Skip to Main content Skip to Navigation
Conference papers

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.
Complete list of metadata

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

File

HAL-DenseMapping-2020.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03087048, version 1

Citation

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⟩

Share

Metrics

Les métriques sont temporairement indisponibles