Latest Immune Models and Hybrid Approaches - Immunological Computation: Theory and Applications - page 153

Chapter 6
Latest Immune Models
and Hybrid Approaches
h is chapter covers some immune-computing concepts, which have not been well
developed yet but appear to have potential in providing more insights of immune
processes. One such model, which has gained a lot of attention recently is the so-
called danger theory (DT) (Matzinger, 1994, 2002). h us, this chapter introduces
the main ideas behind DT and briefl y describes some computational models based
on the DT inspired by dendritic cells (DCs) functionalities. Other recent works
presented include a multilevel immune algorithm, major histocompatibility com-
plex (MHC)-based approaches, and cytokine networks. h e last section discusses
a hybrid approach, which combines negative selection (NS) and neural network
methods to design a classifi cation algorithm.
6.1 The Danger Theory
h e DT (Matzinger, 1994) states that the immune system is activated on receipt
of molecular signals, which indicate damage (or stress) to the body rather than by
pattern matching of “nonself ” versus “self.” Accordingly, distressed cells and tissues
transmit danger signals, which results in capturing antigens by antigen-presenting
cells (APCs) such as macrophages; APCs then travel to the local lymph node and
present the antigen to lymphocytes. Essentially, a danger zone exists around each
danger signal. h erefore, only those B cells whose antibodies match antigens in the
danger zone will get stimulated and then will undergo clonal expansion. Figure 6.1
illustrates immune response described by the DT.
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