Modelling thymic functions in a cellular automaton

Abstract

Along the lines developed by Celada and Seiden, for simulating an immune system by means of cellular automata, we have constructed a 'thymus' where T cells undergo positive and negative selection. The populations thus 'matured' have been analyzed and their performance has been tested in machina. The key feature of this thymus is to allow chance meeting and possible interaction between newly born T cells and antigen presenting cells. The latter represent both the epithelial and the dendritic cells of the biological organ and are equipped with MHC molecules that can accommodate selected self peptides. All possible specificities are represented among the virgin T cells entering the thymus, but this diversity is drastically reduced by the time they exit as mature elements. In the model organ the fate of T cells, i.e. whether they will undergo proliferation or apoptosis, is governed by their capacity to recognize MHCs and the affinity of this interaction. Crucial parameters turn out to be the concentration of presenting cells, the number of types of MHC per cell, the 'size of self' in terms of the number of different peptides and their prevalence. According to the results, events in the automaton can realize unforeseen cooperations and competitions among receptors, depending upon the interaction order and frequency, and ultimately determine the rescue or the killing of thymocytes. Thus the making of the mature T repertoire has a random component and cannot be completely predicted.

A virtual look at Epstein–Barr virus infection: Simulation mechanism

“…Many of these have focused on reproducing and understanding important general properties of the immune response, e.g., [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. It is part of the working faith of any investigator who attempts to simulate the immune system that important features of that system's overall behavior emerge from the interactions generated by a small set of simple features among its working parts.…”

The role of mathematical models of host–pathogen interactions for livestock health and production – a review

“…Although restrictive, the mathematical properties of these types of models were well characterised. Several types of CA models have been developed to model different aspects of the immune system Seiden and Celada, 1992;Morpurgo et al, 1995; Zorzenon dos Santos and Coutinho, 2001;Shi et al, 2008) of which the immune simulator IMMSIM ( Kleinstein and Seiden, 2000) is probably the best known. However, most of these CA models involve essential modifications of the original concepts of CA (e.g.…”

Pattern recognition by primary and secondary response of an Artificial Immune System

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