Department of Cellular and Molecular Physiology, Yale University, New Haven, Connecticut, USA.
Biophys J. 2010 Dec 1;99(11):3684-95. doi: 10.1016/j.bpj.2010.09.067.
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.
隐马尔可夫模型（HMM）为记录提供了极好的分析，这些记录来自于离子通道等系统，其信号/噪声比非常差，这些系统在几个状态之间切换。这种方法最近也被用于建模分子马达的动力学速率常数，其中可观察的变量-位置-由于马达的反应循环而不断积累。我们提出了一种新的 HMM 实现，用于获得分子马达反应循环的化学动力学模型，称为变步长 HMM，其中量化的位置变量由马尔可夫模型的大量状态表示。与以前的方法不同，该模型允许任意步长分布，并允许估计这些分布。结果是一个稳健的算法，几乎不需要用户输入来描述分子马达的步进动力学，这些马达的动力学是通过光学技术记录的。
