Tony O'Hagan - Academic pages - Abstracts

## Bayesian Inference for Ion Channel Gating
Mechanisms Directly from Single Channel
Recordings, using Markov Chain Monte Carlo

F. G. Ball, Y. Cai, J. B. Kadane and A. O'Hagan

*University of Nottingham*

**Publication details: **
*Proceedings of the Royal Society of London* A
**455**, 2879-2932, 1999.

### Abstract

The gating mechanism of a single ion channel is usually modelled by a
finite state space continuous time Markov chain. The patch clamp technique
enables the experimenter to record the current flowing across a single
ion channel. In practice, the current is corrupted by noise and lowpass
filtering, and is sampled with a typically very short sampling interval. We
present a method for performing Bayesian inference about parameters
governing the underlying single channel gating mechanism and the recording
process, directly from such single channel recordings. Our procedure uses a
technique known as Markov chain Monte Carlo, which involves
constructing a Markov chain whose equilibrium distribution is given by the posterior
distribution of the unknown parameters given the observed data.
Simulation of that Markov chain then enables the investigator to estimate the
required posterior distribution. As well as providing a method of
estimating the transition rates of the underlying Markov chain used to model the
single channel gating mechanism and the means and variances of open and
closed conductance levels, the output from our Markov chain Monte Carlo
simulations can also be used to estimate single channel properties, such
as the mean lengths of open and closed sojourn times, and to reconstruct
the unobserved quantal signal which indicates whether the channel is open
or closed. The theory is illustrated by several numerical examples taken
mainly from the ion channel literature.

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Updated: 17 February 2000
Maintained by: Tony O'Hagan