Non-Markovian Persistent Random Walk Model for Intracellular Transport

Nickolay Korabel*, Hamed Al Shamsi, Alexey O. Ivanov, Sergei Fedotov*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Transport of vesicles and organelles inside cells consists of constant-speed bidirectional movement along cytoskeletal filaments interspersed by periods of idling. This transport shows many features of anomalous diffusion. In this paper, we develop a non-Markovian persistent random walk model for intracellular transport that incorporates the removal rate of organelles. The model consists of two active states with different speeds and one resting state. The organelle transitions between states with switching rates that depend on the residence time the organelle spends in each state. The mesoscopic master equations that describe the average densities of intracellular transport in each of the three states are the main results of the paper. We also derive ordinary differential equations for the dynamics for the first and second moments of the organelles’ position along the cell. Furthermore, we analyse models with power-law distributed random times, which reveal the prevalence of the Mittag-Leffler resting state and its contribution to subdiffusive and superdiffusive behaviour. Finally, we demonstrate a non-Markovian non-additivity effect when the switching rates and transport characteristics depend on the rate of organelles removal. The analytical calculations are in good agreement with numerical Monte Carlo simulations. Our results shed light on the dynamics of intracellular transport and emphasise the effects of rest times on the persistence of random walks in complex biological systems.

Original languageEnglish
Article number758
JournalFractal and Fractional
Volume7
Issue number10
DOIs
Publication statusPublished - Oct 15 2023

Keywords

  • integro-differential equations
  • intracellular transport
  • subdiffusion
  • superdiffusion

ASJC Scopus subject areas

  • Analysis
  • Statistical and Nonlinear Physics
  • Statistics and Probability

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