  During developmental processes such as embryogenesis, how a group of cells
fold into specific structures, is a central question in biology that defines
how living organisms form. Establishing tissue-level morphology critically
relies on how every single cell decides to position itself relative to its
neighboring cells. Despite its importance, it remains a major challenge to
understand and predict the behavior of every cell within the living tissue over
time during such intricate processes. To tackle this question, we propose a
geometric deep learning model that can predict multicellular folding and
embryogenesis, accurately capturing the highly convoluted spatial 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.
We successfully use our model to achieve two important tasks, interpretable 4-D
morphological sequence alignment, and predicting local cell rearrangements
before they occur at single-cell resolution. Furthermore, using an activation
map and ablation studies, we demonstrate that cell geometries and cell junction
networks together regulate local cell rearrangement which is critical for
embryo morphogenesis. This approach provides a novel paradigm to study
morphogenesis, highlighting a unified data structure and harnessing the power
of geometric deep learning to accurately model the mechanisms and behaviors of
cells during development. It offers a pathway toward creating a unified dynamic
morphological atlas for a variety of developmental processes such as
embryogenesis.
