Immune Network Theory
The immune network theories propose as the immune system or IS has a dynamic behaviour even in the absent of external stimuli. This is suggested as the immune cells and molecules are able of recognizing each other, what endows the system along with an Eigen behaviour which is not dependent upon foreign stimulation. Various types of immunologists have refuted this theory, conversely its computational features are applicable and this has proved itself, for computational systems, to be a powerful model.
As per to the immune network theory, the receptor molecules enclosed in the surface of the immune cells represent markers, known idiotopes, that can be recognized by receptors on other immune cells. Such idiotopes are displayed in and or around the similar portions of the receptors that recognize non-self antigens. To illustrate the network theory, suppose that a receptor or antibody Ab1 upon a B-cell recognizes a non-self antigen Ag. Suppose now, that this similar receptor Ab1 recognizes also an idiotope i2 on another B-cell receptor Ab2.
We identify that i2 is a part of Ab2, and Ab1 is able of recognizing both Ag2 andAb. Hence, Ab2 is as be the internal image of Ag, more exactly, i2 is the internal image of Ag. The acknowledgment of idiotopes on a cell receptor by another cell receptors, lead to ever raising sets of linked cell receptors and molecules. This is a network of affinities that are not same from the 'hardwired' network of the nervous system. As a outcome of the network identification events, this was suggested as the identification of a cell receptor by other cell receptor outcomes in network suppression, even as the recognition of an antigen via a cell receptor results in cell proliferation and network activation. The innovative theory did not account explicitly for the results of network suppression or activation, and the different artificial immune networks determined in the literature model it in an exacting form. The pseudo-code of immune system or IS model is presented in Programme no.1.
Repeat 1. Choose an antigen A from PA
As PA = Population of Antigens
2. Take (randomly) R antibodies from PS
As PS = population of Antibodies
3. For each antibody r ∈ R, match it against the chosen antigen A
Calculate its match score for example by utilizing Hamming distance
4. Determine the antibody along with the highest match score
Break ties at random
5. Add match score of winning antibody to its fitness
Till maximum number of cycles is reached
Programme no.1: Pseudo-code of Immune System Model