Hidden Markov analysis of improved bandwidth mechanosensitive ion channel data

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• 作者：Ibrahim M. Almanjahie ; R. Nazim Khan ; Robin K. Milne…
• 关键词：EM algorithm ; Hidden Markov models ; Moving average filter ; Parameter estimation ; MscL ; Patch clamp ; Level ; dependent noise
• 刊名：European Biophysics Journal
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：44
• 期：7
• 页码：545-556
• 全文大小：1,354 KB
• 参考文献：Aidley DJ, Stanfield PR (1996) Ion channels: molecules in action. Cambridge University Press, New York
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• 作者单位：Ibrahim M. Almanjahie (1) (2)
  R. Nazim Khan (1)
  Robin K. Milne (1)
  Takeshi Nomura (3)
  Boris Martinac (4)
  
  1. School of Mathematics and Statistics, University of Western Australia, Crawley, WA, 6009, Australia
  2. Department of Mathematics, King Khalid University, Abha, 61413, Saudi Arabia
  3. Department of Molecular Cell Physiology and Bio-Ionomics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, 602-8566, Japan
  4. Mechanosensory Biophysics Laboratory, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia
  
• 刊物类别：Physics and Astronomy
• 刊物主题：Physics
  Biophysics and Biomedical Physics
  Cell Biology
  Biochemistry
  Plant Physiology
  Animal Physiology
  Neurobiology
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1432-1017

文摘

The gating behaviour of a single ion channel can be described by hidden Markov models (HMMs), forming the basis for statistical analysis of patch clamp data. Extensive improved bandwidth (25 kHz, 50 kHz) data from the mechanosensitive channel of large conductance in Escherichia coli were analysed using HMMs, and HMMs with a moving average adjustment for filtering. The aim was to determine the number of levels, and mean current, mean dwell time and proportion of time at each level. Parameter estimates for HMMs with a moving average adjustment for low-pass filtering were obtained using an expectation-maximisation algorithm that depends on a generalisation of Baum’s forward–backward algorithm. This results in a simpler algorithm than those based on meta-states and a much smaller parameter space; hence, the computational load is substantially reduced. In addition, this algorithm maximises the actual log-likelihood rather than that for a related meta-state process. Comprehensive data analyses and comparisons across all our data sets have consistently shown five subconducting levels in addition to the fully open and closed levels for this channel. Keywords EM algorithm Hidden Markov models Moving average filter Parameter estimation MscL Patch clamp Level-dependent noise