Comparison of Black-Box Nonlinear System Identification Methods for Smooth Muscle Cell Dynamics^*

Cells play a vital role in maintaining tissue integrity, making it essential to understand their mechanisms for effectively staging vascular tissue diseases. However, addressing the complexity of cellular systems remains challenging and requires a structured identification framework. To meet this need, an indentation measurement setup is employed, utilizing accessible human tissue samples to investigate smooth muscle cell dynamics from a control systems perspective. This paper compares multiple black-box identification approaches to accurately model smooth muscle cell behavior and evaluate each models’ performance in capturing system dynamics. The effectiveness of various techniques is demonstrated on measured data, and the results indicate a promising approach for modeling complex biological systems, with potential applications in vascular smooth muscle cell mechanobiology.