Dynamics of an artificial immune network

Artificial immune systems (AIS) are adaptive systems developed by taking inspiration from theoretical and experimental immunology and targeted at solving complex problems. Theoretical models are basically aimed at aiding and providing a more qualitative and quantitative description of the biological system they model, sometimes allowing the prediction and design of experiments, and the recovery of information from experimental set-ups. This paper describes the dynamics of one artificial immune network model based on artificial immune systems, named aiNet, and demonstrates through empirical results that even these highly simplified models may present several qualitative behaviours typical of the biological systems and theoretical models; thus being worthy of attention by the more theoretical community. In particular, clonal selection and expansion, affinity maturation, primary, secondary and cross-reactive responses are studied, and demonstrated to be observable in aiNet. Immune network features, such as self-organization, dynamics, connectivity and metadynamics are also discussed.