Immuno fluctuate model as defence system included complexity process

An important mechanism for complexity to emerge spontaneously in the immune system and become diversity in its dynamics is fluctuation. This principle is investigated with reference to the immune-system, which performs defense functions based on self and nonself recognition, and so defends against pathogenic organisms and malignantly transformed cells. A thymus learning system is investigated using the framework of a genetic algorithm based on excitable cellular automaton. We have examined a cellular automaton to describe the T-lymphocyte which operates in the thymus under parasitic infection, and simulated this thymus as the learning system is fluctuating. In this model, we are shown the fluctuate learning rate and the system's inner space (i.e. self) expand on account of the environment (i.e. non-self). Eventually we consider thymus learning system behavior complexity and diversity. As a consequence, this immune fluctuate model could gives the robustness-generative complexity of self-defense which can adapt against irregular, unpredictable and ceaselessly changing environments. Thus our model uncovered the importance of the system's fluctuation in the theoretical immunology.