorDavid K. Gifford.en_US
dc.contributor.authorReeder, Christopher Campbellen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2009-06-30T16:29:07Z
dc.date.available2009-06-30T16:29:07Z
dc.date.copyright2008en_US
dc.date.issued2008en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/45869
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.en_US
dc.descriptionIncludes bibliographical references (p. 75-77).en_US
dc.description.abstractWe present a novel method called Time Series Affinity Propagation (TSAP) for inferring regulatory states and trajectories from time series genomic data. This method builds on the Affinity Propagation method of Frey and Dueck [10]. TSAP incorporates temporal constraints to more accurately model the dynamic nature of underlying biological mechanisms. We first apply TSAP to synthetic data and demonstrate its ability to recover underlying structure that is obscured by noise. We then apply TSAP to real data and demonstrate that it provides insight into the relationship between gene expression and histone posttranslational modifications during motor neuron development. In particular, the trajectories taken by the Hox genes through the space of regulatory states are characterized. Understanding the dynamics of Hox regulation is important because the Hox genes play a fundamental role in the establishment of motor neuron sub-type identity during development [6].en_US
dc.description.statementofresponsibilityby Christopher Campbell Reeder.en_US
dc.format.extent77 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleA novel computational method for inferring dynamic genetic regulatory trajectoriesen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc320092527en_US


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