Bayesian approaches for mechanistic ion channel modeling.
Bayesian approaches for mechanistic ion channel modeling.
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DOI：
10.1007/978-1-62703-450-0_13
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发表时间：
2013-01-01
期刊：
影响因子：
--
通讯作者：
Girolami, Mark
中科院分区：
文献类型：
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作者：
Calderhead, Ben;Epstein, Michael;Girolami, Mark
我们考虑了描述配体门控离子通道动态行为的机制模型的贝叶斯分析。离子通道中跨膜孔的打开是由蛋白质的构象变化引起的，这导致离子通过孔流动。值得注意的是，给定孔的直径，离子从少数通道或从单个离子通道分子的流动可以被实验记录下来。这产生了大量高分辨率的时间序列实验数据，可用于研究这些通道的门控过程。在从贝叶斯的角度研究与分析随机模型反应机制相关的统计问题之前，我们简要概述了这种类型建模的替代最大似然方法的成就和局限性。最后，我们比较了一些可用于解决这一具有挑战性的推理问题的马尔可夫链蒙特卡罗算法。
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.