The systemic inflammatory response syndrome (SIRS) often accompanies critical illness but is evoked by many stimuli e.g. infection, trauma, invasive surgery and biological stressors in general . It often progresses to severe cases of sepsis with a substantial morbidity and mortality where bacterial growth is found and organ failure occurs [2, 3]. Despite increasing knowledge about pathophysiological pathways and processes involved in sepsis as well as promising results on animal studies and preclinical trials [4, 5], the molecular mechanisms and physiological significance of the systemic inflammatory response are still not fully understood. In order to learn more about the mechanisms associated with the host inflammatory responses, the human endotoxemia model, an in vivo model of systemic inflammation in which a single intravenous bolus of E.coli endotoxin (LPS) is given to healthy human subjects, has been employed. The model results in many similar physiological host responses that characterize Gram-negative infection , providing an invaluable source for the systemic identification of biological features representing the complex dynamics of a host undergoing inflammatory responses [7, 8]. With the assumption that transcriptional signatures can be able to characterize the cellular dynamics in response to the inflammatory agent, high-dimensional transcriptional human endotoxemia data were decomposed into significantly co-expressed clusters , three out of which are selected as representatives for common expression profiles of inflammatory biomarkers including pro-inflammatory, anti-inflammatory, and bio-energetic transcriptional patterns. Additionally, the activation for ‘translating' from extra-cellular signals to intra-cellular responses is assumed to be governed by signaling cascades activated with transcription factors (e.g. NFkB [10, 11]) that drive the expression of relevant genes. In an attempt towards a better understanding of the molecular mechanisms as well as the complex dynamics of the host inflammatory responses, we previously developed a semi-mechanistic indirect response model using the ordinary differential equation (ODE) system . However, it is fully deterministic with respect to their behavior given a certain set of initial conditions and assumes the homogeneity and perfect mixing within compartments as well as ignores the spatial aspect [13, 14]. Thus, we here explore an alternative approach – agent-based model (ABM) which can naturally account for more biological phenomena e.g. stochasticity [15, 16], heterogeneity [17, 18]. ABM is an object-oriented, rule-based, discrete, and stochastic modeling method [19, 20]. Interactions between agents are nonlinear, stochastic, spatial, and are described with multiple compartments and asynchronous movements. However, since the model is relied on an ill-defined principle of ‘emergence', the ABM is not a ‘mathematical model' per se, and thus is not subjected to formal analysis and ‘solved' . One of the most important issues is the estimation of model parameters. With the assumption that the default system production of a component type is approximately equal to its degradation in the normal (healthy/steady) state, we present a novel technique based on the pharmacokinetic principles for tuning parameters including the population sizes and the synthesis rates of component types. As such, in a healthy system the total number of individuals of each component type will not change significantly over the time. After all parameters are identified to maintain the system in the healthy state, circadian rhythms are incorporated and then LPS is putatively injected with various doses for qualitatively examining the profiles of representative responses. In conclusion, the aim of this study is two folds: (i) develop a ‘better' in silico agent-based model that can provide more insights into the human endotoxemia, and (ii) utilize the created model to study the individual cellular dynamics and especially ensemble patterns that lead to distinct outcomes under the administration of different LPS doses. References 1. Nystrom PO: The systemic inflammatory response syndrome: definitions and aetiology. J Antimicrob Chemother 1998, 41 Suppl A:1-7. 2. Annane D, Bellissant E, Cavaillon JM: Septic shock. Lancet 2005, 365(9453):63-78. 3. Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR: Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med 2001, 29(7):1303-1310. 4. Riedemann NC, Guo RF, Ward PA: Novel strategies for the treatment of sepsis. Nat Med 2003, 9(5):517-524. 5. Deans KJ, Haley M, Natanson C, Eichacker PQ, Minneci PC: Novel therapies for sepsis: a review. J Trauma 2005, 58(4):867-874. 6. Santos AA, Wilmore DW: The systemic inflammatory response: perspective of human endotoxemia. Shock 1996, 6 Suppl 1:S50-56. 7. Calvano SE, Xiao W, Richards DR, Felciano RM, Baker HV, Cho RJ, Chen RO, Brownstein BH, Cobb JP, Tschoeke SK et al: A network-based analysis of systemic inflammation in humans. Nature 2005, 437(7061):1032-1037. 8. Talwar S, Munson PJ, Barb J, Fiuza C, Cintron AP, Logun C, Tropea M, Khan S, Reda D, Shelhamer JH et al: Gene expression profiles of peripheral blood leukocytes after endotoxin challenge in humans. Physiol Genomics 2006, 25(2):203-215. 9. Nguyen TT, Nowakowski RS, Androulakis IP: Unsupervised selection of highly coexpressed and noncoexpressed genes using a consensus clustering approach. Omics 2009, 13(3):219-237. 10. Courtois G: The NF-kappaB signaling pathway in human genetic diseases. Cell Mol Life Sci 2005, 62(15):1682-1691. 11. Ihekwaba AE, Broomhead DS, Grimley RL, Benson N, Kell DB: Sensitivity analysis of parameters controlling oscillatory signalling in the NF-kappaB pathway: the roles of IKK and IkappaBalpha. Syst Biol (Stevenage) 2004, 1(1):93-103. 12. Foteinou PT, Calvano SE, Lowry SF, Androulakis IP: In silico simulation of corticosteroids effect on an NFkB- dependent physicochemical model of systemic inflammation. PLoS One 2009, 4(3):e4706. 13. Vodovotz Y, Constantine G, Rubin J, Csete M, Voit EO, An G: Mechanistic simulations of inflammation: current state and future prospects. Math Biosci 2009, 217(1):1-10. 14. Wakeland WW, Gallaher EJ, Macovsky LM, Aktipis CA: A comparison of system dynamics and agent-based simulation applied to the study of cellular receptor dynamics. The 37th Hawaii International Conference on System Sciences 2004. 15. Raj A, van Oudenaarden A: Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 2008, 135(2):216-226. 16. Raser JM, O'Shea EK: Noise in gene expression: origins, consequences, and control. Science 2005, 309(5743):2010-2013. 17. Geva-Zatorsky N, Rosenfeld N, Itzkovitz S, Milo R, Sigal A, Dekel E, Yarnitzky T, Liron Y, Polak P, Lahav G et al: Oscillations and variability in the p53 system. Mol Syst Biol 2006, 2:2006 0033. 18. Nelson DE, Ihekwaba AE, Elliott M, Johnson JR, Gibney CA, Foreman BE, Nelson G, See V, Horton CA, Spiller DG et al: Oscillations in NF-kappaB signaling control the dynamics of gene expression. Science 2004, 306(5696):704-708. 19. Chavali AK, Gianchandani EP, Tung KS, Lawrence MB, Peirce SM, Papin JA: Characterizing emergent properties of immunological systems with multi-cellular rule-based computational modeling. Trends Immunol 2008, 29(12):589-599. 20. An G: Introduction of an agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation. Theor Biol Med Model 2008, 5:11.
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