Literature DB >> 25297814

Anomalous diffusion models and their properties: non-stationarity, non-ergodicity, and ageing at the centenary of single particle tracking.

Ralf Metzler1, Jae-Hyung Jeon, Andrey G Cherstvy, Eli Barkai.   

Abstract

Modern microscopic techniques following the stochastic motion of labelled tracer particles have uncovered significant deviations from the laws of Brownian motion in a variety of animate and inanimate systems. Such anomalous diffusion can have different physical origins, which can be identified from careful data analysis. In particular, single particle tracking provides the entire trajectory of the traced particle, which allows one to evaluate different observables to quantify the dynamics of the system under observation. We here provide an extensive overview over different popular anomalous diffusion models and their properties. We pay special attention to their ergodic properties, highlighting the fact that in several of these models the long time averaged mean squared displacement shows a distinct disparity to the regular, ensemble averaged mean squared displacement. In these cases, data obtained from time averages cannot be interpreted by the standard theoretical results for the ensemble averages. Here we therefore provide a comparison of the main properties of the time averaged mean squared displacement and its statistical behaviour in terms of the scatter of the amplitudes between the time averages obtained from different trajectories. We especially demonstrate how anomalous dynamics may be identified for systems, which, on first sight, appear to be Brownian. Moreover, we discuss the ergodicity breaking parameters for the different anomalous stochastic processes and showcase the physical origins for the various behaviours. This Perspective is intended as a guidebook for both experimentalists and theorists working on systems, which exhibit anomalous diffusion.

Year:  2014        PMID: 25297814     DOI: 10.1039/c4cp03465a

Source DB:  PubMed          Journal:  Phys Chem Chem Phys        ISSN: 1463-9076            Impact factor:   3.676


  141 in total

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2.  Anomalous and heterogeneous DNA transport in biomimetic cytoskeleton networks.

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3.  Chromosomal locus tracking with proper accounting of static and dynamic errors.

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5.  Single-Particle Diffusion Characterization by Deep Learning.

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Review 6.  Understanding biochemical processes in the presence of sub-diffusive behavior of biomolecules in solution and living cells.

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Journal:  Biophys Rev       Date:  2019-08-23

Review 7.  Molecular Dynamics Simulations of Membrane Permeability.

Authors:  Richard M Venable; Andreas Krämer; Richard W Pastor
Journal:  Chem Rev       Date:  2019-02-12       Impact factor: 60.622

8.  Information Geometry of Spatially Periodic Stochastic Systems.

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Journal:  Entropy (Basel)       Date:  2019-07-12       Impact factor: 2.524

9.  A Continuous-Time Random Walk Extension of the Gillis Model.

Authors:  Gaia Pozzoli; Mattia Radice; Manuele Onofri; Roberto Artuso
Journal:  Entropy (Basel)       Date:  2020-12-18       Impact factor: 2.524

10.  A Rheological Study of the Association and Dynamics of MUC5AC Gels.

Authors:  Caroline E Wagner; Bradley S Turner; Michael Rubinstein; Gareth H McKinley; Katharina Ribbeck
Journal:  Biomacromolecules       Date:  2017-10-02       Impact factor: 6.988

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