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Guidelines for the fitting of anomalous diffusion mean square displacement graphs from single particle tracking
Eldad Kepten1, Aleksander Weron2, Grzegorz Sikora2
1Physics Department & Institute of Nanotechnology, Bar Ilan University, Ramat Gan, Israel.
Plos One
|February 14, 2015
Summary
Analyzing single particle tracking data using time-averaged mean square displacement (MSD) can be inaccurate. Simulations reveal guidelines to improve MSD estimation accuracy and identify when MSD is not a suitable estimator for diffusive trajectories.
Area of Science:
- Biophysics
- Complex Systems Analysis
- Statistical Mechanics
Background:
- Single particle tracking is crucial for studying complex systems and biophysics.
- Analysis often relies on time-averaged mean square displacement (MSD) of diffusive trajectories.
- Existing MSD methods suffer from significant errors and biases, hindering experimental comparisons.
Purpose of the Study:
- To establish practical guidelines for accurately estimating anomalous time-averaged MSD.
- To assess the impact of measurement errors and length on MSD estimation precision.
- To identify experimental conditions where time-averaged MSD is an unreliable estimator.
Main Methods:
- Simulated multiple scenarios using fractional Brownian motion, a model for fractional ergodic processes.
- Analyzed the precision and accuracy of fitted MSD across various anomalous exponents.
- Evaluated the influence of measurement length and maximum time lags on MSD estimation.
Main Results:
- Developed precision maps for MSD estimation based on simulation parameters.
- Quantified the impact of anomalous exponents and measurement errors on accuracy.
- Identified specific experimental conditions where time-averaged MSD should be avoided.
Conclusions:
- Provided practical guidelines to enhance the accuracy of single particle tracking studies.
- Demonstrated that time-averaged MSD is not universally applicable as an estimator.
- Highlighted the importance of considering measurement length and errors for reliable diffusive trajectory analysis.
