Learning Dynamics from Multicellular Graphs with Deep Neural Networks

Haiqian Yang, Florian Meyer, Shaoxun Huang, Liu Yang, Cristiana Lungu +3 more
1/22/2024
physics.bio-phcond-mat.softcs.LG

No official code found. Best implementation: GuoLab-CellMechanics/GNN-collective-cell-dynamics (9 stars, MIT, last commit 2024-11-11).

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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Code Implementations(1)

GuoLab-CellMechanics/GNN-collective-cell-dynamicsCommunity100%
MIT
92PythonJul 2, 20241 years agoMIT

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Cite this paper

@article{yang2024learning,
  title  = {Learning Dynamics from Multicellular Graphs with Deep Neural Networks},
  author = {Haiqian Yang and Florian Meyer and Shaoxun Huang and Liu Yang and Cristiana Lungu and Monilola A. Olayioye and Markus J. Buehler and Ming Guo},
  year   = {2024},
  eprint = {2401.12196},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.12196v3}
}

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