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

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.