Multiformalism modeling and simulation of immune system mechanisms

The immune system (IS) represents a complex network of cells and molecules devoted to the protection of individuals from external pathogens, and in terms of complexity, it is only second to the central nervous system. As our knowledge of the IS mechanisms has become more exhaustive, interest has grown in applying modeling and simulation techniques in this context. In particular, among these techniques, the Agent Based Models (ABMs) have been increasingly applied for the IS simulation. One of the major drawbacks of ABMs is represented by the lack of well-defined semantics, which may lead to inconsistent results in comparison to other stochastic approaches. In this paper, we make use of the well-defined semantics and the simulation algorithm for ABMs that we proposed in [1] to implement a few models of the Cancer-Immune System. Comparing ABMs and Gillespie’s Stochastic Simulation Algorithm results we show that our methodology brings coherence among the results of ABMs and SSA.