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Dense mapping of intracellular diffusion and drift from single-particle tracking data analysis
Abstract : 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.
https://hal.inria.fr/hal-03087048
Contributor : Charles Kervrann Connect in order to contact the contributor
Submitted on : Wednesday, December 23, 2020 - 11:45:47 AM
Last modification on : Wednesday, January 12, 2022 - 12:18:02 PM
Contributor : Charles Kervrann Connect in order to contact the contributor
Submitted on : Wednesday, December 23, 2020 - 11:45:47 AM
Last modification on : Wednesday, January 12, 2022 - 12:18:02 PM
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HAL-DenseMapping-2020.pdf
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