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Hidden Markov Model Analysis of Subcellular Particle Trajectories by Arkajit

How do proteins, vesicles, or other particles within a cell move? Do they diffuse randomly or flow 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. Thesis Supervisor: Mark Bathe Title: Assistant Professor