Literature DB >> 19168073

Markov chain Monte Carlo fitting of single-channel data from inositol trisphosphate receptors.

Elan Gin1, Martin Falcke, Larry E Wagner, David I Yule, James Sneyd.   

Abstract

In many cell types, the inositol trisphosphate receptor (IPR) is one of the important components that control intracellular calcium dynamics, and an understanding of this receptor (which is also a calcium channel) is necessary for an understanding of calcium oscillations and waves. Recent advances in experimental techniques now allow for the measurement of single-channel activity of the IPR in conditions similar to its native environment, and these data can be used to determine the rate constants in Markov models of the IPR. We illustrate a parameter estimation method based on Markov chain Monte Carlo, which can be used to fit directly to single-channel data, and determining, as an intrinsic part of the fit, the times at which the IPR is opening and closing. We show, using simulated data, the most complex Markov model that can be unambiguously determined from steady-state data and show that non-steady-state data is required to determine more complex models.

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Year:  2008        PMID: 19168073     DOI: 10.1016/j.jtbi.2008.12.020

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  12 in total

1.  A kinetic model of the inositol trisphosphate receptor based on single-channel data.

Authors:  Elan Gin; Martin Falcke; Larry E Wagner; David I Yule; James Sneyd
Journal:  Biophys J       Date:  2009-05-20       Impact factor: 4.033

2.  Inositol trisphosphate receptor and ion channel models based on single-channel data.

Authors:  Elan Gin; Larry E Wagner; David I Yule; James Sneyd
Journal:  Chaos       Date:  2009-09       Impact factor: 3.642

3.  MCMC estimation of Markov models for ion channels.

Authors:  Ivo Siekmann; Larry E Wagner; David Yule; Colin Fox; David Bryant; Edmund J Crampin; James Sneyd
Journal:  Biophys J       Date:  2011-04-20       Impact factor: 4.033

4.  MCMC can detect nonidentifiable models.

Authors:  Ivo Siekmann; James Sneyd; Edmund J Crampin
Journal:  Biophys J       Date:  2012-12-05       Impact factor: 4.033

5.  Bayesian Statistical Inference in Ion-Channel Models with Exact Missed Event Correction.

Authors:  Michael Epstein; Ben Calderhead; Mark A Girolami; Lucia G Sivilotti
Journal:  Biophys J       Date:  2016-07-26       Impact factor: 4.033

6.  Computing rates of Markov models of voltage-gated ion channels by inverting partial differential equations governing the probability density functions of the conducting and non-conducting states.

Authors:  Aslak Tveito; Glenn T Lines; Andrew G Edwards; Andrew McCulloch
Journal:  Math Biosci       Date:  2016-05-03       Impact factor: 2.144

7.  Potassium-selective block of barium permeation through single KcsA channels.

Authors:  Kene N Piasta; Douglas L Theobald; Christopher Miller
Journal:  J Gen Physiol       Date:  2011-09-12       Impact factor: 4.086

8.  A multi-scale approach to airway hyperresponsiveness: from molecule to organ.

Authors:  Anne-Marie Lauzon; Jason H T Bates; Graham Donovan; Merryn Tawhai; James Sneyd; Michael J Sanderson
Journal:  Front Physiol       Date:  2012-06-11       Impact factor: 4.566

9.  Quantitative properties and receptor reserve of the DAG and PKC branch of G(q)-coupled receptor signaling.

Authors:  Björn H Falkenburger; Eamonn J Dickson; Bertil Hille
Journal:  J Gen Physiol       Date:  2013-05       Impact factor: 4.086

10.  Comparison of models for IP3 receptor kinetics using stochastic simulations.

Authors:  Katri Hituri; Marja-Leena Linne
Journal:  PLoS One       Date:  2013-04-10       Impact factor: 3.240

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