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A sequential algorithm to detect diffusion switching along intracellular particle trajectories.

Vincent Briane1,2, Myriam Vimond2, Cesar Augusto Valades-Cruz3,4

  • 1INRIA, Centre de Rennes Bretagne Atlantique, Serpico Project-Team, Rennes 35042, France.

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Summary
This summary is machine-generated.

This study introduces a new method to detect changes in single-molecule dynamics within living cells. The algorithm accurately identifies temporal change-points in molecular trajectories, crucial for understanding cellular processes.

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Area of Science:

  • Molecular Biology
  • Cellular Dynamics
  • Biophysics

Background:

  • Advances in fluorescence microscopy enable tracking single molecules in living cells.
  • Analyzing molecular trajectories reveals dynamic changes over time.
  • Identifying temporal change-points in these dynamics is essential for biological insights.

Purpose of the Study:

  • To develop a robust method for detecting temporal change-points in single-molecule trajectories.
  • To provide a non-parametric procedure that controls false detections and optimizes parameter selection.
  • To validate the method's performance using simulations and real biological data.

Main Methods:

  • A non-parametric procedure using test statistics on local windows along trajectories.
  • A strategy for aggregating detections from multiple window sizes.
  • Monte Carlo simulations for performance evaluation and comparison with existing algorithms.

Main Results:

  • The proposed algorithm effectively detects temporal change-points in molecular dynamics.
  • It controls false positive rates, particularly for Brownian motion.
  • Demonstrated efficacy on real 2D and 3D data, including mRNA-binding protein motion, endocytosis, and trafficking.

Conclusions:

  • The developed method offers a reliable approach for analyzing single-molecule dynamics.
  • It provides a user-friendly Matlab package for practical application.
  • The findings advance the understanding of molecular mechanisms within living cells.