Learning collective cell migratory dynamics from a static snapshot with graph neural networks.

ArXiv Pub Date : 2024-11-11
Haiqian Yang, Florian Meyer, Shaoxun Huang, Liu Yang, Cristiana Lungu, Monilola A Olayioye, Markus J Buehler, Ming Guo
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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.

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利用深度神经网络从多细胞图中学习动态。
推断多细胞自组装是理解形态发生(包括胚胎、器官组织、肿瘤等)的核心追求。然而,要找出能显示多细胞动态的结构特征却非常困难。在此,我们提议利用基于图的深度神经网络(GNN)的预测能力,发现可以预测动态的重要图特征。为了证明这一点,我们在实验和模拟中应用了物理信息 GNN(piGNN),通过多细胞集体的位置快照来预测它们的运动性。我们证明 piGNN 能够浏览多细胞生命系统的复杂图谱特征,而经典的机理模型则无法做到这一点。随着多细胞数据量的不断增加,我们建议通过合作建立一个多细胞数据库(MDB),从而构建一个大型多细胞图模型(LMGM),用于多细胞组织的通用预测。
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