Literature DB >> 24089728

Noisy continuous time random walks.

Jae-Hyung Jeon1, Eli Barkai, Ralf Metzler.   

Abstract

Experimental studies of the diffusion of biomolecules within biological cells are routinely confronted with multiple sources of stochasticity, whose identification renders the detailed data analysis of single molecule trajectories quite intricate. Here, we consider subdiffusive continuous time random walks that represent a seminal model for the anomalous diffusion of tracer particles in complex environments. This motion is characterized by multiple trapping events with infinite mean sojourn time. In real physical situations, however, instead of the full immobilization predicted by the continuous time random walk model, the motion of the tracer particle shows additional jiggling, for instance, due to thermal agitation of the environment. We here present and analyze in detail an extension of the continuous time random walk model. Superimposing the multiple trapping behavior with additive Gaussian noise of variable strength, we demonstrate that the resulting process exhibits a rich variety of apparent dynamic regimes. In particular, such noisy continuous time random walks may appear ergodic, while the bare continuous time random walk exhibits weak ergodicity breaking. Detailed knowledge of this behavior will be useful for the truthful physical analysis of experimentally observed subdiffusion.

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Year:  2013        PMID: 24089728     DOI: 10.1063/1.4816635

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  10 in total

1.  Perspective: Reaches of chemical physics in biology.

Authors:  Martin Gruebele; D Thirumalai
Journal:  J Chem Phys       Date:  2013-09-28       Impact factor: 3.488

2.  Single-Particle Diffusion Characterization by Deep Learning.

Authors:  Naor Granik; Lucien E Weiss; Elias Nehme; Maayan Levin; Michael Chein; Eran Perlson; Yael Roichman; Yoav Shechtman
Journal:  Biophys J       Date:  2019-06-22       Impact factor: 4.033

3.  Filament Rigidity Vies with Mesh Size in Determining Anomalous Diffusion in Cytoskeleton.

Authors:  Sylas J Anderson; Christelle Matsuda; Jonathan Garamella; Karthik Reddy Peddireddy; Rae M Robertson-Anderson; Ryan McGorty
Journal:  Biomacromolecules       Date:  2019-11-15       Impact factor: 6.988

4.  Guidelines for the fitting of anomalous diffusion mean square displacement graphs from single particle tracking experiments.

Authors:  Eldad Kepten; Aleksander Weron; Grzegorz Sikora; Krzysztof Burnecki; Yuval Garini
Journal:  PLoS One       Date:  2015-02-13       Impact factor: 3.240

5.  Estimating the anomalous diffusion exponent for single particle tracking data with measurement errors - An alternative approach.

Authors:  Krzysztof Burnecki; Eldad Kepten; Yuval Garini; Grzegorz Sikora; Aleksander Weron
Journal:  Sci Rep       Date:  2015-06-11       Impact factor: 4.379

6.  Quantifying non-ergodicity of anomalous diffusion with higher order moments.

Authors:  Maria Schwarzl; Aljaž Godec; Ralf Metzler
Journal:  Sci Rep       Date:  2017-06-20       Impact factor: 4.379

7.  Visual information and expert's idea in Hurst index estimation of the fractional Brownian motion using a diffusion type approximation.

Authors:  Ali R Taheriyoun; Meisam Moghimbeygi
Journal:  Sci Rep       Date:  2017-02-14       Impact factor: 4.379

8.  Neuronal messenger ribonucleoprotein transport follows an aging Lévy walk.

Authors:  Minho S Song; Hyungseok C Moon; Jae-Hyung Jeon; Hye Yoon Park
Journal:  Nat Commun       Date:  2018-01-24       Impact factor: 14.919

9.  Large Deviations for Continuous Time Random Walks.

Authors:  Wanli Wang; Eli Barkai; Stanislav Burov
Journal:  Entropy (Basel)       Date:  2020-06-22       Impact factor: 2.524

10.  Origin of subdiffusion of water molecules on cell membrane surfaces.

Authors:  Eiji Yamamoto; Takuma Akimoto; Masato Yasui; Kenji Yasuoka
Journal:  Sci Rep       Date:  2014-04-17       Impact factor: 4.379

  10 in total

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