Measurements and System Identification for the Characterization of Smooth Muscle Cell Dynamics
<|reference_start|>Measurements and System Identification for the Characterization of Smooth Muscle Cell Dynamics: Biological tissue integrity is actively maintained by cells. It is essential to comprehend how cells accomplish this in order to stage tissue diseases. However, addressing the complexity of a cell's system of interrelated mechanisms poses a challenge. This necessitates a well-structured identification framework and an effective integration of measurements. Here we introduce the use of state-of-the-art frequency-domain system identification techniques combined with an indentation measurement platform to analyze the underlying mechanisms from the perspective of control system theory. The ultimate goal is to explore how mechanical and biological factors are related in induced Pluripotent Stem Cell-derived vascular smooth muscle cells. We study on the frequency-domain analysis for the investigation and characterization of cellular dynamics of smooth muscle cells from the measured data. The measurement model in this study exploits the availability of human tissue and samples, enabling fundamental investigations of vascular tissue disease. This approach using human cell lines holds significant potential to decrease the necessity for animal-based safety and efficacy studies. The focus of this review is to investigate the cellular dynamics underlying the myogenic response and to demonstrate the practicability of employing a nano-indentation measurement setup for the broadband frequency-domain characterization of induced Pluripotent Stem Cell-derived vascular smooth muscle cells.<|reference_end|>
@article{ozturk2024measurements,
    title={Measurements and System Identification for the Characterization of
  Smooth Muscle Cell Dynamics},
    author={Dilan Ozturk, Pepijn Saraber, Kevin Bielawski, Alessandro Giudici,
  Leon Schurgers, Koen Reesink, Maarten Schoukens},
    journal={2024 IEEE International Symposium on Medical Measurements and
  Applications (MeMeA)},
    year={2024},
    doi={10.1109/MeMeA60663.2024.10596921},
    archivePrefix={arXiv},
    eprint={2409.16825},
    primaryClass={eess.SY cs.SY math.DS}
}