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.

翻译：多细胞自组装形成功能结构是一个动态过程，在胚胎发育、器官形成、肿瘤侵袭等发育与疾病进程中至关重要。从细胞的静态构型推断其集体迁移动力学，对于理解和预测这些复杂过程具有重要价值。然而，识别能够指示多细胞运动的结构特征一直较为困难，现有度量方法主要依赖于物理直觉。本文研究表明，在图神经网络（GNN）的辅助下，无论是实验数据还是合成数据集，均可从细胞位置的静态快照中推断出多细胞集体的运动规律。