Non-linear Characterization of Commercial and Decellularized Hydrogels: Statistical Framework Enhanced by Bayesian Optimization

Abstract Scope Hydrogels are widely used in the design of tissue substitutes because of their ability to mimic the extracellular matrix (ECM). Their mechanical cues critically influence the cellular response, making accurate characterization essential. However, it remains challenging due to their intricate nature. This study computationally evaluates the hyperelastic properties of next-generation hydrogels of high biomedical interest, including basal membrane extract and decellularized liver matrices, as well as structural proteins. Methods We present a combined framework based on Bayesian optimization and statistical analyses that go beyond classical least-squares fitting, leveraging rheological experimental data. It defines each hyperelastic strain-energy density function, and addresses both intra- and inter-sample variability. This approach quantifies uncertainty and reveals the natural variability that deterministic models overlook, and it also enables quantification of coefficient variation with composition. Results Validation against experimental data shows computational fits of 5% error in most cases, and low calculation time. Analyses reveal that composition—collagen addition, fibrin concentration, and decellularized extracellular matrix (dECM) age—modulate initial stiffness, nonlinearity, and overall mechanical resistance of the hydrogels. Conclusion Hydrogels derived from basal membrane extracts exhibit comparable non-linear mechanics, while collagen addition reduces stiffness and nonlinearity. In fibrin-based hydrogels with decellularized liver matrix, mechanical behavior is concentration and age-dependent. Such insights are highly relevant for mechanobiology, enabling prediction of how cells sense mechanical cues and scaffold composition influences in vivo interactions.