Quantitative characterization of changes in dynamical behavior for single-particle tracking studies.
Single-particle tracking experiments have been used widely to study the heterogeneity of a sample. Segments with dissimilar diffusive behaviors are associated with different intermediate states, usually by visual inspection of the tracking trace. A likelihood-based, systematic approach is presented to remove this incertitude. Maximum likelihood estimators are derived for the determination of diffusion coefficients. A likelihood ratio test is applied to the localization of the changes in them. Simulations suggest that the proposed procedure is statistically robust and is able to quantitatively recover time-dependent changes in diffusion coefficients even in the presence of large measurement noise.
Montiel
D
D
Department of Chemistry, University of California at Berkeley, Physical Biosciences Division, Lawrence Berkeley National Laboratories, Berkeley, California 94720, USA.
Cang
H
H
Yang
H
H
D000465:Algorithms; D016001:Confidence Intervals; D004058:Diffusion; D016013:Likelihood Functions; D018715:Microscopy, Video; D015233:Models, Statistical; D009038:Motion; D053758:Nanoparticles; D013269:Stochastic Processes