Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
Biophys J. 2011 Apr 20;100(8):1919-29. doi: 10.1016/j.bpj.2011.02.059.
Ion channels are characterized by inherently stochastic behavior which can be represented by continuous-time Markov models (CTMM). Although methods for collecting data from single ion channels are available, translating a time series of open and closed channels to a CTMM remains a challenge. Bayesian statistics combined with Markov chain Monte Carlo (MCMC) sampling provide means for estimating the rate constants of a CTMM directly from single channel data. In this article, different approaches for the MCMC sampling of Markov models are combined. This method, new to our knowledge, detects overparameterizations and gives more accurate results than existing MCMC methods. It shows similar performance as QuB-MIL, which indicates that it also compares well with maximum likelihood estimators. Data collected from an inositol trisphosphate receptor is used to demonstrate how the best model for a given data set can be found in practice.
离子通道的特征是固有随机性，可以用连续时间马尔可夫模型（CTMM）来表示。虽然有从单个离子通道收集数据的方法，但将开和闭通道的时间序列转换为 CTMM 仍然是一个挑战。贝叶斯统计与马尔可夫链蒙特卡罗（MCMC）抽样相结合，为直接从单个通道数据估计 CTMM 的速率常数提供了一种方法。在本文中，结合了用于 MCMC 抽样的不同马尔可夫模型方法。就我们所知，这种方法检测到了过度参数化，并给出了比现有 MCMC 方法更准确的结果。它与 QuB-MIL 的性能相似，这表明它与最大似然估计器的比较也很好。使用三磷酸肌醇受体收集的数据来演示如何在实践中找到给定数据集的最佳模型。
