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通过分数阶时间序列模型识别质膜上单粒子轨迹的扩散运动。

Identifying diffusive motions in single-particle trajectories on the plasma membrane via fractional time-series models.

机构信息

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

Department of Biomedical Sciences, Colorado State University, Fort Collins, CO 80523, USA.

出版信息

Phys Rev E. 2019 Jan;99(1-1):012101. doi: 10.1103/PhysRevE.99.012101.

Abstract

In this paper we show that an autoregressive fractionally integrated moving average time-series model can identify two types of motion of membrane proteins on the surface of mammalian cells. Specifically we analyze the motion of the voltage-gated sodium channel Nav1.6 and beta-2 adrenergic receptors. We find that the autoregressive (AR) part models well the confined dynamics whereas the fractionally integrated moving average (FIMA) model describes the nonconfined periods of the trajectories. Since the Ornstein-Uhlenbeck process is a continuous counterpart of the AR model, we are also able to calculate its physical parameters and show their biological relevance. The fitted FIMA and AR parameters show marked differences in the dynamics of the two studied molecules.

摘要

本文表明,自回归分数阶积分移动平均时间序列模型可以识别哺乳动物细胞膜蛋白的两种运动类型。具体来说,我们分析了电压门控钠离子通道 Nav1.6 和β2 肾上腺素能受体的运动。我们发现,自回归(AR)部分很好地模拟了受限动力学,而分数阶积分移动平均(FIMA)模型描述了轨迹的非受限时期。由于 Ornstein-Uhlenbeck 过程是 AR 模型的连续对应物,我们也能够计算其物理参数,并展示其生物学相关性。拟合的 FIMA 和 AR 参数在两个研究分子的动力学方面表现出显著差异。

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本文引用的文献

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Compartmentalization of the plasma membrane.质膜的分区化。
Curr Opin Cell Biol. 2018 Aug;53:15-21. doi: 10.1016/j.ceb.2018.04.002. Epub 2018 Apr 12.

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