Bayesian Statistical Inference in Ion-Channel Models with Exact Missed Event Correction
Bayesian Statistical Inference in Ion-Channel Models with Exact Missed Event Correction
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DOI：
10.1016/j.bpj.2016.04.053
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发表时间：
2016-07-26
影响因子：
3.4
通讯作者：
Sivilotti, Lucia G.
中科院分区：
文献类型：
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作者：
Epstein, Michael;Calderhead, Ben;Sivilotti, Lucia G.
单离子通道的随机行为通常被描述为具有离散状态的聚合连续时间马尔可夫过程。对于配体门控通道，每个状态可以代表通道蛋白的不同构象或不同数量的结合配体。单通道记录仅显示通道是打开还是关闭：相等电导的状态被聚合，因此必须间接推断它们之间的转换。从原始信号中过滤噪声的要求进一步使建模过程变得复杂，因为它限制了数据的时间分辨率。带宽减少的结果是无法观察到比分辨率短的开口或关闭；这些被称为错过的事件。因此，使用过滤数据拟合的假设模型必须明确考虑遗漏事件，以避免速率参数估计中的偏差，从而准确评估参数可识别性。在本文中，据我们所知，我们提出了第一个离子通道的贝叶斯建模，并具有精确的遗漏事件校正。贝叶斯分析通过将模型参数视为随机变量来表示对模型参数真实值的不确定知识。这使我们能够在估计模型参数值时充分了解参数的可识别性和不确定性。然而，贝叶斯推理在这种情况下尤其具有挑战性，因为对遗漏事件的校正增加了模型可能性的计算复杂性。尽管如此，我们成功地实现了一种称为“BICME”的两步马尔可夫链蒙特卡罗方法，该方法在现实复杂性的模型中执行贝叶斯推理。该方法在来自肌肉烟碱乙酰胆碱通道的合成和真实单通道数据上进行了演示。我们表明，与最大似然方法相比，可以更准确地表征参数不确定性。我们在这些离子通道模型中执行推理的代码是公开的。
The stochastic behavior of single ion channels is most often described as an aggregated continuous-time Markov process with discrete states. For ligand-gated channels each state can represent a different conformation of the channel protein or a different number of bound ligands. Single-channel recordings show only whether the channel is open or shut: states of equal conductance are aggregated, so transitions between them have to be inferred indirectly. The requirement to filter noise from the raw signal further complicates the modeling process, as it limits the time resolution of the data. The consequence of the reduced bandwidth is that openings or shuttings that are shorter than the resolution cannot be observed; these are known as missed events. Postulated models fitted using filtered data must therefore explicitly account for missed events to avoid bias in the estimation of rate parameters and therefore assess parameter identifiability accurately. In this article, we present the first, to our knowledge, Bayesian modeling of ion-channels with exact missed events correction. Bayesian analysis represents uncertain knowledge of the true value of model parameters by considering these parameters as random variables. This allows us to gain a full appreciation of parameter identifiability and uncertainty when estimating values for model parameters. However, Bayesian inference is particularly challenging in this context as the correction for missed events increases the computational complexity of the model likelihood. Nonetheless, we successfully implemented a two-step Markov chain Monte Carlo method that we called "BICME", which performs Bayesian inference in models of realistic complexity. The method is demonstrated on synthetic and real single-channel data from muscle nicotinic acetylcholine channels. We show that parameter uncertainty can be characterized more accurately than with maximum-likelihood methods. Our code for performing inference in these ion channel models is publicly available.