2024/07/09 by Haiqian Yang, Anh Quoc Nguyen, Yang, Haiqian +8 · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Biological Physics (physics.bio-ph) #Cell Image Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Soft Condensed Matter (cond-mat.soft) #cond-mat.soft #cs.LG #physics.bio-ph
paper · pdf · doi:10.48550/arxiv.2407.07055
openalex publication_date 2024/07/09 · arxiv published 2024/07/09 · arxiv updated 2024/07/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
During developmental processes such as embryogenesis, how a group of cells fold into specific structures, is a central question in biology. However, it remains a major challenge to understand and predict the behavior of every cell within the living tissue over time during such intricate processes. Here we present a geometric deep-learning model that can accurately capture the highly convoluted interactions among cells. We demonstrate that multicellular data can be represented with both granular and foam-like physical pictures through a unified graph data structure, considering both cellular interactions and cell junction networks. Using this model, we achieve interpretable 4-D morphological sequence alignment, and predicting cell rearrangements before they occur at single-cell resolution. Furthermore, using neural activation map and ablation studies, we demonstrate cell geometries and cell junction networks together regulate morphogenesis at single-cell precision. This approach offers a pathway toward a unified dynamic atlas for a variety of developmental processes.