Imperial College London

DrBenCalderhead

Faculty of Natural SciencesDepartment of Mathematics

Lecturer in Statistics
 
 
 
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Contact

 

+44 (0)20 7594 8577b.calderhead

 
 
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Location

 

523Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Calderhead:2013:10.1007/978-1-62703-450-0_13,
author = {Calderhead, B and Epstein, M and Sivilotti, L and Girolami, M},
doi = {10.1007/978-1-62703-450-0_13},
journal = {Methods Mol Biol},
pages = {247--272},
title = {Bayesian approaches for mechanistic ion channel modeling.},
url = {http://dx.doi.org/10.1007/978-1-62703-450-0_13},
volume = {1021},
year = {2013}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - 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.
AU - Calderhead,B
AU - Epstein,M
AU - Sivilotti,L
AU - Girolami,M
DO - 10.1007/978-1-62703-450-0_13
EP - 272
PY - 2013///
SP - 247
TI - Bayesian approaches for mechanistic ion channel modeling.
T2 - Methods Mol Biol
UR - http://dx.doi.org/10.1007/978-1-62703-450-0_13
UR - https://www.ncbi.nlm.nih.gov/pubmed/23715989
VL - 1021
ER -