Simulation-Based Bioelectronic Modeling for Clustering Cancer Cells by Malignancy in Biosensing Applications

This study presents a novel approach to organic electrochemical transistor (OECT)-based biosensing, integrating unsupervised clustering for cancer cell differentiation. By analyzing impedance and frequency response features, this work demonstrates the ability of OECTs to capture distinct electrical signatures associated with cellular metastatic potential. A synthetic dataset was generated to simulate the electrical behavior of different cell lines, where membrane capacitance, double-layer capacitance, and crossover frequency were identified as key parameters for cell interaction. K-means clustering was employed to identify inherent patterns within the data, revealing distinct groupings of cell states based on electrical properties, which map their metastatic behavior. This proof-of-concept study not only establishes OECTs as a viable tool for cancer cell differentiation but also highlights the transformative potential of machine learning in the development of next-generation biosensing chips for cancer diagnostics and screening.