Bayesian Approaches for Mechanistic Ion Channel Modeling

  • Ben Calderhead
  • Michael Epstein
  • Lucia Sivilotti
  • Mark Girolami
Part of the Methods in Molecular Biology book series (MIMB, volume 1021)


We consider the Bayesian analysis of mechanistic models describing the dynamic behavior of ligand-gated ion channels. The opening of the transmembrane pore in an ion channel is brought about by conformational changes in the protein, which results in a flow of ions through the pore. Remarkably, given the diameter of the pore, the flow of ions from a small number of channels or indeed from a single ion channel molecule can be recorded experimentally. This produces a large time-series of high-resolution experimental data, which can be used to investigate the gating process of these channels. We give a brief overview of the achievements and limitations of alternative maximum-likelihood approaches to this type of modeling, before investigating the statistical issues associated with analyzing stochastic model reaction mechanisms from a Bayesian perspective. Finally, we compare a number of Markov chain Monte Carlo algorithms that may be used to tackle this challenging inference problem.

Key words

Ion channels Mechanistic models Bayesian inference Markov chain Monte Carlo 


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Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Ben Calderhead
    • 1
  • Michael Epstein
    • 1
  • Lucia Sivilotti
    • 1
  • Mark Girolami
    • 1
  1. 1.Department of Computing ScienceUniversity of GlasgowGlasgowScotland

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