Learning Dynamics from Multicellular Graphs with Deep Neural Networks
Learning Dynamics from Multicellular Graphs with Deep Neural Networks
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DOI：
10.48550/arxiv.2401.12196
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发表时间：
2024-01
期刊：
影响因子：
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通讯作者：
Haiqian Yang;Florian Meyer;Shaoxun Huang;Liu Yang;C. Lungu;Monilola A. Olayioye;M. Buehler;Ming Guo
中科院分区：
文献类型：
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作者：
Haiqian Yang;Florian Meyer;Shaoxun Huang;Liu Yang;C. Lungu;Monilola A. Olayioye;M. Buehler;Ming Guo
多细胞自组装的推论是理解形态发生的核心任务，包括胚胎、类器官、肿瘤等。然而，识别可以指示多细胞动力学的结构特征非常困难。在这里，我们建议利用基于图的深度神经网络（GNN）的预测能力来发现可以预测动态的重要图特征。为了进行演示，我们应用物理信息 GNN (piGNN) 从实验和模拟中的位置快照来预测多细胞集体的运动性。我们证明 piGNN 能够导航多细胞生命系统的复杂图形特征，这是经典机械模型无法实现的。随着多细胞数据量的增加，我们建议可以共同努力创建一个多细胞数据库（MDB），从中可以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。
The inference of multicellular self-assembly is the central quest of understanding morphogenesis, including embryos, organoids, tumors, and many others. However, it has been tremendously difficult to identify structural features that can indicate multicellular dynamics. Here we propose to harness the predictive power of graph-based deep neural networks (GNN) to discover important graph features that can predict dynamics. To demonstrate, we apply a physically informed GNN (piGNN) to predict the motility of multi-cellular collectives from a snapshot of their positions both in experiments and simulations. We demonstrate that piGNN is capable of navigating through complex graph features of multicellular living systems, which otherwise can not be achieved by classical mechanistic models. With increasing amounts of multicellular data, we propose that collaborative efforts can be made to create a multicellular data bank (MDB) from which it is possible to construct a large multicellular graph model (LMGM) for general-purposed predictions of multicellular organization.