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首页> 外文期刊>Mathematical Biosciences: An International Journal >Hidden Markov analysis of mechanosensitive ion channel gating
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Hidden Markov analysis of mechanosensitive ion channel gating

机译:机械灵敏离子通道门控的隐马尔可夫分析

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摘要

Patch clamp data from the large conductance mechanosensitive channel (MscL) in E. coli was studied with the aim of developing a strategy for statistical analysis based on hidden Markov models (HMMs) and determining the number of conductance levels of the channel, together with mean current, mean dwell time and equilibrium probability of occupancy for each level. The models incorporated state-dependent white noise and moving average adjustment for filtering, with maximum likelihood parameter estimates obtained using an EM (expectation-maximisation) based iteration. Adjustment for filtering was included as it could be expected that the electronic filter used in recording would have a major effect on obviously brief intermediate conductance level sojourns. Preliminary data analysis revealed that the brevity of intermediate level sojourns caused difficulties in assignment of data points to levels as a result of over-estimation of noise variances. When reasonable constraints were placed on these variances using the better determined noise variances for the closed and fully open levels, idealisation anomalies were eliminated. Nevertheless, simulations suggested that mean sojourn times for the intermediate levels were still considerably over-estimated, and that recording bandwidth was a major limitation; improved results were obtained with higher bandwidth data (10 kHz sampled at 25 kHz). The simplest model consistent with these data had four open conductance levels, intermediate levels being approximately 20%, 51% and 74% of fully open. The mean lifetime at the fully open level was about 1 ms; estimates for the three intermediate levels were 5492 mu s, probably still over-estimates. (c) 2005 Elsevier Inc. All rights reserved.
机译:研究了来自大肠杆菌中大电导机械敏感通道(MscL)的膜片钳数据,目的是开发一种基于隐马尔可夫模型(HMM)的统计分析策略,并确定该通道的电导水平数量以及平均值当前,平均停留时间和每个级别的均衡居住概率。这些模型结合了状态相关的白噪声和移动平均调整以进行滤波,并使用基于EM(期望最大化)的迭代获得了最大似然参数估计。可以对滤波进行调整,因为可以预期的是,记录中使用的电子滤波器将对明显短暂的中间电导水平产生很大影响。初步的数据分析表明,由于对噪声方差的高估,中级级别的逗留很短暂,导致难以将数据点分配给级别。当使用更好的确定的关闭和完全打开级别的噪声方差对这些方差进行合理的约束时,可以消除理想化异常。然而,模拟表明,中间级别的平均逗留时间仍然被大大高估了,而记录带宽是一个主要限制。使用更高的带宽数据(在25 kHz下采样10 kHz)可获得更好的结果。与这些数据一致的最简单模型具有四个开放电导水平,中间水平约为完全开放电导率的20%,51%和74%。完全打开时的平均寿命约为1 ms。三个中间水平的估计值为5492亩s,可能仍高估了。 (c)2005 Elsevier Inc.保留所有权利。

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