Agent-Based Modeling of Endotoxin-Induced Acute Inflammatory Response in Human Blood Leukocytes Xu Dong 1 , Panagiota T. Foteinou1, Steven E. Calvano2, Stephen F. Lowry2, Ioannis P. Androulakis1* 1 Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey, United States of America, 2 Department of Surgery, University of Medicine and Dentristry of New Jersey Robert Wood Johnson Medical School, New Brunswick, New Jersey, United States of America Abstract Background: Inflammation is a highly complex biological response evoked by many stimuli. A persistent challenge in modeling this dynamic process has been the (nonlinear) nature of the response that precludes the single-variable assumption. Systems-based approaches offer a promising possibility for understanding inflammation in its homeostatic context. In order to study the underlying complexity of the acute inflammatory response, an agent-based framework is developed that models the emerging host response as the outcome of orchestrated interactions associated with intricate signaling cascades and intercellular immune system interactions. Methodology/Principal Findings: An agent-based modeling (ABM) framework is proposed to study the nonlinear dynamics of acute human inflammation. The model is implemented using NetLogo software. Interacting agents involve either inflammation-specific molecules or cells essential for the propagation of the inflammatory reaction across the system. Spatial orientation of molecule interactions involved in signaling cascades coupled with the cellular heterogeneity are further taken into account. The proposed in silico model is evaluated through its ability to successfully reproduce a self- limited inflammatory response as well as a series of scenarios indicative of the nonlinear dynamics of the response. Such scenarios involve either a persistent (non)infectious response or innate immune tolerance and potentiation effects followed by perturbations in intracellular signaling molecules and cascades. Conclusions/Significance: The ABM framework developed in this study provides insight on the stochastic interactions of the mediators involved in the propagation of endotoxin signaling at the cellular response level. The simulation results are in accordance with our prior research effort associated with the development of deterministic human inflammation models that include transcriptional dynamics, signaling, and physiological components. The hypothetical scenarios explored in this study would potentially improve our understanding of how manipulating the behavior of the molecular species could manifest into emergent behavior of the overall system. Citation: Dong X, Foteinou PT, Calvano SE, Lowry SF, Androulakis IP (2010) Agent-Based Modeling of Endotoxin-Induced Acute Inflammatory Response in Human Blood Leukocytes. PLoS ONE 5(2): e9249. doi:10.1371/journal.pone.0009249 Editor: Diego Di Bernardo, Fondazione Telethon, Italy Received August 26, 2009; Accepted November 27, 2009; Published February 18, 2010 Copyright: 2010 Dong et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: X.D., P.T.F., and I.P.A. acknowledge support from the National Science Foundation grant 0519563, the Environmental Protection Agency grant GAD-R- 832721-010-RRA, and a Busch Biomedical Research grant. S.E.C. and S.F.L. are supported in part from National Institutes of Health GM34695. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: yannis@rci.rutgers.edu Introduction The acute inflammatory response (AIR) is the initial response of the host to a diverse array of biological stressors including infection, burns, trauma and invasive surgery. Under normal circumstances the dynamics of acute inflammation are tightly regulated and self-limited [1]; however when anti-inflammatory processes fail an amplified inflammatory state is characterized by severe, uncontrolled systemic inflammation and multiple organ dysfunction can develop [2]. Despite the growing understanding of the cellular and molecular mechanisms of systemic inflammation [3] the complexity of the response has challenged therapeutic develop- ment [4,5]. A key reason for this conundrum has been speculated to be the difficulty of predicting the impact of manipulating individual components of the highly complex, non-linear and redundant inflammatory response [6]. Thus progress would require a greater understanding of how components are organ- ized to generate a behavior thus making systems based approaches appealing. Mathematical modeling as a dynamic knowledge representation offers a promising possibility for understanding complex physiologic responses in their homeo- static context. As a result, various approaches have been proposed to simulate the underlying complexity of the inflam- matory response including both equation based models (EBM) and agent based models (ABM) [7,8]. Although both modeling approaches (EBM and ABM) have both advantages and disadvantages [8], agent based modeling has emerged as an alternative for addressing features of complex biological systems [9]. The recognition that EBM are predicated on the assumption of a homogeneously distributed system has made it less applicable in situations where spatial effects are important [10]. On the other hand, ABM has an intrinsically spatial component based on its reliance upon local interactions PLoS ONE | www.plosone.org 1 February 2010 | Volume 5 | Issue 2 | e9249