Learning Dynamics from Multicellular Graphs with Deep Neural Networks
Haiqian Yang Florian Meyer Shaoxun Huang Liu Yang Cristiana Lungu Monilola A. Olayioye Markus J. Buehler Ming Guo
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 multicellular
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.