  Multicellular self-assembly into functional structures is a dynamic process
that is critical in the development and diseases, including embryo development,
organ formation, tumor invasion, and others. Being able to infer collective
cell migratory dynamics from their static configuration is valuable for both
understanding and predicting these complex processes. However, the
identification of structural features that can indicate multicellular motion
has been difficult, and existing metrics largely rely on physical instincts.
Here we show that using a graph neural network (GNN), the motion of
multicellular collectives can be inferred from a static snapshot of cell
positions, in both experimental and synthetic datasets.
