Large scale agent-based modeling of the humoral and cellular immune response

Authors:
Giovanni Stracquadanio;Renato Umeton;Jole Costanza;Viviana Annibali;Rosella Mechelli;Mario Pavone;Luca Zammataro;Giuseppe Nicosia
Affiliations:
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD;Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA;Department of Mathematics and Computer Science, University of Catania, Catania, Italy;Neurology and Centre for Experimental Neurological Therapies, Sapienza University of Rome, Roma, Italy;Neurology and Centre for Experimental Neurological Therapies, Sapienza University of Rome, Roma, Italy;Department of Mathematics and Computer Science, University of Catania, Catania, Italy;Humanitas, University of Milan, Rozzano, Milan, Italy;Department of Mathematics and Computer Science, University of Catania, Catania, Italy
Venue:
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Year:
2011

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Abstract

The Immune System is, together with Central Nervous System, one of the most important and complex unit of our organism. Despite great advances in recent years that shed light on its understanding and in the unraveling of key mechanisms behind its functions, there are still many areas of the Immune System that remain object of active research. The development of in-silico models, bridged with proper biological considerations, have recently improved the understanding of important complex systems [1,2]. In this paper, after introducing major role players and principal functions of the mammalian Immune System, we present two computational approaches to its modeling; i.e., two insilico Immune Systems. (i) A large-scale model, with a complexity of representation of 106 - 108 cells (e.g., APC, T, B and Plasma cells) and molecules (e.g., immunocomplexes), is here presented, and its evolution in time is shown to be mimicking an important region of a real immune response. (ii) Additionally, a viral infection model, stochastic and light-weight, is here presented as well: its seamless design from biological considerations, its modularity and its fast simulation times are strength points when compared to (i). Finally we report, with the intent of moving towards the virtual lymph note, a cost-benefits comparison among Immune System models presented in this paper.