Inferring transient particle transport dynamics in live cells
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 s...
| Main Authors: | , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Nature Publishing Group
2017
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| Online Access: | http://hdl.handle.net/1721.1/106511 https://orcid.org/0000-0001-8844-7170 https://orcid.org/0000-0003-4573-5206 https://orcid.org/0000-0002-3829-5612 https://orcid.org/0000-0002-6199-6855 |
| Summary: | 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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