The introduction of mechanisms of cross-regulation between the two responses can bias the system towards one of them. We also study a mean field model, based on coupled maps, to investigate virus-like infections. This simple model reproduces the attractors for average populations observed in the cellular automaton. All the dynamical behavior connected to spatial extension is lost, as is the oscillating feature. Thus the mean field approximation introduced with coupled maps destroys oscillations.
Computer modelling can be a valuable adjunct in immunological research.
Cellular automata are particularly useful in this regard since they can easily handle the complexities and special cases inherent in the immune system. We describe a generalized cellular automaton that has been designed to model the humoral component of the immune system. The function of the immune system is not only to produce a specific response to any of a zillion possibly dangerous foreign molecules, but also to improve on it by continuously changing the quality of the antibody or effector cell, an important evolutionary measure to stay abreast of the mutability and infectivity of the foreign invaders. In this study, based on experiments conducted in a cellular automaton model of the immune system, we have reproduced affinity maturation of the antibodies and determined the relative roles of affinity selection and hypermutation of antibody genes in bringing about the final result, i.e., optimization of the effector function. We have found that hypermutation is not necessary for affinity maturation if the repertoire of B-cell specificities is sufficiently complete, while in the realistic case of very incomplete repertoires, only mutation can fill in the holes before affinity selection can take place.