Multicell-Fold: geometric learning in folding multicellular life.
ArXiv
; 2024 Jul 22.
Article
in En
| MEDLINE
| ID: mdl-39040638
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. 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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
ArXiv
Year:
2024
Document type:
Article
Affiliation country:
Estados Unidos
Country of publication:
Estados Unidos