Biomolecule trafficking and network tomography-based simulations

Kervrann, Charles

doi:10.1016/b978-0-12-824349-7.00031-1

Cited by 1 publication

(1 citation statement)

References 47 publications

(59 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(15)(16)(17)(18) Beyond the aforementioned popular softwares, there is already a vast variety of stochastic models introduced in the literature to represent the individual trajectories, allowing for instance for Brownian, confined, anomalous, or directed motions with variable velocities within the cell, (7,8,(19)(20)(21)(22)(23)(24)(25)(26) or even supported along a cytoskeleton network. (20,27,28) However, these dynamics are rarely prone to regime switching, though this feature is often observed in real applications. (22) They also generally assume independence between particles.…”

Section: Introductionmentioning

confidence: 99%

A generative model to simulate spatiotemporal dynamics of biomolecules in cells

Balsollier,

Lavancier,

Salamero

et al. 2023

Biol. Imaging

Self Cite

View full text Add to dashboard Cite

Generators of space-time dynamics in bioimaging have become essential to build ground truth datasets for image processing algorithm evaluation such as biomolecule detectors and trackers, as well as to generate training datasets for deep learning algorithms. In this contribution, we leverage a stochastic model, called birth-death-move (BDM) point process, in order to generate joint dynamics of biomolecules in cells. This particle-based stochastic simulation method is very flexible and can be seen as a generalization of well-established standard particle-based generators. In comparison, our approach allows us: (1) to model a system of particles in motion, possibly in interaction, that can each possibly switch from a motion regime (e.g., Brownian) to another (e.g., a directed motion); (2) to take into account finely the appearance over time of new trajectories and their disappearance, these events possibly depending on the cell regions but also on the current spatial configuration of all existing particles. This flexibility enables to generate more realistic dynamics than standard particle-based simulation procedures, by for example accounting for the colocalization phenomena often observed between intracellular vesicles. We explain how to specify all characteristics of a BDM model, with many practical examples that are relevant for bioimaging applications. As an illustration, based on real fluorescence microscopy datasets, we finally calibrate our model to mimic the joint dynamics of Langerin and Rab11 proteins near the plasma membrane, including the well-known colocalization occurrence between these two types of vesicles. We show that the resulting synthetic sequences exhibit comparable features as those observed in real microscopy image sequences. Impact StatementThe paper presents agenerator of spatio-temporal dynamic for bio-imaging, called the birth-death-move (BDM) model. This stochastic model simulates particle dynamics, accounting for interactions and colocalization. We illustrate the high flexibility of this model by presenting results on real-word image series. Model calibration from real fluorescence microscopy data shows that it faithfully reproduces the joint dynamics of the Langerin and Rab11 proteins.

show abstract