Bayesian inference and Markov chain Monte Carlo techniques offer alternative approaches to maximum-likelihood methods for fitting Markov models to single-channel patch-clamp data.
Bayesian methods may aid single-channel model selection; leaves open superiority over maximum-likelihood approaches in routine cardiac electrophysiology.
The inositol trisphosphate receptor (IPR) plays an important role in controlling the dynamics of intracellular Ca(2+). Single-channel patch-clamp recordings are a typical way to study these receptors as well as other ion channels. Methods for analyzing and using this type of data have been developed to fit Markov models of the receptor. The usual method of parameter fitting is based on maximum-likelihood techniques. However, Bayesian inference and Markov chain Monte Carlo techniques are becoming more popular. We describe the application of the Bayesian methods to real experimental single-channel data in three ion channels: the ryanodine receptor, the K(+) channel, and the IPR. One of the main aims of all three studies was that of model selection with different approaches taken. We also discuss the modeling implications for single-channel data that display different levels of channel activity within one recording.
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Gin et al. (2009) studied this question.
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