Résumé
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 approach is very flexible and allows us
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), along with the appearance over time of new trajectories and
their death after some lifetime, all of these features possibly depending on
the current spatial configuration of all existing particles. We explain how to
specify all characteristics of a BDM model, with many practical examples that
are relevant for bioimaging applications. 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. We show that the
resulting synthetic sequences exhibit comparable features as those observed in
real microscopy image sequences.