Literature DB >> 25353510

Anomalous diffusion due to hindering by mobile obstacles undergoing Brownian motion or Orstein-Ulhenbeck processes.

Hugues Berry1, Hugues Chaté2.   

Abstract

In vivo measurements of the passive movements of biomolecules or vesicles in cells consistently report "anomalous diffusion," where mean-squared displacements scale as a power law of time with exponent α<1 (subdiffusion). While the detailed mechanisms causing such behaviors are not always elucidated, movement hindrance by obstacles is often invoked. However, our understanding of how hindered diffusion leads to subdiffusion is based on diffusion amidst randomly located immobile obstacles. Here, we have used Monte Carlo simulations to investigate transient subdiffusion due to mobile obstacles with various modes of mobility. Our simulations confirm that the anomalous regimes rapidly disappear when the obstacles move by Brownian motion. By contrast, mobile obstacles with more confined displacements, e.g., Orstein-Ulhenbeck motion, are shown to preserve subdiffusive regimes. The mean-squared displacement of tracked protein displays convincing power laws with anomalous exponent α that varies with the density of Orstein-Ulhenbeck (OU) obstacles or the relaxation time scale of the OU process. In particular, some of the values we observed are significantly below the universal value predicted for immobile obstacles in two dimensions. Therefore, our results show that subdiffusion due to mobile obstacles with OU type of motion may account for the large variation range exhibited by experimental measurements in living cells and may explain that some experimental estimates are below the universal value predicted for immobile obstacles.

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Year:  2014        PMID: 25353510     DOI: 10.1103/PhysRevE.89.022708

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  7 in total

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Journal:  Biophys J       Date:  2019-06-22       Impact factor: 4.033

2.  Explicit spatiotemporal simulation of receptor-G protein coupling in rod cell disk membranes.

Authors:  Johannes Schöneberg; Martin Heck; Klaus Peter Hofmann; Frank Noé
Journal:  Biophys J       Date:  2014-09-02       Impact factor: 4.033

3.  Controlling Anomalous Diffusion in Lipid Membranes.

Authors:  Helena L E Coker; Matthew R Cheetham; Daniel R Kattnig; Yong J Wang; Sergi Garcia-Manyes; Mark I Wallace
Journal:  Biophys J       Date:  2019-01-16       Impact factor: 4.033

4.  Effects of soft interactions and bound mobility on diffusion in crowded environments: a model of sticky and slippery obstacles.

Authors:  Michael W Stefferson; Samantha L Norris; Franck J Vernerey; Meredith D Betterton; Loren E Hough
Journal:  Phys Biol       Date:  2017-06-29       Impact factor: 2.583

5.  Urea-mediated anomalous diffusion in supported lipid bilayers.

Authors:  E E Weatherill; H L E Coker; M R Cheetham; M I Wallace
Journal:  Interface Focus       Date:  2018-08-17       Impact factor: 3.906

6.  Impact of Feature Choice on Machine Learning Classification of Fractional Anomalous Diffusion.

Authors:  Hanna Loch-Olszewska; Janusz Szwabiński
Journal:  Entropy (Basel)       Date:  2020-12-19       Impact factor: 2.524

7.  Detection of Anomalous Diffusion with Deep Residual Networks.

Authors:  Miłosz Gajowczyk; Janusz Szwabiński
Journal:  Entropy (Basel)       Date:  2021-05-22       Impact factor: 2.524

  7 in total

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