Suppr超能文献

马尔可夫、分形、扩散及相关的离子通道门控模型。与来自两种离子通道的实验数据的比较。

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

作者信息

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

机构信息

Department of Zoology, University of Nottingham, University Park, United Kingdom.

出版信息

Biophys J. 1989 Dec;56(6):1229-43. doi: 10.1016/S0006-3495(89)82770-5.

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.

摘要

单离子通道的门控动力学通常根据具有相对较少通道状态的马尔可夫过程进行建模。最近,已经提出了通道门控的分形模型(利博维奇等人,1987年。数学生物科学。84:37 - 68)和扩散模型(米尔豪泽等人,1988年。美国国家科学院院刊。85:1502 - 1507)。这些模型提出通道蛋白存在许多相似的构象底物,所有这些底物都对观察到的门控动力学有贡献。如果要在理解通道门控的分子事件方面取得进展,确定马尔可夫模型是否提供了对通道动力学最准确的描述就很重要。在本研究中,针对实验单通道数据测试了六类替代的门控模型。所采用的单通道数据来自(a)NG 108 - 15细胞的延迟整流钾通道和(b)蝗虫肌肉谷氨酸受体通道。测试的模型有(a)马尔可夫模型,(b)分形模型,(c)一维扩散模型,(d)三维扩散模型,(e)拉伸指数模型,以及(f)指数 - 指数模型。通过将通道开放和关闭时间的预测分布与实验观察到的分布进行拟合来比较这些模型。使用自举重采样程序按拟合优度对模型进行排序。结果表明,马尔可夫模型对这两种类型通道观察到的开放和关闭时间分布提供了明显更好的描述。这为继续使用马尔可夫模型来探索通道门控机制提供了依据。

相似文献

6
Percolation model of ionic channel dynamics.离子通道动力学的渗流模型。
Biophys J. 1990 Mar;57(3):681-4. doi: 10.1016/S0006-3495(90)82588-1.
7
Fractional diffusion modeling of ion channel gating.离子通道门控的分数扩散建模
Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Nov;70(5 Pt 1):051915. doi: 10.1103/PhysRevE.70.051915. Epub 2004 Nov 24.
8
Single channel kinetics of a glutamate receptor.谷氨酸受体的单通道动力学
Biophys J. 1987 Jan;51(1):137-44. doi: 10.1016/S0006-3495(87)83318-0.

引用本文的文献

2
Modeling Neurons in 3D at the Nanoscale.在纳米尺度上对三维神经元进行建模。
Adv Exp Med Biol. 2022;1359:3-24. doi: 10.1007/978-3-030-89439-9_1.
4
5
Ion channel noise can explain firing correlation in auditory nerves.离子通道噪声可以解释听觉神经中的放电相关性。
J Comput Neurosci. 2016 Oct;41(2):193-206. doi: 10.1007/s10827-016-0613-9. Epub 2016 Aug 2.
7
Nonlinear and Stochastic Dynamics in the Heart.心脏中的非线性与随机动力学
Phys Rep. 2014 Oct 10;543(2):61-162. doi: 10.1016/j.physrep.2014.05.002.
9
Gating of maxi channels observed from pseudo-phase portraits.从伪相图观察到的最大通道门控。
Am J Physiol Cell Physiol. 2013 Mar 1;304(5):C450-7. doi: 10.1152/ajpcell.00378.2012. Epub 2013 Jan 2.

本文引用的文献

3
On the Williams-Watts function of dielectric relaxation.关于介电弛豫的威廉姆斯-瓦特函数。
Proc Natl Acad Sci U S A. 1984 Feb;81(4):1280-3. doi: 10.1073/pnas.81.4.1280.
7
Estimating kinetic constants from single channel data.从单通道数据估算动力学常数。
Biophys J. 1983 Aug;43(2):207-23. doi: 10.1016/S0006-3495(83)84341-0.
10
On the stochastic properties of single ion channels.关于单离子通道的随机特性。
Proc R Soc Lond B Biol Sci. 1981 Mar 6;211(1183):205-35. doi: 10.1098/rspb.1981.0003.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验