Identifying transport behavior of single-molecule trajectories

Area of Science:

• Biophysics
• Chemical Physics
• Computational Biology

Background:

• Accurate classification of diffusive dynamics is crucial for modeling biological diffusion-reaction systems.
• Distinguishing between Fickian, subdiffusive, and superdiffusive behaviors is essential for understanding molecular transport.
• Existing methods may lack robustness or require extensive data for analyzing complex biological diffusion.

Purpose of the Study:

• To develop and validate a robust method for classifying anomalous diffusion in biological systems using single-molecule trajectories.
• To quantitatively characterize the underlying stochastic processes, including the anomalous scaling exponent.
• To apply the method to experimental data from complex biological environments.

Main Methods:

• Utilized a renormalization group operator to analyze molecular trajectories.
• Developed a classification algorithm to identify anomalous (non-Fickian) diffusion.
• Validated the algorithm using simulated trajectories with known scaling properties.

Main Results:

• The renormalization group operator effectively identifies anomalous diffusion from short molecular trajectories.
• The method provides quantitative insights into the stochastic process and its anomalous scaling exponent.
• Application to experimental data revealed heterogeneous diffusive dynamics in cytoplasm for diffusing microspheres.

Conclusions:

• The developed classification algorithm is simple, robust, and effective for analyzing anomalous diffusion.
• This tool is valuable for studying rare stochastic events in complex biological systems.
• The findings advance the understanding of molecular transport mechanisms in cellular environments.