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Manipulating the three-dimensional (3D) structures of cells is important for facilitating tissue repair and regeneration. We developed a self-assembly system for human mesenchymal stem cells (hMSCs) using cellulose nanofibers (CNFs) and concentrated polymer brushes (CPBs) to fabricate various cell 3D structures. To further generate tissues at an implantable level, it is necessary to carry out a large number of experiments using different cell culture conditions and material properties; however, this is too complex on a practical level. To address this issue, we present a graph neural network-based simulator (GNS) that can be trained using assembly process images to predict the assembly status of future time steps. A total of 24 (25 steps) time-series images were recorded (four repeats for each of the six conditions), and each image was transformed into a graph by regarding the cells as nodes and the connecting neighboring cells as edges. Using the obtained data, the performances of the GNS were examined under three scenarios (i.e., changing a pair of the training or test data) to verify the possibility of using the GNS as a predictor for further time steps. It was confirmed that the GNS could reasonably reproduce the assembly process, even under the toughest scenario wherein the experimental conditions differed between the training and test datasets. This result could reduce the number of experiments required to find the optimal conditions required for generating cells with desired 3D structures. Ultimately, such systems could accelerate progress in regenerative medicine.

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Keyword: Machine learning, Multicellular dynamics, Concentrated polymer brush, Cellulose nanofiber

Conference: The International Symposium on Exponential Biomedical DX 2024 (eMEDX-24) (2024-12-19 - 2024-12-20)

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Manuscript type: Not a journal article

MDR DOI: https://doi.org/10.48505/nims.5860

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Updated at: 2025-11-06 12:30:42 +0900

Published on MDR: 2025-11-06 12:24:51 +0900