Anomalous subdiffusion in fluorescence photobleaching recovery: a Monte Carlo study

Area of Science:

• Biophysics
• Cell Biology
• Physical Chemistry

Background:

• Anomalous subdiffusion describes particle movement where mean-square displacement is time-dependent with an exponent less than one.
• This phenomenon is frequently observed for lipids and proteins within cellular plasma membranes.
• Understanding anomalous subdiffusion is crucial for interpreting biological processes at the molecular level.

Purpose of the Study:

• To simulate fluorescence photobleaching recovery (FRAP) experiments to establish optimal data analysis methods for anomalous subdiffusion.
• To evaluate the effectiveness of fitting recovery curves using both conventional and anomalous diffusion models.
• To investigate the utility of log-log plots for assessing the goodness of fit in anomalous diffusion studies.

Main Methods:

• Simulations of fluorescence photobleaching recovery experiments under anomalous subdiffusion conditions.
• Application of both standard and anomalous diffusion equations for fitting recovery data.
• Analysis of data using log-log plots to evaluate model fit quality.
• Consideration of three distinct anomalous subdiffusion models: obstruction, fractional Brownian motion, and continuous-time random walk.

Main Results:

• Simulations indicate that simplified approximate treatments of anomalous subdiffusion generally yield accurate results.
• The choice of model significantly impacts short-time behavior and noise levels.
• Obstructed diffusion near the percolation threshold exhibits increased noise, broader diffusion coefficient distributions, and a reduced mobile fraction.
• Extreme fluctuations in recovery curves at the percolation threshold correlate with geometric variations in the percolation cluster.

Conclusions:

• Fitting FRAP data with both standard and anomalous diffusion models, assessed via log-log plots, is a robust method for analyzing anomalous subdiffusion.
• The simplest approximate treatments are often sufficient for accurate analysis.
• Obstructed diffusion models reveal complex behaviors near percolation thresholds, highlighting the importance of considering model-specific characteristics.