MCMC Estimation of Markov Models for Ion Channels

Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
Biophysical Journal (Impact Factor: 3.97). 04/2011; 100(8):1919-29. DOI: 10.1016/j.bpj.2011.02.059
Source: PubMed


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.

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    • "Here, we focus on the problem of idealization, as it is called in this field, of ion channel current traces, i.e., on the reconstruction or estimation of the channel's conductivity over time without noise; one may also call that denoising or signal detection. This is an important step in the analysis of an ion channel's traces as many of its characteristics can be decuced from idealized traces: number of states, open and closed times, transition rates between states [5], or the Nernst potential [6]. Many idealization methods are based on specific models for the channel's behavior [7], e.g., that there is a Markov chain of states, each with its associated conductance. "
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    • "Improved version of the algorithm by Siekmann et al. (2011) The main difference to the algorithm described in Siekmann et al. (2) is that the complicated likelihood function presented there (which requires sampling a sequence of Markov states) is replaced by a simpler version, see Eq. A12. More details can be found in Siekmann et al. (3). "

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