Data-based modeling and prediction of cytotoxicity induced by contaminants in water resources

This paper is concerned with dynamic modeling, prediction and analysis of cell cytotoxicity induced by water contaminants. 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 for dynamic modeling and prediction of cytotoxicity. Through several validation studies, multi-step-ahead predictions are calculated and compared with the actual CI obtained from experiments. It is shown that SVR-based dynamic modeling has great potential in predicting the cytotoxicity response of the cells in the presence of toxicant.

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► We model dynamic cytotoxicity responses of living cells to 3 potential water toxicants.
► Support vector regression (SVR) is applied to develop nonlinear dynamic local models.
► The local SVR-based models demonstrate better predictive performance than the ANNs.
► The local SVR-based models are more robust to the increase of prediction horizons.
► SVR-based dynamic modeling has great potential in predicting cytotoxicity responses.