Multicell-Fold: geometric learning in folding multicellular life.

ABSTRACT: 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.

SUBMITTER: Yang H 

PROVIDER: S-EPMC11261991 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature