Self/non-self Detection Modeling of Immune System Based on Normal Model of DNA Pattern

A novel modeling approach and its computational technique of self/non-self detection in the immune system were proposed to attain 100% detection rate theoretically. The modeling of self/non-self detection was based on the normal model of the immune system. Moreover, the normal model was uniquely identified by the space-time properties of each immune component for the immune system. The components included some immune cells and molecules such as antibodies. For the two properties, the space property was the unique DNA pattern of the component and the time property was the time state of the component. Through proving with a theorem, the normal state of each component was uniquely identified by the space-time properties of the component. The immune system was comprised of three tiers, i.e. innate immune tier, adaptive immune tier and parallel immune cell tier. The innate immune tier was used to detect all self/non-self with the normal model of the immune system. Traditional non-self detection approaches detected the non-self through matching the feature information of all known non-self in the knowledge base of the immune system. Based on the normal model, the novel non-self detection approach detected the non-self through matching the space-time properties of the self in the self database, which was mapped from the normal model. Because all the self was known, the detection of all self was easy and its detection rate could be 100%. Furthermore, if an antigen was not determined as a self, then the antigen was sure a non-self. Therefore, the non-self detection also became easy and its detection rate could also be 100%. At last, the visual result of the simulation shows that the self/non-self detection approach on the normal model can provide an effective way of simulating the complex immune system.