Learning Dynamics from Multicellular Graphs with Deep Neural Networks

TL;DR

This paper demonstrates that graph neural networks can infer the dynamic movement of multicellular groups from static images, aiding understanding of biological processes like development and disease.

Contribution

It introduces a novel application of GNNs to predict multicellular dynamics from static configurations, advancing analysis of biological self-assembly processes.

Findings

GNNs accurately predict cell movement from static data

The method applies to both experimental and synthetic datasets

Structural features indicative of motion are effectively identified

Abstract

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.