A Stochastic Cellular Automata Model of Tumor-immune Interaction

Cancer is a leading cause of death in the world. Mathematical and computer models may improve current treatments by helping scientists better understand this disease. They may also introduce new aspects of therapy by predicting the result of changes in microenvironment of the tumor or the interaction between different types of cells. In this paper, a square lattice Cellular Automata model of tumor-immune cell interaction is presented. The state of each tumor cell can be updated according to stochastic rules related to its previous state and the states of its Moore neighborhood. The growth fraction and necrotic fraction are used as output parameters beside a 2-D graphical growth presentation. Our results show that entering immune system not only improves the compatibility of the model with physiological reality which show the impact of immune cells on tumor invasion, but also the results of output parameters are fitter to experimental data.

Keywords:

Cellular Automata , Immune cells , Tumor growth , Growth fraction , Necrotic fraction