Literature DB >> 2482085

Markov, fractal, diffusion, and related models of ion channel gating. A comparison with experimental data from two ion channels.

M S Sansom1, F G Ball, C J Kerry, R McGee, R L Ramsey, P N Usherwood.   

Abstract

The gating kinetics of single-ion channels are generally modeled in terms of Markov processes with relatively small numbers of channel states. More recently, fractal (Liebovitch et al. 1987. Math. Biosci. 84:37-68) and diffusion (Millhauser et al. 1988. Proc. Natl. Acad. Sci. USA. 85:1502-1507) models of channel gating have been proposed. These models propose the existence of many similar conformational substrates of the channel protein, all of which contribute to the observed gating kinetics. It is important to determine whether or not Markov models provide the most accurate description of channel kinetics if progress is to be made in understanding the molecular events of channel gating. In this study six alternative classes of gating model are tested against experimental single-channel data. The single-channel data employed are from (a) delayed rectifier K+ channels of NG 108-15 cells and (b) locust muscle glutamate receptor channels. The models tested are (a) Markov, (b) fractal, (c) one-dimensional diffusion, (d) three-dimensional diffusion, (e) stretched exponential, and (f) expo-exponential. The models are compared by fitting the predicted distributions of channel open and closed times to those observed experimentally. The models are ranked in order of goodness-of-fit using a boot-strap resampling procedure. The results suggest that Markov models provide a markedly better description of the observed open and closed time distributions for both types of channel. This provides justification for the continued use of Markov models to explore channel gating mechanisms.

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Year:  1989        PMID: 2482085      PMCID: PMC1280626          DOI: 10.1016/S0006-3495(89)82770-5

Source DB:  PubMed          Journal:  Biophys J        ISSN: 0006-3495            Impact factor:   4.033


  41 in total

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Authors:  L S Liebovitch
Journal:  Biophys J       Date:  1989-02       Impact factor: 4.033

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Journal:  Pflugers Arch       Date:  1987-11       Impact factor: 3.657

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Journal:  Proc Natl Acad Sci U S A       Date:  1984-02       Impact factor: 11.205

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Journal:  Biophys J       Date:  1978-04       Impact factor: 4.033

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  21 in total

1.  Ion channel gating: a first-passage time analysis of the Kramers type.

Authors:  Igor Goychuk; Peter Hänggi
Journal:  Proc Natl Acad Sci U S A       Date:  2002-03-12       Impact factor: 11.205

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Authors:  Leif Matsson; Virulh Sa-yakanit; Santipong Boribarn
Journal:  Neurochem Res       Date:  2003-02       Impact factor: 3.996

3.  Glutamate receptor-channel gating. Maximum likelihood analysis of gigaohm seal recordings from locust muscle.

Authors:  S E Bates; M S Sansom; F G Ball; R L Ramsey; P N Usherwood
Journal:  Biophys J       Date:  1990-07       Impact factor: 4.033

4.  Diffusion model in ion channel gating. Extension to agonist-activated ion channels.

Authors:  R E Oswald; G L Millhauser; A A Carter
Journal:  Biophys J       Date:  1991-05       Impact factor: 4.033

5.  Statistical properties of ion channel records. Part II: estimation from the macroscopic current.

Authors:  Ali Nekouzadeh; Yoram Rudy
Journal:  Math Biosci       Date:  2007-05-04       Impact factor: 2.144

6.  Statistical assessment of change point detectors for single molecule kinetic analysis.

Authors:  Sean P Parsons; Jan D Huizinga
Journal:  J Membr Biol       Date:  2013-05-08       Impact factor: 1.843

7.  Single ion channel models incorporating aggregation and time interval omission.

Authors:  F G Ball; G F Yeo; R K Milne; R O Edeson; B W Madsen; M S Sansom
Journal:  Biophys J       Date:  1993-02       Impact factor: 4.033

8.  Time course of reactions controlled and gated by intramolecular dynamics of proteins: predictions of the model of random walk on fractal lattices.

Authors:  M Kurzynski; K Palacz; P Chelminiak
Journal:  Proc Natl Acad Sci U S A       Date:  1998-09-29       Impact factor: 11.205

Review 9.  Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism.

Authors:  A Destexhe; Z F Mainen; T J Sejnowski
Journal:  J Comput Neurosci       Date:  1994-08       Impact factor: 1.621

10.  Nonlinear and Stochastic Dynamics in the Heart.

Authors:  Zhilin Qu; Gang Hu; Alan Garfinkel; James N Weiss
Journal:  Phys Rep       Date:  2014-10-10       Impact factor: 25.600

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