First passage time statistics of non-Markovian random walker: Dynamical response approach

Sakamoto, Yuta; Sakaue, Takahiro

doi:10.1103/physrevresearch.5.043148

Phys. Rev. Research

2023

DOI: 10.1103/physrevresearch.5.043148

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First passage time statistics of non-Markovian random walker: Dynamical response approach

Yuta Sakamoto,

Takahiro Sakaue

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2024

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Cited by 3 publications

References 34 publications

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Method of filtration in first passage time problems

Sakamoto,

Sakaue

2024

J. Phys. A: Math. Theor.

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Statistics of stochastic processes crucially depends on the imposed boundary condition. In one spatial dimension, for example, first passage time distribution in semi-infinite space (one absorbing boundary) is markedly different from that in a finite interval with two absorbing boundaries. Here we propose a method, which we call method of filtration, that allows us to construct the latter from the knowledge of the former only. We argue that our method is a sort of generalization of the method of image.We also demonstrate that in terms of the performance, it is complementary to another standard method based on the eigenfunction expansion solution of the Fokker-Planck equation.

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Method of filtration in first passage time problems

Sakamoto,

Sakaue

2024

J. Phys. A: Math. Theor.

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Chromatin Remodeling Due to Transient-Link-and-Pass Activity Enhances Subnuclear Dynamics

Das,

Sakaue,

Shivashankar

et al. 2024

Phys. Rev. Lett.

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Spatiotemporal coordination of chromatin and subnuclear compartments is crucial for cells. Numerous enzymes act inside nucleus-some of those transiently link and pass two chromatin segments. Here, we study how such an active perturbation affects fluctuating dynamics of an inclusion in the chromatic medium. Using numerical simulations and a versatile effective model, we categorize inclusion dynamics into three distinct modes. The transient-link-and-pass activity speeds up inclusion dynamics by affecting a slow mode related to chromatin remodeling, viz., size and shape of the chromatin meshes.

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Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches

Schirripa Spagnolo,

Luin

2024

IJMS

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Single-particle tracking is a powerful technique to investigate the motion of molecules or particles. Here, we review the methods for analyzing the reconstructed trajectories, a fundamental step for deciphering the underlying mechanisms driving the motion. First, we review the traditional analysis based on the mean squared displacement (MSD), highlighting the sometimes-neglected factors potentially affecting the accuracy of the results. We then report methods that exploit the distribution of parameters other than displacements, e.g., angles, velocities, and times and probabilities of reaching a target, discussing how they are more sensitive in characterizing heterogeneities and transient behaviors masked in the MSD analysis. Hidden Markov Models are also used for this purpose, and these allow for the identification of different states, their populations and the switching kinetics. Finally, we discuss a rapidly expanding field—trajectory analysis based on machine learning. Various approaches, from random forest to deep learning, are used to classify trajectory motions, which can be identified by motion models or by model-free sets of trajectory features, either previously defined or automatically identified by the algorithms. We also review free software available for some of the analysis methods. We emphasize that approaches based on a combination of the different methods, including classical statistics and machine learning, may be the way to obtain the most informative and accurate results.

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First passage time statistics of non-Markovian random walker: Dynamical response approach

Cited by 3 publications

References 34 publications

Method of filtration in first passage time problems

Method of filtration in first passage time problems

Chromatin Remodeling Due to Transient-Link-and-Pass Activity Enhances Subnuclear Dynamics

Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches

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