Detection of Transition Times from Single-particle-tracking Trajectories

TL;DR

This paper introduces a new method to detect transition times in diffusivity from single-particle-tracking data, enabling better understanding of heterogeneous environments with fluctuating diffusion properties.

Contribution

The paper presents a novel fluctuation analysis-based method to identify transition times of diffusivity in single-particle trajectories, improving upon existing techniques.

Findings

Successfully detects transition times in simulated data

Accurately estimates time-dependent diffusion coefficients

Applicable to real experimental trajectories

Abstract

In heterogeneous environments, the diffusivity is not constant but changes with time. It is important to detect changes in the diffusivity from single-particle-tracking trajectories in experiments. Here, we devise a novel method for detecting the transition times of the diffusivity from trajectory data. A key idea of this method is the introduction of a characteristic time scale of the diffusive states, which is obtained by a fluctuation analysis of the time-averaged mean square displacements. We test our method in silico by using the Langevin equation with a fluctuating diffusivity. We show that our method can successfully detect the transition times of diffusive states and obtain the diffusion coefficient as a function of time. This method will provide a quantitative description of the fluctuating diffusivity in heterogeneous environments and can be applied to time series with transitions of states.