A Stochastic Model of the Effector T Cell Lifecycle
The dynamics of immune response to initial infection and reinfection by the same pathogen sometime later, are considerably different. Primary response, which follows initial infection, is characterised by relatively slow precursor cell activation and population growth rates, with a consequent elongated pathogen clearance profile, typically extended over six days or more. On the other hand, secondary response (to reinfection by the same pathogen some time later) is notable for short effector activation time, high specificity of response, rapid pathogen elimination and high degree of memory cell participation. In this paper, we present a seven state non-deterministic finite automata (NFA) of the effector T cell lifecycle, which is encoded as a set of states and state transitions. Our objective is to study the degree to which variable infection outcome is dependent on the accumulation of chance events. Such chance events may be represented as the consequence of premature, delayed or even failed state transitions. We show how small variation in crucial state transitions probabilities during the lifecycle can induce widely variable infection outcomes. This model is implemented as a spatially extended, concurrent two-dimensional stochastic cellular automata, executing on a MPI-based Linux cluster.
KeywordsCellular Automaton Memory Cell Primary Response Cellular Automaton Secondary Response
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