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A Radiation Carcinogenesis Model Applied to Radon- Induced Lung Cancer Risk Prediction Using a Sugarscape Cellular Automaton

Author Affiliations

  • 1 Department of Medical Radiation Engineering, Amirkabir University of Technology, Tehran, IRAN
  • 2Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, IRAN
  • 3Radiation Application School, Nuclear Sciences and Technology Research Institute, Tehran, IRAN

Int. Res. J. Biological Sci., Volume 2, Issue (2), Pages 34-39, February,10 (2013)


Exposure to Radon and its decay products is one of the important risks of ionizing radiation from natural sources. It is the second leading cause of lung cancer after smoking in the world. This special characteristic makes an increase in methods and models of lung cancer risk prediction from Radon. In this paper, we present a stochastic cellular automaton based on sugarscape to computational study complex biological effect of radon progeny alpha particles in lung bronchial airways. Our major objective is an assessment of lung cancer risk by following mechanism of cell action in different radiation doses. The model included mechanism of DNA damage induced alpha particles hits and formation of transformation in the lung cells. To achieve our goal, we follow the metabolism rate of infected cell induced alpha particles traversals in sugarscape environment to reach oncogenic transformation. For the first time, a cellular automata model is used to calculate transformation frequency in lung bronchial airways induced Radon and to predict lung cancer risk. The results are validated by a comparison epidemiological data, dosimetric and biological models. It has been shown that the cellular automata using sugarscape model could be a suitable method for cancer risk prediction.


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