Modeling evolution and immune system by cellular automata

In this review the behavior of two different biological systems is investigated using cellular automata. Starting from this spatially extended approach it is also tried, in some cases, to reduce the complexity of the system introducing mean-field approximation, and solving (or trying to solve) these simplified systems. It is discussed the biological meaning of the results, the comparison with experimental data (if available) and the different features between spatially extended and mean-field versions. The biological systems considered in this review are the following: Darwinian evolution in simple ecosystems and immune system response. In the first section the main features of molecular evolution are introduced, giving a short survey of genetics for physicists and discussing some models for prebiotic systems and simple ecosystems. It is also introduced a cellular automaton model for studying a set of evolving individuals in a general fitness landscape, considering also the effects of co-evolution. In particular the process of species formation (speciation) is described in sect. 5. The second part deals with immune system modeling. The biological features of immune response are discussed, as well as it is introduced the concept of shape space and of idiotypic network. More detailed reviews which deal with immune system models (mainly focused on idiotypic network models) can be found. Other themes here discussed: the applications of CA to immune system modeling, two complex cellular automata for humoral and cellular immune response. Finally, it is discussed the biological data and the general conclusions are drawn in the last section