Large Scale Agent-Based Modeling of the Humoral and Cellular Immune Response
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Large Scale Agent-Based Modeling of the Humoral and Cellular Immune Response.pdf
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Author(s)
Stracquadanio, Giovanni
Umeton, Renato
Costanza, Jole
Annibali, Viviana
Mechelli, Rosella
Pavone, Mario
Zammataro, Luca
Nicosia, Giuseppe
Date Issued
2011
Journal
Artificial Immune Systems
Publisher
Springer-Verlag
Citation
Stracquadanio, Giovanni, Renato Umeton, Jole Costanza, Viviana Annibali, Rosella Mechelli, Mario Pavone, Luca Zammataro, and Giuseppe Nicosia. “Large Scale Agent-Based Modeling of the Humoral and Cellular Immune Response.” Artificial Immune Systems (2011): 15–29.
Version
Author's final manuscript
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 in-silico Immune Systems. (i) A large-scale model, with a complexity of representation of 10[superscript 6] − 10[superscript 8] 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.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
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DOI of Published Version
https://doi.org/10.1007/978-3-642-22371-6_2
