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In this introduction the timeliness and interest of dedicating an issue of Autoimmunity to mostly ''discrete'' models is motivated by highlighting number of circumstances, observations encounters, that all have favored the rise of a family of agent based simulations of the Immune System. Franco Celada was among the first experimentalists to accept the challenge of interdisciplinarity and create a computational Immune System. He thinks that discrete models are especially useful in handling hypotheses: initiating them, representing their consequences, and revealing their plusses and minuses. He is sure that ''looking at'' the immune machinery as a cognitive system is useful both to the intuitive understanding and the creative development of models. [ABSTRACT FROM AUTHOR]

The relationship between biological research and mathematical modeling is complex, critical, and vital. In this review, we summarize the results of the collaboration between two laboratories, exploring the interaction between mathematical modeling and wet-lab immunology. During this collaboration several aspects of the immune defence against viral infections were investigated, focusing primarily on the subject of heterologous immunity. In this manuscript, we emphasize the topics where computational simulations were applied in conjunction with experiments, such as immune attrition, the growing and shrinking of cross-reactive T cell repertoires following repeated infections, the short and long-term effects of cross-reactive immunological memory, and the factors influencing the appearance of new clonal specificities. For each topic, we describe how the mathematical model used was adapted to answer specific biological questions, and we discuss the hypotheses that were generated by simulations. Finally, we propose rules for testing hypotheses that emerge from model experimentation in the wet lab, and vice-versa. [ABSTRACT FROM AUTHOR]

Over the past decade, multi-level complex behavior and reactive nature of biological systems, has been a focus point for the biomedical community. We have developed a computational approach, termed Reactive Animation (RA) for simulating such complex biological systems. RA is an approach for describing the dynamic characteristics of biological systems based on facts collected from experiments. These data are integrated bottom-up by computational tools and methods for reactive systems development and are simulated concomitantly to a front-end user friendly visualization and reporting systems. Using RA, the experimenter may intervene mid-simulation, suggest new hypotheses for cellular and molecular interactions, apply them to the simulation and observe their resulting outcomes ''on-line''. Several RA models have been developed including models of T cell activation, thymocyte development and pancreatic organogenesis, which are describe in the in this review. [ABSTRACT FROM AUTHOR]

The supplanting of the 1°° IgM response by the 2°° isotype-switched response is one of the best known phenomena of immune system dynamics. Given that the conditions determining which B cells will switch isotype and which ones will not are intrinsic to the entities of the immune system, it should be possible to predict the effects that the small-scale (e.g. molecular) properties have on the large scale dynamics of isotype switch. However, in practice, it is notoriously difficult to predict large scale, emergent effects from small scale conditions. Thus, it is unclear what effects (if any), mutation, IgM avidity and chronic immunization exert on isotype switch. To explore these effects, we have constructed a model of isotype switch. With this model, a modified version of the IMMSIM cellular automaton, we are able to alter small scale parameters at will and thus determine the conditions that lead to the observed large scale dynamics of isotype switch. We show that isotype switch is stabilized by high-IgM avidity, affinity/isotype-dependent cell division, and, surprisingly, mutation. We also demonstrate that chronic immunization leads, in our model, to a severe depletion of the IgM response even while the IgG response remains normal. [ABSTRACT FROM AUTHOR]

The present study uses the agent-based model IMMSIM to simulate immune responses to a viral infection, with a focus on the impact of preformed memory (homologous and heterologous) on the quality and the efficacy of the response. The in machina results confirm the observed thwarting of new, naïïve responses exerted by cross-reacting memory, but they also reveal that the competitive inhibition is made possible by the different time frame used by the primary and the secondary response, a well-known fact, epitomized by the interval of about 75 time steps between their peaks. This novel finding justifies the depression of naïïve responses and the long-term consequences it could bring about and the role of memory as a player in a survival of the fittest game. [ABSTRACT FROM AUTHOR]

Experimentation in vitro is a vital part of the process by which the clinical and epidemiological characteristics of a particular influenza virus strain are determined. We detail the considerations which must be made in designing appropriate theoretical/mathematical models of these experiments and show how modeling can increase the information output of such experiments. Starting from a traditional system of ordinary differential equations, common to infectious disease modeling, we broaden the approach by using an agent-based model, applicable to more general experimental geometries and assumptions about the biological properties of viruses, cell and their interaction. Within this framework, we explore the limits of the assumptions made by more traditional models and the conditions under which these assumptions begin to break down, requiring the use of more sophisticated models. We apply the agent-based model to experimental plaque growth of two influenza strains, one resistant to the antiviral oseltamivir, and extract the values of key infection parameters specific to each strain. [ABSTRACT FROM AUTHOR]