首页> 外文期刊>The Journal of Membrane Biology: An International Journal for Studies on the Structure, Function & Genesis of Biomembranes >Distributions-per-level: a means of testing level detectors and models of patch-clamp data.
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Distributions-per-level: a means of testing level detectors and models of patch-clamp data.

机译:每个级别的分布:一种测试级别检测器和膜片钳数据模型的方法。

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摘要

Level or jump detectors generate the reconstructed time series from a noisy record of patch-clamp current. The reconstructed time series is used to create dwell-time histograms for the kinetic analysis of the Markov model of the investigated ion channel. It is shown here that some additional lines in the software of such a detector can provide a powerful new means of patch-clamp analysis. For each current level that can be recognized by the detector, an array is declared. The new software assigns every data point of the original time series to the array that belongs to the actual state of the detector. From the data sets in these arrays distributions-per-level are generated. Simulated and experimental time series analyzed by Hinkley detectors are used to demonstrate the benefits of these distributions-per-level. First, they can serve as a test of the reliability of jump and level detectors. Second, they can reveal beta distributions as resulting from fast gating that would usually be hidden in the overall amplitude histogram. Probably the most valuable feature is that the malfunctions of the Hinkley detectors turn out to depend on the Markov model of the ion channel. Thus, the errors revealed by the distributions-per-level can be used to distinguish between different putative Markov models of the measured time series.
机译:电平或跳变检测器根据贴片钳电流的噪声记录生成重建的时间序列。重建的时间序列用于创建停留时间直方图,以对所研究离子通道的马尔可夫模型进行动力学分析。此处显示,这种检测器软件中的一些附加行可以提供强大的新型膜片钳分析方法。对于检测器可以识别的每个电流水平,声明一个数组。新软件将原始时间序列的每个数据点分配给属于检测器实际状态的阵列。根据这些数组中的数据集,生成每个级别的分布。由欣克利探测器进行的仿真和实验时间序列用于证明每级这些分布的好处。首先,它们可以用作跳跃和电平检测器可靠性的测试。其次,他们可以揭示由于快速门控而产生的β分布,通常会隐藏在整体振幅直方图中。可能最有价值的功能是,欣克利探测器的故障最终取决于离子通道的马尔可夫模型。因此,每级分布揭示的误差可用于区分测量时间序列的不同推定马尔可夫模型。

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