TY - GEN
T1 - Multiformalism modeling and simulation of immune system mechanisms
AU - Amparore, Elvio Gilberto
AU - Beccuti, Marco
AU - Castagno, Paolo
AU - Franceschinis, Giuliana
AU - Pennisi, Marzio
AU - Pernice, Simone
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The immune system (IS) represents a complex network of cells and molecules devoted to the protection of individuals from external pathogens, and in terms of complexity, it is only second to the central nervous system. As our knowledge of the IS mechanisms has become more exhaustive, interest has grown in applying modeling and simulation techniques in this context. In particular, among these techniques, the Agent Based Models (ABMs) have been increasingly applied for the IS simulation. One of the major drawbacks of ABMs is represented by the lack of well-defined semantics, which may lead to inconsistent results in comparison to other stochastic approaches. In this paper, we make use of the well-defined semantics and the simulation algorithm for ABMs that we proposed in [1] to implement a few models of the Cancer-Immune System. Comparing ABMs and Gillespie's Stochastic Simulation Algorithm results we show that our methodology brings coherence among the results of ABMs and SSA.
AB - The immune system (IS) represents a complex network of cells and molecules devoted to the protection of individuals from external pathogens, and in terms of complexity, it is only second to the central nervous system. As our knowledge of the IS mechanisms has become more exhaustive, interest has grown in applying modeling and simulation techniques in this context. In particular, among these techniques, the Agent Based Models (ABMs) have been increasingly applied for the IS simulation. One of the major drawbacks of ABMs is represented by the lack of well-defined semantics, which may lead to inconsistent results in comparison to other stochastic approaches. In this paper, we make use of the well-defined semantics and the simulation algorithm for ABMs that we proposed in [1] to implement a few models of the Cancer-Immune System. Comparing ABMs and Gillespie's Stochastic Simulation Algorithm results we show that our methodology brings coherence among the results of ABMs and SSA.
KW - Agent Based Models
KW - Extended Stochastic Symmetric Nets
KW - Immune system modeling
KW - Stochastic Simulation Algorithm
UR - http://www.scopus.com/inward/record.url?scp=85125172150&partnerID=8YFLogxK
U2 - 10.1109/BIBM52615.2021.9669796
DO - 10.1109/BIBM52615.2021.9669796
M3 - Conference contribution
AN - SCOPUS:85125172150
T3 - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
SP - 3259
EP - 3266
BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
A2 - Huang, Yufei
A2 - Kurgan, Lukasz
A2 - Luo, Feng
A2 - Hu, Xiaohua Tony
A2 - Chen, Yidong
A2 - Dougherty, Edward
A2 - Kloczkowski, Andrzej
A2 - Li, Yaohang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Y2 - 9 December 2021 through 12 December 2021
ER -
