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Dense mapping of intracellular diffusion and drift from single-particle tracking data analysis

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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.
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Dates and versions

hal-03087048 , version 1 (23-12-2020)

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  • HAL Id : hal-03087048 , version 1

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Antoine Salomon, Cesar Augusto Valades Cruz, Ludovic Leconte, Charles Kervrann. Dense mapping of intracellular diffusion and drift from single-particle tracking data analysis. ICASSP 2020 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2020, Barcelona, Spain. pp.1-5. ⟨hal-03087048⟩
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