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基于扩散类型的时间依赖性分类:均方位移指数转变的统计检测。

Time-dependent classification of protein diffusion types: A statistical detection of mean-squared-displacement exponent transitions.

机构信息

Faculty of Pure and Applied Mathematics, Hugo Steinhaus Center, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland.

出版信息

Phys Rev E. 2020 Feb;101(2-1):022107. doi: 10.1103/PhysRevE.101.022107.

Abstract

In this paper, we have proposed a statistical procedure for detecting transitions of the mean-square-displacement exponent value within a single trajectory. With this procedure, we have identified three regimes of proteins dynamics on a cell membrane, namely, subdiffusion, free diffusion, and immobility. The fourth considered dynamics type, namely, superdiffusion was not detected. We show that the analyzed protein trajectories are not stationary and not ergodic. Moreover, classification of the dynamics type performed without prior detection of transitions may lead to the overestimation of the proportion of subdiffusive trajectories.

摘要

在本文中,我们提出了一种统计程序,用于在单个轨迹内检测均方位移指数值的转变。通过此程序,我们确定了细胞膜上蛋白质动力学的三种状态,即亚扩散、自由扩散和不活动。未检测到第四个考虑的动力学类型,即超扩散。我们表明,分析的蛋白质轨迹不是稳定的,也不是遍历的。此外,在没有预先检测到转变的情况下进行的动力学类型分类可能导致亚扩散轨迹比例的高估。

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