OJMSi> Vol.2 No.1, January 2014

Agent-Based Model: A Surging Tool to Simulate Infectious Diseases in the Immune System

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ABSTRACT

Agent-based models (ABMs) are capable of constructing individual system components at different levels of representation to describe non-linear relationships between those components. Compared to a traditional mathematical modeling approach, agent-based models have an inherent spatial component with which they can easily describe local interactions and environmental heterogeneity. Furthermore, agent-based model maps interactions among agents inherently to the biological phenomenon by embedding the stochastic nature and dynamics transitions, thereby demonstrating suitability for the development of complex biological processes. Recently, an abundance of literature has presented application of agent-based modeling in the biological system. This review focuses on application of agent-based modeling to progression in simulation of infectious disease in the human immune system and discusses advantages and disadvantages of agent-based modeling application. Finally, potential implementation of agent-based modeling in relation to infectious disease modeling in future research is explored.

Cite this paper

Shi, Z. , Wu, C. and Ben-Arieh, D. (2014) Agent-Based Model: A Surging Tool to Simulate Infectious Diseases in the Immune System. Open Journal of Modelling and Simulation, 2, 12-22. doi: 10.4236/ojmsi.2014.21004.

References

[1] Wikipedia.org. http://en.wikipedia.org/wiki/Infectious_disease
[2] R. Kumar, G. Clermont, Y. Vodovotz and C. C. Chow, “The Dynamics of Acute Inflammation,” Journal of Theoretical Biology, Vol. 230, No. 2, 2004, pp. 145-155. http://dx.doi.org/10.1016/j.jtbi.2004.04.044
[3] A. Reynolds, J. Rubin, G. Clermont, J. Day, Y. Vodovotz and G. B. Ermentrout, “A Reduced Mathematical Model of the Acute Inflammatory Response: I. Derivation of Model and Analysis of Anti-Inflammation,” Journal of Theoretical Biology, Vol. 242, No. 1, 2006, pp. 220-236.
http://dx.doi.org/10.1016/j.jtbi.2006.02.016
[4] R. Axelrod, “The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration,” 1th Edition, Princeton University Press, 1997.
[5] J. M. Epstein and R. L. Axtell, “Growing Artificial Societies: Social Science from the Bottom Up,” 1th Edition, MIT Press, 1996.
[6] A. L. Bauer, C. A. A. Beauchemin and A. S. Perelson, “Agent-Based Modeling of Host-Pathogen Systems: The Successes and Challenges,” Information Sciences, Vol. 179, No. 10, 2009, pp. 1379-1389. http://dx.doi.org/10.1016/j.ins.2008.11.012
[7] T. K. Kar, “Stability Analysis of a Prey-Predator Model Incorporating a Prey Refuge,” Communications in Nonlinear Science and Numerical Simulation, Vol. 10, No. 6, 2005, pp. 681-691.
http://dx.doi.org/10.1016/j.cnsns.2003.08.006
[8] R. C. Bone, R. A. Balk, F. B. Cerra, R. P. Dellinger, A. M. Fein and W. A. Knaus, “Definitions for Sepsis and Organ Failure and Guidelines for the Use of Innovative Therapies in Sepsis,” Chest, Vol. 101, No. 6, 1992, pp. 1644-1655.
[9] M. P. Glauser, “Pathophysiologic Basis of Sepsis: Considerations for Future Strategies of Intervention,” Critical Care Medicine, Vol. 28, No. 9, 2000, pp. 84-88.
[10] American College of Chest Physicians/Society of Critical Medicine Consensus Committee, “Definitions for Sepsis and Organ Failure and Guidelines for the Use of Innovative Therapies in Sepsis,” Critical Care Medicine, Vol. 20, No. 6, 1992, pp. 864-874. http://dx.doi.org/10.1097/00003246-199206000-00025
[11] G. An, “Agent-Based Computer Simulation and SIRS: Building a Bridge between Basic Science and Clinical Trials,” Shock, Vol. 16, No. 4, 2001, pp. 266-273. http://dx.doi.org/10.1097/00024382-200116040-00006
[12] G. An, “In Silico Experiments of Existing and Hypothetical Cytokine-Directed Clinical Trials Using Agent-Based Modeling,” Critical Care Medicine, Vol. 32, No. 10, 2004, pp. 2050-2060.
http://dx.doi.org/10.1097/01.CCM.0000139707.13729.7D
[13] J. Wu, D. Ben-Arieh and Z. Z. Shi, “An Autonomous Multi-Agent Simulation Model for Acute Inflammatory Response,” International Journal of Artificial Life Research, Vol. 2, No. 2, 2011, pp. 105-121. http://dx.doi.org/10.4018/jalr.2011040106
[14] X. Dong, P. T. Foteinou, S. E. Calvano, S. F. Lowry and I. P. Androulakis, “Agent-Based Modeling of Endotoxin-Induced Acute Inflammatory Response in Human Blood Leukocytes,” PLoS One, Vol. 5, No. 2, 2011, Article ID: e9249. http://dx.doi.org/10.1371/journal.pone.0009249
[15] Q. Mi, B. Rivière, G. Clermont, D. L. Steed and Y. Vodovotz, “Agent-Based Model of Inflammation and Wound Healing: Insights into Diabetic Foot Ulcer Pathology and the Role of Transforming Growth Factor-β1,” Wound Repair and Regeneration, Vol. 15, No. 5, 2007, pp. 671-682.
http://dx.doi.org/10.1111/j.1524-475X.2007.00271.x
[16] N. Y. K. Li, K. Verdolini, G. Clermont, Q. Mi, E. N. Rubinstein, P. A. Hebda and Y. Vodovotz, “A Patient-Specific in Silico Model of Inflammation and Healing Tested in Acute Vocal Fold Injury,” PLoS One, Vol. 3, No. 7, 2008, Article ID: e2789. http://dx.doi.org/10.1371/journal.pone.0002789
[17] G. M. Dancik, D. E. Jones and K. S. Dorman, “An Agent-Based Model for Leishmania Major Infection,” Unifying Themes in Complex System, pp. 243-250.
[18] J. B. Seal, J. C. Alverdy, O. Zaborina and G. An, “Agent-Based Dynamic Knowledge Representation of Pseudomonas aeruginosa Virulence Activation in the Stressed Gut: Towards Characterizing Host-Pathogen Interactions in Gut-Derived Sepsis,” Theoretical Biology and Medical Modelling, Vol. 8, No. 33, 2011, pp. 1-34.
[19] Y. G. Mei, R. Hontecillas, X. Y. Zhang, K. Bisset, S. Eubank, S. Hoops, M. Marathe and J. Bassaganya-Riera, “ENISI Visual, an Agent-Based Simulator for Modeling Gut Immunity,” IEEE International Conference on Bioinformatics and Biomedicine, Philadelphia, 4-7 October 2012, pp. 1-5.
[20] T. Lux and M. Marchesi, “Volatility Clustering in Financial Market: A Micro-Simulation of Interacting Agents,” International Journal of Theoretical and Applied Finance, Vol. 3, No. 4, 2000, pp. 675-702.
http://dx.doi.org/10.1142/S0219024900000826
[21] B. LeBaron, “Agent-Based Computational Finance: Suggested Readings and Early Research,” Journal of Economic Dynamics and Control, Vol. 24, No. 5-7, 2000, pp. 679-702.
http://dx.doi.org/10.1016/S0165-1889(99)00022-6
[22] A. M. EI-Sayed, P. Scarborough, L. Seemann and S. Galea, “Social Network Analysis and Agent-Based Modeling in Social Epidemiology,” Epidemiologic Perspectives and Innovations, Vol. 9, No. 9, 2012, pp. 1-9.
[23] Netlogo, Software Version 4.0.4. http://ccl.northwestern.edu/netlogo
[24] S. F. Railsback, “Agent-Based Simulation Platforms: Review and Development Recommendations,” Simulation, Vol. 82, No. 9, 2006, pp. 609-623. http://dx.doi.org/10.1177/0037549706073695
[25] Repast. http://repast.sourceforge.net/
[26] E. Bonabeau, “Agent-Based Modeling: Methods and Techniques for Simulating Human Systems,” PNAS, Vol. 99, No. 3, 2002, pp. 7280-7287. http://dx.doi.org/10.1073/pnas.082080899
[27] M. Aziz, A. Jacob, W. L. Yang, A. Matsuda and P. Wang, “Current Trends in Inflammatory and Immunomodulatory Mediators in Sepsis,” Journal of Leukocyte Biology, Vol. 93, No. 3, 2013, pp. 1-14.
http://dx.doi.org/10.1189/jlb.0912437
[28] G. An, “Introduction of an Agent-Based Multi-Scale Modular Architecture for Dynamic Knowledge Representation of Acute Inflammation,” Theoretical Biology and Medical Modeling, Vol. 5, 2008, p. 11.
http://dx.doi.org/10.1186/1742-4682-5-11
[29] S. Wendel and C. Dibble, “Dynamic Agent Compression,” Journal of Artificial Societies and Social Simulation, Vol. 10, No. 2, 2007, pp. 1-16.
[30] L. Harris and P. Clancy, “A Partitioned Leaping Approach to Multi-Scale Modeling of Chemical Reaction Dynamics,” Journal of Chemical Physics, Vol. 125, No. 14, 2006, pp. 1-10.
http://dx.doi.org/10.1063/1.2354085

  
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