Computational Biology and Chemistry 35 (2011) 69–80 Contents lists available at ScienceDirect Computational Biology and Chemistry journal homepage: www.elsevier.com/locate/compbiolchem Research article Data-based modeling and prediction of cytotoxicity induced by contaminants in water resources S. Khatibisepehr a , B. Huang a,∗ , F. Ibrahim a , J.Z. Xing b , W. Roa c a Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2G6, Canada b Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 2S2, Canada c Division of Radiation Oncology, Cross Cancer Institute, Edmonton, AB T6G 2V4, Canada article info Article history: Received 2 October 2010 Received in revised form 27 January 2011 Accepted 21 February 2011 Keywords: Cytotoxicity monitoring Cell population dynamic modeling Support vector regression Water protection Early warning abstract 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 micro- electronic 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. © 2011 Elsevier Ltd. All rights reserved. 1. Introduction Chemical disinfection of water was a major public health tri- umph of the 20th century. Yet, the ever-increasing number of chemical compounds produced by various process industries has prompted the development of research methods for rapid cytotox- icity screening to enhance water quality monitoring. There are several methods for early warning monitoring to detect hazardous events in water supplies (Hasan et al., 2004). Two representative ones are analytical–chemical approach and biolog- ical approach. Analytical–chemical methods can detect a specific compound or a range of compounds having similar properties. This approach does not necessarily by itself give information about bioavailability and possible toxic effects (Brosnan, 1999). There- fore, the main weakness of the analytical–chemical approach is its inability to directly detect the toxicant effect on the living mech- anisms. On the other hand, biological early warning systems or bioalarming systems are capable of signaling hazardous events and directly detecting the effect of the events, regardless of type and concentration of the substances. However, by its nature, bioalarm- ing systems through the responses of living organisms give more false positives, and installation of such a system implies the accep- tance of a certain risk of false positive. The limitations of the ∗ Corresponding author. Tel.: +1 780 492 9016. E-mail address: biao.huang@ualberta.ca (B. Huang). bioalarming system are therefore its uncertainty, complexity to track down, and slow response. Clearly, an early warning system that is capable of quick and reliable detection of the hazardous effects is yet desirable. During the past few decades, applications of mathematical modeling in the assessment of water quality have been widely investigated by Clark et al. (1986), Mazijk (1996) and others. In recent years, mathematical models have been established as a valuable supplement to the classical methods for online water quality monitoring (Yang et al., 2008). Water contaminants have two major effects on human cells, namely, toxicity effects (cell killing by apoptosis and/or necrosis) and cancer effects (uncontrolled cell proliferation caused by can- cer contaminants stimulations). In order to obtain predictions of these effects for early warning purposes, mathematical models can be developed to describe these effects on human cells. The models so obtained are able to predict cell responses to different values of toxicant concentration and to allow assessment of the biological consequences of toxic chemicals in environmental contamination (Ibrahim et al., 2010). The main objective of this work is thus to develop dynamic mathematical models to obtain predictions of cytotoxicity effects on living cells caused by certain water contam- inants. Cytotoxicity is the degree to which an agent possesses a spe- cific toxic action on living cell referring to cells killing, cell lysis and certain cellular pathological changes, such as cellular morphologi- cal and adhesion change, induced by toxic agents. When exposing to toxic compounds, cells undergo physiological and patholog- 1476-9271/$ – see front matter © 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.compbiolchem.2011.02.001