Using Cellular Automata to Learn About the Immune System

Abstract

Cellular automata are very simple systems that can exhibit complex dynamics on its time evolution. Over the last decade there have been many applications of cellular automata to modeling of biological systems. Those applications have been stimulated by the study of complex systems which has brought many insights into the cooperative and global behavior of the biological systems. Along with this discussion we present two different applications of deterministic and also of probabilistic cellular automata that are used to model the dynamics involved in cooperative and collective behavior of the immune system. In the first example, we use a deterministic cellular automata to model the time evolution of the immune repertoire, as a network, according to the Jerne's theory. Using this model we could reproduce some recent experimental results about immunization and aging of the immune system. In the second example, we use a probabilistic cellular automata model to study the evolution of HIV infection and the onset of AIDS. The results are in excellent agreement with experimental data obtained from infected patients. Besides the examples above, other interesting applications, such as models for cancer and recurrent epidemics, are being considered in the present framework.