Agent-Based Modeling Figure 4. Temporal responses of an unresolved inflammatory response due to high LPS concentration. A high concentration of LPS (LPS(0) = 750) can cause a malfunction in the dynamics of the host leading to an exacerbated inflammatory response (solid lines). Dashed lines refer to the implications of high concentration of LPS as simulated by our deterministic (ODE) approach. For the purpose of comparing the simulated output between the ABM and the ODE model, all responses are normalized so that numerically they range between (0,1). doi:10.1371/journal.pone.0009249.g004 involved in bio-energetic cellular processes [52]. On the other hand the number of anti-inflammatory (A) molecules raised the energy level and drove the cells to the ‘‘healthy’’ state, deactivating the switch. Many switch-like phenomena have been observed in biological systems [53]. In conclusion, an agent based modeling framework is proposed as an alternative modeling approach to study the complex, non- linear dynamics of acute human inflammation. We specifically proposed an agent based model that couples critical aspects of the host response to endotoxin. Predicated upon our prior research effort where a deterministic approach has been taken to couple extracellular signals and intricate signaling cascades with the transcriptional response level, the work discussed in this study explored the potential of an agent based modeling approach to improve our understanding of how a system gives rise to a response through its interacting molecules (or cells). Agent based models offer a promising approach in that they can express the dynamics through intuitive multiple interactions between the agents over time. A well known feature of ABMs is their ability to generate surprisingly complex and emergent behavior from very simple rules. However, this modeling framework is not without its own limitations. A key limitation of agent-based modeling has to do with the difficulty in applying a formal analysis to the relationship between the agent rules and the behavior of the system [8,54]. Thus, in contrast to equation-based modeling for which analytical tasks (e.g. parameter sensitivity analysis, bifurca- tion analysis, e.t.c.) can be performed, the stochastic behavior of agents makes it extremely difficult to analyze how each parameter of an AMB simulation affects the output of ABM. To address this issue, researchers rely upon the principles of pattern-oriented analysis, in which patterns of dynamic behavior are used to relate the computational ABM to its real-world reference [55]. The downside of this characteristic, however, is that extensive computational power may be required to generate dense datasets amenable to statistical analysis. In this context, the high computational cost related to running ABM, as compared to equation-based models, may constrain the size of ABM imple- mentations that can be run in the typical academic setting. However, researchers have started to explore ‘‘hybrid’’ model systems in order to increase the feasibility of large ABM-based simulations. Additionally, the recognition that both approaches (EBM and ABM) have their advantages and limitations has placed emphasis on cross-platform validation where some processes are simulated discretely while other processes are handled in a continuous simulation [56]. It is important to realize that such multi-modal approaches are complementary and ideally both would be used to provide a mathematical characterization of a complex dynamical system. Materials and Methods Human Endotoxin Model and Data Collection The data used in this study were generated as part of the Inflammation and Host Response to Injury Large Scale Collabo- rative Project funded by the USPHS, U54 GM621119 [57,58]. Human subjects were injected intravenously with endotoxin (CC- RE, lot 2) at a dose of 2-ng/kg body weight (endotoxin treated subjects) or 0.9% sodium chloride (placebo treated subjects). PLoS ONE | www.plosone.org 7 February 2010 | Volume 5 | Issue 2 | e9249