MCMC estimation of Markov models for ion channels |
| |
| Authors: | Siekmann Ivo Wagner Larry E Yule David Fox Colin Bryant David Crampin Edmund J Sneyd James |
| |
| Institution: | †Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand;‡Department of Engineering Science, The University of Auckland, Auckland, New Zealand;§Department of Mathematics, The University of Auckland, Auckland, New Zealand;¶Department of Pharmacology and Physiology, University of Rochester Medical Center, Rochester, New York;‖Department of Physics, University of Otago, Dunedin, New Zealand;∗∗Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand |
| |
| Abstract: | 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. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect PubMed 等数据库收录！ |
|
