Model selection for hidden Markov models of ion channel data by reversible jump Markov chain Monte Carlo

M.C.M. de Gunst, B. Schouten

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Ion channels are proteins that are located in the membranes of cells and are capable of conducting ions through the membrane. An ion channel is not always 'open' for transport. The ion channel molecule may reside in several configurations, some of which correspond to an open channel and others to a closed channel. The transitions of the channel between the different configurational states have a random nature. Markov processes are often used to describe this randomness. In practice, there often exist a number of candidate Markov models. The objective of this paper is the selection of a Markov model from a finite collection of such models. We propose a Bayesian setting in which the model indicator itself is viewed as a random variable, and we develop a reversible jump Markov chain Monte Carlo (MCMC) algorithm in order to generate a sample from the posterior distribution of the model indicator given the data of a single-channel recording. A hidden Markov model is used to incorporate the correlated noise in recordings and the effects of filters that are present in the experimental set-up. The reversible jump MCMC sampler is applied to both simulated and recorded data sets. © 2003 ISI/BS.
Original languageEnglish
Pages (from-to)373-393
JournalBernoulli: A Journal of Mathematical Statistics and Probability
Volume9
Issue number3
DOIs
Publication statusPublished - 2003

Fingerprint

Reversible Jump Markov Chain Monte Carlo
Ion Channels
Model Selection
Markov Model
Membrane
Open Channel
Correlated Noise
Markov Chain Monte Carlo Algorithms
Posterior distribution
Markov Process
Randomness
Random variable
Molecules
Model
Filter
Protein
Closed
Configuration
Cell

Cite this

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title = "Model selection for hidden Markov models of ion channel data by reversible jump Markov chain Monte Carlo",
abstract = "Ion channels are proteins that are located in the membranes of cells and are capable of conducting ions through the membrane. An ion channel is not always 'open' for transport. The ion channel molecule may reside in several configurations, some of which correspond to an open channel and others to a closed channel. The transitions of the channel between the different configurational states have a random nature. Markov processes are often used to describe this randomness. In practice, there often exist a number of candidate Markov models. The objective of this paper is the selection of a Markov model from a finite collection of such models. We propose a Bayesian setting in which the model indicator itself is viewed as a random variable, and we develop a reversible jump Markov chain Monte Carlo (MCMC) algorithm in order to generate a sample from the posterior distribution of the model indicator given the data of a single-channel recording. A hidden Markov model is used to incorporate the correlated noise in recordings and the effects of filters that are present in the experimental set-up. The reversible jump MCMC sampler is applied to both simulated and recorded data sets. {\circledC} 2003 ISI/BS.",
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Model selection for hidden Markov models of ion channel data by reversible jump Markov chain Monte Carlo. / de Gunst, M.C.M.; Schouten, B.

In: Bernoulli: A Journal of Mathematical Statistics and Probability, Vol. 9, No. 3, 2003, p. 373-393.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - Model selection for hidden Markov models of ion channel data by reversible jump Markov chain Monte Carlo

AU - de Gunst, M.C.M.

AU - Schouten, B.

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AB - Ion channels are proteins that are located in the membranes of cells and are capable of conducting ions through the membrane. An ion channel is not always 'open' for transport. The ion channel molecule may reside in several configurations, some of which correspond to an open channel and others to a closed channel. The transitions of the channel between the different configurational states have a random nature. Markov processes are often used to describe this randomness. In practice, there often exist a number of candidate Markov models. The objective of this paper is the selection of a Markov model from a finite collection of such models. We propose a Bayesian setting in which the model indicator itself is viewed as a random variable, and we develop a reversible jump Markov chain Monte Carlo (MCMC) algorithm in order to generate a sample from the posterior distribution of the model indicator given the data of a single-channel recording. A hidden Markov model is used to incorporate the correlated noise in recordings and the effects of filters that are present in the experimental set-up. The reversible jump MCMC sampler is applied to both simulated and recorded data sets. © 2003 ISI/BS.

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