Institute for Physics & Astronomy, University of Potsdam, Karl-Liebknecht-Straße 24/25, 14476 Potsdam-Golm, Germany.
Max Planck Institute for the Physics of Complex Systems, Nöthnitzer Straße 38, 01187 Dresden, Germany.
Phys Chem Chem Phys. 2022 Aug 10;24(31):18482-18504. doi: 10.1039/d2cp01741e.
How does a systematic time-dependence of the diffusion coefficient () affect the ergodic and statistical characteristics of fractional Brownian motion (FBM)? Here, we answer this question studying the characteristics of a set of standard statistical quantifiers relevant to single-particle-tracking (SPT) experiments. We examine, for instance, how the behavior of the ensemble- and time-averaged mean-squared displacements-denoted as the standard MSD 〈()〉 and TAMSD quantifiers-of FBM featuring (where is the Hurst exponent and is the [lag] time) changes in the presence of a power-law deterministically varying diffusivity () ∝ -germane to the process of scaled Brownian motion (SBM)-determining the strength of fractional Gaussian noise. The resulting compound "scaled-fractional" Brownian motion or FBM-SBM is found to be nonergodic, with 〈()〉 ∝ and . We also detect a stalling behavior of the MSDs for very subdiffusive SBM and FBM, when + 2 - 1 < 0. The distribution of particle displacements for FBM-SBM remains Gaussian, as that for the parent processes of FBM and SBM, in the entire region of scaling exponents (0 < < 2 and 0 < < 1). The FBM-SBM process is aging in a manner similar to SBM. The velocity autocorrelation function (ACF) of particle increments of FBM-SBM exhibits a dip when the parent FBM process is subdiffusive. Both for sub- and superdiffusive FBM contributions to the FBM-SBM process, the SBM exponent affects the long-time decay exponent of the ACF. Applications of the FBM-SBM-amalgamated process to the analysis of SPT data are discussed. A comparative tabulated overview of recent experimental (mainly SPT) and computational datasets amenable for interpretation in terms of FBM-, SBM-, and FBM-SBM-like models of diffusion culminates the presentation. The statistical aspects of the dynamics of a wide range of biological systems is compared in the table, from nanosized beads in living cells, to chromosomal loci, to water diffusion in the brain, and, finally, to patterns of animal movements.
扩散系数的系统时变如何影响分数布朗运动(FBM)的遍历性和统计特征?在这里,我们通过研究一组与单粒子跟踪(SPT)实验相关的标准统计量的特征来回答这个问题。我们研究了例如,在存在幂律确定性变化扩散系数()∝-与标度布朗运动(SBM)过程相关的分数高斯噪声的分数高斯噪声的情况下,具有(其中是赫斯特指数,是[滞后]时间)的 FBM 的系综和时间平均均方根位移-表示为标准 MSD〈()〉和 TAMSD 量-的行为如何变化,确定分数高斯噪声的强度。发现这种复合“标度分数”布朗运动或 FBM-SBM 是非遍历的,〈()〉∝并且。我们还检测到非常亚扩散 SBM 和 FBM 的 MSD 出现停滞行为,当+2-1<0 时。对于 FBM-SBM,粒子位移的分布仍然是高斯分布,就像 FBM 和 SBM 的母体过程一样,在整个标度指数区域(0< <2 和 0< <1)内。FBM-SBM 过程以类似于 SBM 的方式老化。FBM-SBM 颗粒增量的速度自相关函数(ACF)在母体 FBM 过程是亚扩散时表现出凹陷。对于 FBM-SBM 过程的亚扩散和超扩散 FBM 贡献,SBM 指数都会影响 ACF 的长时间衰减指数。讨论了将 FBM-SBM 合并过程应用于 SPT 数据分析。总结了最近的实验(主要是 SPT)和可用于解释分数布朗运动(FBM)、标度布朗运动(SBM)和 FBM-SBM 样扩散模型的计算数据集的比较表格概述。该表格比较了从活细胞中的纳米级珠粒到染色体位置,再到大脑中的水扩散,最后到动物运动模式的广泛生物系统的动力学的统计方面。