Immune response via interacting three dimensional network of cellular automata

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Abstract
A computer simulation is used to study the dynamics of neural-network like system of three cell types on a cubic lattice in an attempt to understand the immune response (a) in autoimmune disease and (b) in immune weakness each with two kind of independent interactions. In case (a) the number of infected sites seems to grow with (time )3.3. The saturation time required to infect all the sites varies roughly, with the initial concentration p of the cells, as p-0.3 for all the cell types except for the activated killer cells where it varies as p-0.5 with one particular interaction. In case (b), the evolution of infected cells is studied for binary mixtures of random interactions of strength B. The number of viral infected cells grows and the number of T4-cells decreases monotonically on increasing the interaction intensity B. For a special case of annealed random interaction, an anomaly is observed in the variation of the T4-cells as a function of time in a narrow regime of B (near B = 0.9)

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