Abstract
The biological immune system is the progressive complex adaptive systems (CASs) that consist of inhomogeneous and adaptive agents. It is an important defense mechanism in the human beings that generates an complex cellular response against the foreign disturbances. It also exhibits prominent properties such as emergence and self-organization. There is an immediate need of immune system modeling to understand its complex inbuilt mechanisms and to see how it keep up the homeostasis. Various mathematical and computational simulation approaches have been proposed for the modeling of the intricate dynamics of the complex biological immune system. There are two kinds of modeling techniques that are used to simulate the immune system: equation-based modeling and agent-based modeling. Due to some drawbacks of the equation-based simulation, agent-based modeling technique is used. In this paper, we propose an agent-based model of the adaptive immune system and it is developed with the help of Netlogo simulation tool. This model helps researchers to understand the structure of the immune response and verify hypotheses. This model can be easily employed as an educational tool in academics and a research tool in scientific disciplines for developing medicines that can keep a disease under control.
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Shinde, S.B., Kurhekar, M.P. (2020). Agent-Based Modeling of the Adaptive Immune System Using Netlogo Simulation Tool. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1057. Springer, Singapore. https://doi.org/10.1007/978-981-15-0184-5_40
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DOI: https://doi.org/10.1007/978-981-15-0184-5_40
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