
단일 입자 추적 데이터를 이용하여 브라운 운동, 제한 운동, 방향성 운동 등 다양한 운동 유형을 효율적이고 강건하게 분류하고 특성을 정량화할 수 있는 도구를 제시한다.


coremsg

Single-particle tracking is a powerful tool for understanding protein dynamics and characterizing microenvironments. As the motion of unconstrained nanoscale particles is governed by Brownian diffusion, deviations from this behavior are biophysically insightful. However, the stochastic nature of particle movement and the presence of localization error pose a challenge for the robust classification of non-Brownian motion. Here, we present aTrack , a versatile tool for classifying track behaviors and extracting key parameters for particles undergoing Brownian, confined, or directed motion. Our tool quickly and accurately estimates motion parameters from individual tracks and determines their likely motion state. Further, our tool can analyze populations of tracks and determine the most likely number of motion states. We determine the working range of our approach on simulated tracks and demonstrate its application for characterizing particle motion in cells and for biosensing applications. Our tool is implemented as a stand-alone software package, making it simple to analyze tracking data.

### Competing Interest Statement

The authors have declared no competing interest.

Detecting directed motion and confinement in single-particle trajectories using hidden variables

단일-입자-궤적을-이용한-방향성-운동과-제한-탐지


단일 입자 궤적을 이용한 방향성 운동과 제한 탐지
