Artificial Immune Systems
Natural immune theory represents a complex mechanism through which the body defends itself against infection from
various harmful intrusions. This system invokes various response mechanisms, dependent on the pathogen, (intrusive cell)
type, as well as how it infiltrated the body, i.e. either neutralizing the pathogens effect or completely destroying the
intrusive cell. This process is illustrated in the figure below- the complete process from intrusion to solution in the
II. The MHC protein is indexed and passed along the immune system
III. The T-Cell, (white blood cell,) matches the MHC protein of the Antibody and categorises it as either a threat or a safe cell. Because this is an antigen, it is categorised as a threat, and thus the T-Cell is activated, thus commencing the immune response.
IV. The activated T-Cell releases Lymphokines which act as a catalyst for the B-Cell, (also white blood cell.) This catalyst activates the B-Cells.
V. The activated B-Cells then begin the matching process, where matching is the degree by which the antigen matches the B-Cell
VI. The matched B-Cell then produces numerous antibodies of the same genetic makeup that are capable of fighting the pathogenic intrusion/ antigen
VII. The final stage of the immune response where the antibodies bind to the antigen and cure the intrusion by either destroying the cell or neutralizing its effect.
Similar to the Natural immune system, Artificial Immune Systems incorporate the notion of intrusion and solution when understanding, modeling or even defining complexity of a given situation and this is based on varying algorithms deployed. By encoding the relevant data as elements of the immune system, they abstract a pattern matching technique from nature which can be generically applied across various spectra. These techniques/ models are available under different abstractions based on the various aspects of immunology and dependent on the type of data and queries in question; these include- Negative (and Positive) Selection, Danger Models, Clonal Selection and Immune Network Models.