The transition between immune and disease states in a cellular automaton model of clonal immune response

Abstract

In this paper we extend the Celada-Seiden (CS) model of the humoral immune response to include infectious virus and cytotoxic T lymphocytes (cellular response). The response of the system to virus involves a competition between the ability of the virus to kill the host cells and the host's ability to eliminate the virus. We find two basins of attraction in the dynamics of this system, one is identified with disease and the other with the immune state. There is also an oscillating state that exists on the border of these two stable states.Fluctuations in the population of virus or antibody can end the oscillation and 1 drive the system into one of the stable states. The introduction of mechanisms of cross-regulation between the two responses can bias the system towards one of them. We also study a mean field model, based on coupled maps, to investigate virus-like infections. This simple model reproduces the attractors for average populations observed in the cellular automaton. All the dynamical behavior connected to spatial extension is lost, as is the oscillating feature.Thus the mean field approximation introduced with coupled maps destroys oscillations.

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.…”

“…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. Even so small an "immune system" as a 15 × 15 twodimensional grid is capable of simulating important features of the immune system [32]. Indeed, a C-ImmSim model captures important elements of EBV infection [46].…”

Pathogen responses to host immunity: the impact of time delays and memory on the evolution of virulence

“…To help understand the nature of these interactions, attempts have been made to describe immune function using mathematical models. While some large-scale, biologically realistic simulation models have been developed (Bezzi et al 1997;Kleinstein & Seiden 2000;Kohler et al 2000), arguably most progress has been made through the use of generic, mathematically tractable analytical models that are easy to interpret and provide general insights into the overall behaviour of the system; in particular, the work of Nowak and May (Nowak & May 2000) and Antia and colleagues (Antia & Koella 1994;Antia & Lipsitch 1997;Ganusov et al 2002) have been instrumental in developing this burgeoning field. However, in the desire for simplicity, these models often make limiting assumptions about the nature of the various processes.…”

Selection of escape mutants from immune recognition during HIV infection

“…27,28 In those cases whether the immune system is able to defeat the infection or not depends on the viral duplication rate.…”