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|a Park, Hye Yoon
|e author
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|a Massachusetts Institute of Technology. Department of Biological Engineering
|e contributor
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|a Massachusetts Institute of Technology. Department of Biology
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|a Whitehead Institute for Biomedical Research
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|a Monnier, Nilah
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|a Barry, Zachary Thomas
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|a Su, Kuan-Chung
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|a Dey, Arkajit
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|a Pan, Keyao
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|a Cheeseman, Iain M
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|a Bathe, Mark
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|a Katz, Zachary
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|a English, Brian P.
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|a Singer, Robert H.
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|a Monnier, Nilah
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|a Barry, Zachary Thomas
|e author
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|a Su, Kuan-Chung
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|a Dey, Arkajit
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|a Pan, Keyao
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|a Cheeseman, Iain M
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|a Bathe, Mark
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|a Inferring transient particle transport dynamics in live cells
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|b Nature Publishing Group,
|c 2017-01-17T19:12:46Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/106511
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|a Live-cell imaging and particle tracking provide rich information on mechanisms of intracellular transport. However, trajectory analysis procedures to infer complex transport dynamics involving stochastic switching between active transport and diffusive motion are lacking. We applied Bayesian model selection to hidden Markov modeling to infer transient transport states from trajectories of mRNA-protein complexes in live mouse hippocampal neurons and metaphase kinetochores in dividing human cells. The software is available at http://hmm-bayes.org/.
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|a National Institutes of Health (U.S.)
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|a National Institute of Mental Health (U.S.) (Grant U01 MH106011)
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|a National Science Foundation (U.S.). Physics of Living Systems (Grant PHY 1305537)
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|a Leukemia & Lymphoma Society of America (Scholar Award)
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|a National Institute of General Medical Sciences (U.S.) (Grant GM088313)
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|a Austrian Science Fund (Schroedinger Fellowship)
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|a en_US
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|a Article
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|t Nature Methods
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