Learning Dynamics from Multicellular Graphs with Deep Neural Networks
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915013632983040 |
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| author | Yang, Haiqian Meyer, Florian Huang, Shaoxun Yang, Liu Lungu, Cristiana Olayioye, Monilola A. Buehler, Markus J. Guo, Ming |
| author_facet | Yang, Haiqian Meyer, Florian Huang, Shaoxun Yang, Liu Lungu, Cristiana Olayioye, Monilola A. Buehler, Markus J. Guo, Ming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12196 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Dynamics from Multicellular Graphs with Deep Neural Networks Yang, Haiqian Meyer, Florian Huang, Shaoxun Yang, Liu Lungu, Cristiana Olayioye, Monilola A. Buehler, Markus J. Guo, Ming Biological Physics Soft Condensed Matter Machine Learning 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. |
| title | Learning Dynamics from Multicellular Graphs with Deep Neural Networks |
| topic | Biological Physics Soft Condensed Matter Machine Learning |
| url | https://arxiv.org/abs/2401.12196 |