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Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

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Estimating the anomalous diffusion exponent for single particle tracking data with measurement errors - An

Krzysztof Burnecki¹, Eldad Kepten², Yuval Garini²

¹Hugo Steinhaus Center, Wroclaw University of Technology, Wyspianskiego 27, 50-370 Wroclaw, Poland.

Scientific Reports

|June 13, 2015

Summary

This study introduces the fractionally integrated moving average (FIMA) process to accurately characterize anomalous diffusion in biophysics, even with measurement errors. FIMA outperforms traditional methods for analyzing single particle tracking data.

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Area of Science:

Biophysics
Statistical Physics
Data Analysis

Background:

Anomalous diffusion characterization is crucial in biophysics.
Measurement errors complicate single-trajectory analysis using time-averaged mean square displacement (TAMSD).

Purpose of the Study:

To introduce and evaluate the fractionally integrated moving average (FIMA) process as a model for anomalous diffusion with measurement errors.
To compare the efficacy of FIMA estimators against traditional TAMSD estimators for anomalous diffusion exponents.

Main Methods:

Simulated fractional Brownian motion with Gaussian white noise.
Applied FIMA process and TAMSD estimators to analyze simulated data.
Compared performance across various anomalous exponents and noise levels.

Main Results:

The FIMA framework effectively characterizes dynamics across a wide range of anomalous exponents and noise levels.
FIMA estimation demonstrates superiority over TAMSD in numerous scenarios, particularly with high measurement errors.

Conclusions:

The FIMA process provides a robust method for analyzing anomalous diffusion data corrupted by measurement errors.
This approach is expected to advance single particle tracking (SPT) experiments by enabling reliable measurements under challenging conditions.