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Comparing system dynamics and agent-based simulation for tumour growth and its interactions with effector cells

Published:27 June 2011Publication History

ABSTRACT

There is little research concerning comparisons and combination of System Dynamics Simulation (SDS) and Agent Based Simulation (ABS). ABS is a paradigm used in many levels of abstraction, including those levels covered by SDS. We believe that the establishment of frameworks for the choice between these two simulation approaches would contribute to the simulation research. Hence, our work aims for the establishment of directions for the choice between SDS and ABS approaches for immune system-related problems. Previously, we compared the use of ABS and SDS for modelling agents' behaviourin an environment with no movement or interactions between these agents. We concluded that for these types of agents it is preferable to use SDS, as it takes up less computational resources and produces the same results as those obtained by the ABS model. In order to move this research forward, our next research question is: if we introduce interactions between these agents will SDS still be the most appropriate paradigm to be used? To answer this question for immune system simulation problems, we will use, as case studies, models involving interactions between tumour cells and immune effector cells. Experiments show that there are cases where SDS and ABS can not be used interchangeably, and therefore, their comparison is not straightforward.

References

  1. R Eftimie, J L Bramson, and D J Earn. Interactions between the immune system and cancer: A brief review of non-spatial mathematical models. Bull Math Biol., march 2010.Google ScholarGoogle Scholar
  2. Grazziela P. Figueredo and Uwe Aickelin. Investigating immune system aging: System dynamics and agent-based modelling. In Proceedings of the Summer Computer Simulation Conference 2010, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. W. O. Kermack and A. G. McKendrick. Contributions to the mathematical theory of epidemics. In Proc. Roy. Soc., 1927.Google ScholarGoogle ScholarCross RefCross Ref
  4. Vladimir A. Kuznetsov, Iliya A. Makalkin, Mark A. Taylor, and Alan S. Perelson. Nonlinear dynamics of immunogenic tumors: parameter estimation and global bifurcation analysis. Bulletin of Mathematical Biology, 56(2):295--321, 1994.Google ScholarGoogle ScholarCross RefCross Ref
  5. Charles M. Macal. To agent-based simulation from system dynamics. In Proceedings of the 2010 Winter Simulation Conference, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. John Pourdehnad, Kambiz Maani, and Habib Sedehi. System dynamics and intelligent agent based simulation: where is the synergy? In Proceedings of the XX International Conference of the System Dynamics society., 2002.Google ScholarGoogle Scholar
  7. Hazhir Ramandad and John Sterman. Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models. Management Science, 5(5), may 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Nadine Schieritz. Integrating system dynamics and agent-based modeling. In Proceedings of the XX International Conference of the System Dynamics society., 2002.Google ScholarGoogle Scholar
  9. Nadine Schieritz and Andreas Größler. Emergent structures in supply chains: a study integrating agent-based and system dynamics modeling. In Proceedings of the XXI International Conference of the System Dynamics society., 2003.Google ScholarGoogle ScholarCross RefCross Ref
  10. Nadine Schieritz and Peter M. Milling. Modeling the forrest or modeling the trees: A comparison of system dynamics and agent based simulation. In Proceedings of the XXI International Conference of the System Dynamics society., 2003.Google ScholarGoogle Scholar
  11. Wayne W. Wakeland, Edward J. Gallaher, Louis M. Macovsky, and C. Athena Aktipis. A comparison of system dynamics and agent-based simulation applied to the study of cellular receptor dynamics. Hawaii International Conference on System Sciences, 3, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  1. Comparing system dynamics and agent-based simulation for tumour growth and its interactions with effector cells

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