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Modelling danger and anergy in artificial immune systems

Cayzer, Steve; Sullivan, Julie


Keyword(s): artificial immune systems; danger theory; anergy; AIS; immune systems; autoimmune disease; false positives

Abstract: Artificial Immune Systems are engineering systems which have been inspired from the functioning of the biological immune system. We present an immune system model which incorporates two biologically motivated mechanisms to protect against autoimmune reactions, or false positives. The first, anergy, has been subject to the intense focus of immunologists as a possible key to autoimmune disease. The second is danger theory, which has attracted much interest as a possible alternative to traditional self-nonself selection models. We adopt a published immunological model, validate and extend it. Using the same calculations and assumptions as the original model, we integrate danger theory into the software. Without anergy, both models - the original and the danger model - produce similar results. When anergy is added, both models' performance improves. However, there seems to be some synergy between the mechanisms; anergy has a greater effect on the danger model than the original model. These findings should be of interest both to AIS practitioners and to the immunological community. Publication Info: GECCO 2007 Genetic and Evolutionary Computation Conference, July 7-11 2007, London, UK

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