Model-Assisted Prioritization of Gelatin Methacryloyl (GelMA) Hydrogel Formulations for Three-Dimensional Cell...

Area of Science:

• Biomaterials Science
• Tissue Engineering
• Biotechnology

Background:

• Gelatin methacryloyl (GelMA) hydrogels are crucial for 3D cell culture due to tunable mechanics.
• Limited experimental sampling hinders efficient screening of GelMA formulations.
• Predictive models are needed to optimize hydrogel selection for specific applications.

Purpose of the Study:

• To develop a Bayesian Neural Network (BNN)-based modeling pipeline for GelMA hydrogels.
• To map cross-linking parameters to linear viscoelastic moduli and nonlinear critical stress.
• To categorize GelMA formulations into distinct mechanical windows for efficient screening.

Main Methods:

• Developed a BNN-based modeling pipeline.
• Mapped GelMA cross-linking parameters to mechanical properties (viscoelastic moduli, critical stress).
• Validated the model using C2C12 myoblast and primary cardiomyocyte morphology.

Main Results:

• Categorized GelMA formulations into low, intermediate, and high stable mechanical windows.
• Nonlinear critical stress refined screening priorities, complementing linear rheological parameters.
• Morphological trends of cells validated the predefined mechanical windows, with some ambiguous boundary formulations.

Conclusions:

• The BNN pipeline provides a bounded prioritization strategy for rapid GelMA composition selection under sparse experimental conditions.
• Nonlinear critical stress is a valuable parameter for refining hydrogel screening.
• Further validation is required for generalized predictive use in diverse tissue engineering scenarios.