  The characterization of dynamical processes in living systems provides
important clues for their mechanistic interpretation and link to biological
functions. Thanks to recent advances in microscopy techniques, it is now
possible to routinely record the motion of cells, organelles, and individual
molecules at multiple spatiotemporal scales in physiological conditions.
However, the automated analysis of dynamics occurring in crowded and complex
environments still lags behind the acquisition of microscopic image sequences.
Here, we present a framework based on geometric deep learning that achieves the
accurate estimation of dynamical properties in various biologically-relevant
scenarios. This deep-learning approach relies on a graph neural network
enhanced by attention-based components. By processing object features with
geometric priors, the network is capable of performing multiple tasks, from
linking coordinates into trajectories to inferring local and global dynamic
properties. We demonstrate the flexibility and reliability of this approach by
applying it to real and simulated data corresponding to a broad range of
biological experiments.
