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dc.contributor.advisorMark Bathe.en_US
dc.contributor.authorDey, Arkajiten_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2011-10-17T19:48:54Z
dc.date.available2011-10-17T19:48:54Z
dc.date.copyright2011en_US
dc.date.issued2011en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/66307
dc.descriptionThesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 71-73).en_US
dc.description.abstractHow do proteins, vesicles, or other particles within a cell move? Do they diffuse randomly or ow in a particular direction? Understanding how subcellular particles move in a cell will reveal fundamental principles of cell biology and biochemistry, and is a necessary prerequisite to synthetically engineering such processes. We investigate the application of several variants of hidden Markov models (HMMs) to analyzing the trajectories of such particles. And we compare the performance of our proposed algorithms with traditional approaches that involve fitting a mean square displacement (MSD) curve calculated from the particle trajectories. Our HMM algorithms are shown to be more accurate than existing MSD algorithms for heterogeneous trajectories which switch between multiple phases of motion.en_US
dc.description.statementofresponsibilityby Arkajit Dey.en_US
dc.format.extent73 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.titleHidden Markov model analysis of subcellular particle trajectoriesen_US
dc.typeThesisen_US
dc.description.degreeM.Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc755091154en_US


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