Data-based modeling and prediction of cytotoxicity on microelectronic sensors

This paper is concerned with dynamic modeling, prediction and analysis of cell cytotoxicity. A real-time cell electronic sensing (RT-CES) system has been used for continuously monitoring dynamic cytotoxicity responses of living cells. Cells are grown onto the surfaces of the microelectronic sensors. Changes in cell number expressed as cell index (CI) have been recorded on-line as time series. The CI data are used to develop dynamic prediction models for cell cytotoxicity process. We consider Support Vector Regression (SVR) algorithm to implement data-based system identification. Through several validation studies, multi-step-ahead predictions are calculated and compared with the actual CI. It is shown that SVR-based dynamic modeling has great potential in predicting the cytotoxicity response of the cells in the presence of toxicant.