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Learning Dynamics from Multicellular Graphs with Deep Neural Networks.

ABSTRACT: 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.

SUBMITTER: Yang H 

PROVIDER: S-EPMC10854275 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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

Yang Haiqian H   Meyer Florian F   Huang Shaoxun S   Yang Liu L   Lungu Cristiana C   Olayioye Monilola A MA   Buehler Markus J MJ   Guo Ming M  

ArXiv 20241111

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 largel  ...[more]

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