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Modelling modal gating of ion channels with hierarchical Markov models

Siekmann, I, Fackrell, M, Crampin, EJ and Taylor, P (2016) Modelling modal gating of ion channels with hierarchical Markov models. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 472 (2192). ISSN 1364-5021

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Many ion channels spontaneously switch between different levels of activity. Although this behaviour known as modal gating has been observed for a long time it is currently not well understood. Despite the fact that appropriately representing activity changes is essential for accurately capturing time course data from ion channels, systematic approaches for modelling modal gating are currently not available. In this paper, we develop a modular approach for building such a model in an iterative process. First, stochastic switching between modes and stochastic opening and closing within modes are represented in separate aggregated Markov models. Second, the continuous-time hierarchical Markov model, a new modelling framework proposed here, then enables us to combine these components so that in the integrated model both mode switching as well as the kinetics within modes are appropriately represented. A mathematical analysis reveals that the behaviour of the hierarchical Markov model naturally depends on the properties of its components. We also demonstrate how a hierarchical Markov model can be parametrized using experimental data and show that it provides a better representation than a previous model of the same dataset. Because evidence is increasing that modal gating reflects underlying molecular properties of the channel protein, it is likely that biophysical processes are better captured by our new approach than in earlier models.

Item Type: Article
Uncontrolled Keywords: 01 Mathematical Sciences, 02 Physical Sciences, 09 Engineering
Subjects: Q Science > QA Mathematics
Q Science > QC Physics
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Applied Mathematics (merged with Comp Sci 10 Aug 20)
Publisher: Royal Society, The
Related URLs:
Date Deposited: 20 Sep 2017 11:14
Last Modified: 04 Sep 2021 03:50
DOI or ID number: 10.1098/rspa.2016.0122
URI: https://researchonline.ljmu.ac.uk/id/eprint/7134
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