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Estimation of general time-varying single particle tracking linear models using local likelihood.

Boris I Godoy1, Nicholas A Vickers1, Y Lin2

  • 1Department of Mechanical Engineering, Boston University, Boston, MA, 02215 USA.

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This study introduces a new method for estimating single particle tracking models with changing parameters. Our approach accurately tracks parameter evolution in real-world conditions, outperforming existing algorithms.

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Area of Science:

  • Physics
  • Biophysics
  • Computational Biology

Background:

  • Single particle tracking (SPT) is crucial for understanding molecular dynamics.
  • Estimating parameters in SPT models can be challenging due to their time-varying nature.
  • Existing methods may struggle with dynamic parameter changes in real-world experimental data.

Purpose of the Study:

  • To develop a general and robust approach for estimating single particle tracking models with time-varying parameters.
  • To improve the accuracy and reliability of parameter estimation in SPT.
  • To provide a method applicable to nonlinear models and real-world experimental conditions.

Main Methods:

  • Utilizing local Maximum Likelihood (ML) estimation with a sliding window approach.
  • Combining local ML with the Expectation Maximization algorithm for iterative parameter estimation.
  • Validating the method using controlled-experimental data with known parameter evolutions.

Main Results:

  • The proposed algorithm successfully tracks time-varying parameters in SPT models.
  • Demonstrated superior performance compared to other similar algorithms in real-world conditions.
  • The method proved effective in capturing parameter changes throughout particle trajectories.

Conclusions:

  • The local ML combined with Expectation Maximization offers a powerful tool for analyzing dynamic SPT data.
  • This approach enhances the understanding of molecular mechanisms by accurately characterizing parameter evolution.
  • The algorithm's robustness and generalizability make it suitable for various applications in biophysics and beyond.