Hidden Markov model analysis of subcellular particle trajectories
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2011
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| Online Access: | http://hdl.handle.net/1721.1/66307 |
| _version_ | 1826196401110908928 |
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| author | Dey, Arkajit |
| author2 | Mark Bathe. |
| author_facet | Mark Bathe. Dey, Arkajit |
| author_sort | Dey, Arkajit |
| collection | MIT |
| description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. |
| first_indexed | 2024-09-23T10:26:15Z |
| format | Thesis |
| id | mit-1721.1/66307 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T10:26:15Z |
| publishDate | 2011 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/663072019-04-12T14:55:51Z Hidden Markov model analysis of subcellular particle trajectories Dey, Arkajit Mark Bathe. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student submitted PDF version of thesis. Includes bibliographical references (p. 71-73). How 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. by Arkajit Dey. M.Eng. 2011-10-17T19:48:54Z 2011-10-17T19:48:54Z 2011 2011 Thesis http://hdl.handle.net/1721.1/66307 755091154 eng 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 73 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Dey, Arkajit Hidden Markov model analysis of subcellular particle trajectories |
| title | Hidden Markov model analysis of subcellular particle trajectories |
| title_full | Hidden Markov model analysis of subcellular particle trajectories |
| title_fullStr | Hidden Markov model analysis of subcellular particle trajectories |
| title_full_unstemmed | Hidden Markov model analysis of subcellular particle trajectories |
| title_short | Hidden Markov model analysis of subcellular particle trajectories |
| title_sort | hidden markov model analysis of subcellular particle trajectories |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/66307 |
| work_keys_str_mv | AT deyarkajit hiddenmarkovmodelanalysisofsubcellularparticletrajectories |