Dense mapping of intracellular diffusion and drift from single-particle tracking data analysis
Résumé
It is of primary interest for biologists to be able to visualize the dynamics of proteins within the cell. In this paper, we propose a new mapping method to robustly estimate dynamics in the entire cell from particle tracks. To obtain satisfying
diffusion and drift maps, we use a spatiotemporal kernel estimator. Trajectory classification data is used as input and
allows to automatically label particle movements into three classes: confined motion (or subdiffusion), Brownian motion, and directed motion (or superdiffusion). We then use this information to calculate diffusion coefficient and drift maps
separately on each class of motion.
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