Multicell-Fold: geometric learning in folding multicellular life (arxiv.org)

A groundbreaking study introduces the Multicell-Fold model, a geometric deep learning framework designed to predict how groups of cells fold into complex structures during embryogenesis. This model addresses a long-standing challenge in biology by leveraging a unified graph data structure that captures both cellular interactions and the networks formed by cell junctions. By analyzing multicellular data through granular and foam-like representations, the researchers achieved two critical advancements: precise 4-D morphological sequence alignment and predictions of local cell rearrangements at single-cell resolution. The significance of this work lies in its ability to provide a clearer understanding of morphogenesis, the process by which tissues develop their forms. By demonstrating that cell geometries and junction networks are key regulators of local cellular movement, this approach paves the way for a dynamic morphological atlas applicable across various developmental contexts. The implications for the AI/ML community are profound, as this model not only enhances our ability to simulate biological processes but also exemplifies the potential of geometric deep learning in deciphering complex biological phenomena.