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David K. Gifford. Reeder, Christopher Campbell Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2009-06-30T16:29:07Z 2009-06-30T16:29:07Z 2008 2008 http://hdl.handle.net/1721.1/45869 320092527 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008. Includes bibliographical references (p. 75-77). We 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]. by Christopher Campbell Reeder. S.M. 77 p. eng Massachusetts Institute of Technology M.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. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. A novel computational method for inferring dynamic genetic regulatory trajectories Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="98a1a205-477a-4c7c-b7a5-7e2bd46957bc"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>A novel computational method for inferring dynamic genetic regulatory trajectories</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2008</PublicationDate> <Authors> <Author> <DisplayName>Reeder, Christopher Campbell</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>We 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].</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>