Learning Dynamics from Multicellular Graphs with Deep Neural Networks
This addresses a challenge in biology for researchers studying multicellular systems, but it is incremental as it applies an existing method (GNN) to a new domain.
The paper tackled the problem of inferring collective cell migratory dynamics from static configurations, which is valuable for understanding development and disease processes, and showed that a graph neural network can predict motion from a single snapshot in experimental and synthetic datasets.
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.