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Graph Network-Based Simulation of Multicellular Dynamics Driven by Concentrated Polymer Brush-Modified Cellulose
Chiaki Yoshikawa1, Duc Anh Nguyen2, Tadashi Nakaji-Hirabayashi3,4
1Research Center for Functional Materials, National Institute for Materials Science (NIMS), Tsukuba, Ibaraki 305-0047, Japan.
ACS Biomaterials Science & Engineering
|March 28, 2024
Summary
A graph neural network simulator predicts cell assembly for tissue regeneration. This computational tool reduces experiments needed to create 3D cell structures, accelerating regenerative medicine advancements.
Area of Science:
- Biomaterials Science
- Computational Biology
- Regenerative Medicine
Background:
- 3D cell structure manipulation is key for tissue repair and regeneration.
- Current methods require extensive experimentation for optimal cell assembly using cellulose nanofibers (CNFs) and concentrated polymer brushes (CPBs).
Purpose of the Study:
- To develop a computational tool for predicting 3D cell assembly dynamics.
- To reduce the experimental burden in optimizing conditions for engineered tissues.
Main Methods:
- A graph neural network simulator (GNS) was developed, trained on time-series images of cell self-assembly.
- Images were converted into graphs, with cells as nodes and connections as edges.
- GNS performance was evaluated under varying training and testing data conditions.
Main Results:
- The GNS accurately predicted future cell assembly states, even with differing experimental conditions between training and testing data.
- The simulator successfully predicted cell types 3 weeks post-assembly based on initial 24-hour images.
- The GNS demonstrated robust performance across tested scenarios.
Conclusions:
- The developed GNS can effectively predict complex cell assembly processes.
- This approach significantly reduces the number of experiments needed for optimizing 3D cell structure fabrication.
- The GNS has the potential to accelerate progress in regenerative medicine by streamlining tissue engineering workflows.
