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Estimation of diffusive states from single-particle trajectory in heterogeneous medium using machine-learning

Yu Matsuda1, Itsuo Hanasaki, Ryo Iwao

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We developed a new hybrid machine learning method to analyze particle movement in complex environments. This approach accurately identifies distinct movement patterns from tracking data, even in challenging conditions.

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Area of Science:

  • Physics
  • Biophysics
  • Computational Science

Background:

  • Analyzing particle movement in complex media is crucial for understanding biological and physical processes.
  • Traditional methods struggle to accurately characterize heterogeneous environments and multiple diffusive states.
  • Single-particle tracking (SPT) generates rich trajectory data but requires sophisticated analysis.

Purpose of the Study:

  • To introduce a novel hybrid machine learning approach for analyzing random walks in heterogeneous media.
  • To accurately determine the number and sequence of diffusive states from particle trajectory data.
  • To assess the method's reliability and applicability to experimental data.

Main Methods:

  • Utilizing a hybrid machine learning framework combining a gamma mixture model and a hidden Markov model.
  • Employing the gamma mixture model to identify the number of distinct diffusive states.
  • Using the hidden Markov model to determine the most probable sequence of these states.
  • Analyzing time-series position data from single-particle tracking (SPT/SMT).

Main Results:

  • The proposed method accurately extracts the number of diffusive states from sufficiently long trajectories.
  • The approach provides an indicator of the suitability of the discrete diffusive states assumption.
  • Successful application demonstrated on both numerically generated and experimentally obtained SPT data.

Conclusions:

  • The hybrid gamma mixture and hidden Markov model approach offers a robust tool for analyzing complex particle dynamics.
  • This method enhances the characterization of heterogeneous media by identifying discrete diffusive states.
  • The technique is validated for its potential in advancing single-particle tracking data analysis.