Wayne Wakeland
ABSTRACT
The modeling of complex biological systems presents a significant challenge. Central to this challenge is striking a balance between the degree of abstraction required to facilitate analysis and understanding, and the degree of comprehensiveness required for fidelity of the model to its reference-system. It is likely necessary to utilize multiple modeling methods in order to achieve this balance. Our research created a hybrid simulation model by melding an agent-based model of acute local infection with a system dynamics model that reflects key systemic properties. The agent based model was originally developed to simulate global inflammation in response to injury or infection, and has been used to simulate clinical drug trials. The long term objective is to develop models than can be scaled up to represent organ and system level phenomena such as multiple organ failure associated with severe sepsis. The work described in this paper is an initial proof of concept of the ability to combine these two modeling methods into a hybrid model, the type of which will almost certainly be needed to accomplish the ultimate objective of comprehensive in silico research platforms.
- An, Gary (2001) "Agent-based computer simulation and SIRS: building a bridge between basic science and clinical trials" Shock 16(4):266--273.Google Scholar
Cross Ref
- An, Gary (2004) "In silico experiments of existing and hypothetical Cytokine-directed clinical trials using agent based modeling" Crit Care Med 32(10):2050--2060.Google ScholarCross Ref
- Buchman TG (1997) "Awash in data: the problem of predicting physiology in severe sepsis" Shock 8(3):232.Google ScholarCross Ref
- Buchman TG, Cobb JP, Lapedes AS, Kepler TB (2001) "Complex systems analysis: a tool for shock research" Shock 16(4):248--51.Google ScholarCross Ref
- Clermont, Gilles, John Bartels, Rukmini Kumar, Greg Constantine, Yoram Vodovotz, Carson Chow (2004) "In silico design of clinical trials: A method coming of age" Crit Care Med 32(10):2061--2070.Google ScholarCross Ref
- Dancey JT, Deubelbeiss KA, Harker LA, Finch CA (1976) "Neutrophil kinetics in man" J. Clin. Invest. 58: 705--715.Google ScholarCross Ref
- Kumar R, Clermont G, Vodovotz Y, Chow CC (2004) "The dynamics of acute inflammation" J Th. Bio 230:145--155.Google ScholarCross Ref
- Mantovani, A., C. A. Dinarello, et al. (2000). Pharmacology of Cytokines. Oxford, Oxford University PressGoogle Scholar
- Marshall, John C (2004) "Through the glass darkly: The brave new world of in silico modeling" Crit Care Med 32(10):2157--2158.Google ScholarCross Ref
- Matlab (2005) see The Mathworks http://www.mathworks.com/ (software verion 7)Google Scholar
- Netlogo (2005) see Northwestern University http://ccl.northwestern.edu/netlogo/ (software version 2.1)Google Scholar
- Neugebauer EA, Willy C, Sauerland S (2001) "Complexity and non-linearity in shock research: reductionism or synthesis?" Shock 16(4):252--8.Google ScholarCross Ref
- Ropella, GEP, Hunt, CA, Sheikh-Bahaei, S (2005) Methodological Considerations of Heuristic Modeling of Biological Systems. Proceedings of The 9th World Multi-Conference on Systemics, Cybernetics and Informatics, July 10-13, 2005 - Orlando, Florida; available at http://biosystems.ucsf.edu/Researc/RecentPapers.htmlGoogle Scholar
- Suwa T, Hogg JC, English D, Van Eeden SF (2000) "Interleukin-6 induces demargination of intravascular neutophils and shortens their transit in marrow" A J P Heart Circ Physiol 279:H2954--H2960.Google ScholarCross Ref
- Vodovotz, Yoram, Gilles Clermont, Carson Chow, Gary An (2004) "Mathematical models of the acute inflammatory response," Current Opinion in Critical Care 10:383--390.Google ScholarCross Ref
- Vodovotz Y, Clermont G, Hunt CA, Lefering R, Bartels J, Seydel R, Hotchkiss J, Ta'asan S, Neugebauer EA and An G. Evidence-based Modeling of Critical Illness: An Initial Consensus from the Society of Complexity in Acute Illness. Journal of Critical Care, in press.Google Scholar
- Wakeland W, Macovsky L, Gallaher E, Aktipis A (2004) "A Comparison of System Dynamics and Agent-based Simulation Applied to the Study of Cellular Receptor Dynamics," Proc. of the 37th Ann. Conf. of the Hawaii Int'l Complex System Society, pp 1381--1390. Google ScholarDigital Library
- Wakeland W, Macovsky L, An G (2006) Details of SD submodel in hybrid model of Airs, http://www.sysc.pdx.edu/faculty/Wakeland/papers/HybridAIRSmodel.pdfGoogle Scholar
- A hybrid simulation model for studying acute inflammatory response
Recommendations
An Autonomous Multi-Agent Simulation Model for Acute Inflammatory Response
This research proposes an agent-based simulation model combined with the strength of systemic dynamic mathematical model, providing a new modeling and simulation approach of the pathogenesis of AIR. AIR is the initial stage of a typical sepsis episode, ...
Computer simulation to study inflammatory response
Symposium: medical and healthcare simulation, part 2Development of a model-based clinical sepsis biomarker for critically ill patients
Sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however positive blood culture results may take up to 48h. Insulin sensitivity (S"I) ...
Comments