Improved Hidden Markov Models for Molecular Motors, Part 1 Basic Theory
Improved Hidden Markov Models for Molecular Motors, Part 1 Basic Theory
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DOI：
10.1016/j.bpj.2010.09.067
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发表时间：
2010-12-01
影响因子：
3.4
通讯作者：
Sigworth, Fred J.
中科院分区：
文献类型：
--
作者：
Mullner, Fiona E.;Syed, Sheyum;Sigworth, Fred J.
隐马尔可夫模型 (HMM) 提供了对由离子通道等系统在几种状态之间切换的信号/噪声比非常差的记录的出色分析。该方法最近还用于对分子马达的动力学速率常数进行建模，其中可观察变量位置由于马达反应周期而稳定积累。我们提出了一种新的 HMM 实现，用于获得分子马达反应周期的化学动力学模型，称为可变步长 HMM，其中量化位置变量由马尔可夫模型的大量状态与以前的方法不同，该模型允许步长大小的任意分布，并允许估计这些分布结果是一个强大的算法，需要很少或不需要用户输入来表征光学技术记录的分子马达的步进动力学
Hidden Markov models (HMMs) provide an excellent analysis of recordings with very poor signal/noise ratio made from systems such as ion channels which switch among a few states This method has also recently been used for modeling the kinetic rate constants of molecular motors where the observable variable the position steadily accumulates as a result of the motor s reaction cycle We present a new HMM implementation for obtaining the chemical kinetic model of a molecular motor's reaction cycle called the variable stepsize HMM in which the quantized position variable is represented by a large number of states of the Markov model Unlike previous methods the model allows for arbitrary distributions of step sizes and allows these distributions to be estimated The result is a robust algorithm that requires little or no user input for characterizing the stepping kinetics of molecular motors as recorded by optical techniques