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MCMC estimation of Markov models for ion channels - PubMed

  • ️Sat Jan 01 2011

MCMC estimation of Markov models for ion channels

Ivo Siekmann et al. Biophys J. 2011.

Abstract

Ion channels are characterized by inherently stochastic behavior which can be represented by continuous-time Markov models (CTMM). Although methods for collecting data from single ion channels are available, translating a time series of open and closed channels to a CTMM remains a challenge. Bayesian statistics combined with Markov chain Monte Carlo (MCMC) sampling provide means for estimating the rate constants of a CTMM directly from single channel data. In this article, different approaches for the MCMC sampling of Markov models are combined. This method, new to our knowledge, detects overparameterizations and gives more accurate results than existing MCMC methods. It shows similar performance as QuB-MIL, which indicates that it also compares well with maximum likelihood estimators. Data collected from an inositol trisphosphate receptor is used to demonstrate how the best model for a given data set can be found in practice.

Copyright © 2011 Biophysical Society. Published by Elsevier Inc. All rights reserved.

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Figures

Figure 1
Figure 1

Examples for Markov models.

Figure 2
Figure 2

Histograms for the algorithm MHG after 50,000 iterations and a burn-in time of 10,000 iterations. MHG was run on a test data set consisting of 40,000 data points generated from model M2 (see Table 1, M2 columns). (Vertical dotted lines) True values of the rate constants. (Asterisks) Means of the histograms and (Arrows) standard deviations.

Figure 3
Figure 3

Model M2 is fitted to test data of 40,000 data points generated from the simpler model M1 using the MH sampler. The convergence plots (a and b) show that the rates connecting to the extra state O5 wander around whereas the others tend to the correct values (compare this to Table 1, M1 columns). Histograms for q45 and q54 are shown in panel c. The wide-spread multimodal posterior distributions for both rate constants clearly indicate that the state O5 is not supported by the data.

Figure 4
Figure 4

Model M3 is fitted to test data (40,000 data points) generated from the simpler model M1 using the MH algorithm. The stationary probability of the additional open state O5 quickly tends to zero. This suggests that the sampler detects when a model is too complex for representing a given data set and reacts by switching off transitions to the additional state.

Figure 5
Figure 5

Rosales' Gibbs sampler and the MH algorithm are compared for a test data set of 100,000 data points. As representative examples, we show histograms for components ρ11 and ρ15 of the matrix exponential Aτ (plotted in red) of model M2 (see Fig. 1) with results from Rosales' Gibbs sampler (plotted in green). (Vertical dotted line) Exact value of the matrix component. Both methods give very similar results for the diagonal of Aτ, as can be seen in panel a, for example. Mean and standard deviations for MH algorithm (purple) and Rosales' Gibbs sampler (green) are similar. The histograms for the off-diagonal elements found by Rosales' method are distributed over a wide range and are therefore much less accurate than the estimates found by MH; compare the two fits for ρ15 in panel b.

Figure 6
Figure 6

Selected histograms for a MHG run (50,000 iterations) and results of QUB-MIL for a test data set of 40,000 data points. (Dotted vertical line) The true value of a rate constant. (Asterisk) The maximum likelihood estimator found by QuB-MIL. (Upper arrows) Standard deviation found by QuB-MIL; (lower arrows) mean and standard deviations found by MHG. The estimates with relative errors are q^32MIL=0.522(−13.0%),q^32MHG=0.606(+1%) and q^45MIL=0.365(+21.5%),q^45MHG=0.359(+19.7%).

Figure 7
Figure 7

Open histogram for a data set of 1,400,000 data points which was collected from an IP3-type I receptor at a calcium concentration of 200 nmol/L (24). This is shown together with the superimposed open time distributions determined by fits to two different models, one with one open state (M1) and one with two open states (M2) (see Fig. 1, a and b). Although the histogram has only one distinguished peak indicating that one open state is sufficient, it shows that the open time histogram is better approximated for long open events by the model with two open states.

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