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Aggregated Markov Model Using Time Series of Single Molecule Dwell Times with Minimum Excessive Information

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/53360

Title: Aggregated Markov Model Using Time Series of Single Molecule Dwell Times with Minimum Excessive Information
Authors: Li, Chun-Biu Browse this author
Komatsuzaki, Tamiki Browse this author →KAKEN DB
Issue Date: 1-Aug-2013
Publisher: American Physical Society
Journal Title: Physical Review Letters
Volume: 111
Issue: 5
Start Page: 58301
Publisher DOI: 10.1103/PhysRevLett.111.058301
PMID: 23952451
Abstract: Statistics of the dwell times, the stationary state distributions (SSDs), are often studied to infer the underlying kinetics from a single molecule finite-level time series. However, it is well known that the underlying kinetic scheme, a hidden Markov model (HMM), cannot be identified uniquely from the SSDs because some features of the underlying HMM are hidden by finite-level measurements. Here, we quantify the amount of excessive information in a given HMM that is not warranted by the measured SSDs and extract the HMM with minimum excessive information as the most objective representation of the data. The method is applied to a single molecule enzymatic turnover experiment, and the origin of dynamic disorder is discussed in terms of the network properties of the HMM.
Rights: ©2013 American Physical Society
Type: article
URI: http://hdl.handle.net/2115/53360
Appears in Collections:電子科学研究所 (Research Institute for Electronic Science) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 小松崎 民樹

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