AWERProcedia Information Technology & Computer Science Vol 04 (2013) 957-963 3 rd World Conference on Innovation and Computer Sciences 2013 Modeling of Dendritic Cell-based vaccination Immunotherapy using Artificial Neural Networks Mohammad Mehrian *, Laboratory of Signals and Electronic Systems, Electrical and Computer Engineering faculty, K. N. Toosi University of technology, Tehran, Iran, 1355-16315. Abazar Arabameri, Laboratory of Signals and Electronic Systems, Electrical and Computer Engineering faculty, K. N. Toosi University of technology, Tehran, Iran, 1355-16315. Alireza Sedghi, Laboratory of Signals and Electronic Systems, Electrical and Computer Engineering faculty, K. N. Toosi University of technology, Tehran, Iran, 1355-16315. Davud Asemani, Laboratory of Signals and Electronic Systems, Electrical and Computer Engineering faculty, K. N. Toosi University of technology, Tehran, Iran, 1355-16315. Suggested Citation: Mehrian M., Arabameri A., Sedghi A. & Asemani D. Modeling of Dendritic Cell-based vaccination Immunotherapy using Artificial Neural Networks. AWERProcedia Information Technology & Computer Science. [Online]. 2013, 04, pp 957-963. Available from: www.awer-center.org/pitcs Received December 17, 2012; revised January 16, 2013; accepted March 13, 2013. Selection and peer review under responsibility of Prof. Dr. FahrettiŶ Sadıkoglu, Near East UŶiǀersitLJ. ©ϮϬϭϯ AĐadeŵiĐ World EduĐatioŶ & ResearĐh CeŶter. All rights reserǀed. Abstract Exposure-response Modeling and Simulation is especially useful in oncology as it permits to predict and design un- experimented clinical trials and dose selection. Dendritic Cells (DC) are the most effective immune cells in the regulation of immune system. In this paper, a model based on Artificial Neural Network (ANN) is presented for analyzing the dynamics of antitumor vaccines using empirical data obtained from the experimentations of different groups of mice treated with DCs matured by bacterial CpG-DNA, LPS and whole lysate of a Gram-positive bacteria Listeria monocytogenes. Simulations show that the proposed model can interpret important features of empirical data. Owing to nonlinearity capability of ANN, the proposed ANN model has been able not only to describe the contradictory empirical results, but also to predict new vaccination patterns for controlling the tumor growth. For example, the proposed model predicts an exponentially-increasing pattern of CpG-matured DC which appears to be effective in suppressing the tumor growth. Keywords: Artificial Neural Network, Dendritic Cells, Immunotherapy, Tumor growth rate; *ADDRESSES FOR CORRESPONDANCE: Mohammad Mehrian, Laboratory of Signals and Electronic Systems, Electrical and Computer Engineering faculty, K. N. Toosi University of technology, Tehran, Iran, 1355-16315. E-mail Address: m_mehrian@ee.kntu.ac.ir