(94b) Agent-Based Simulation of Endotoxin Induced Acute Inflammatory Response in Human Blood Leukocytes

The acute inflammatory response is one important component of the initial response of the host to a diverse array of biological stressors including infection, burns, trauma and surgery. While the amount of experimental information has grown, the complexity of the response has hampered the search for adequate clinical approaches in the settings of various inflammation related disorders. A key reason for this conundrum is the difficulty of predicting the impact of manipulating individual components of the highly complex, non-linear and redundant inflammatory response[1]. Progress in treating these processes requires a greater understanding of how components are organized to generate a behavior thus making model based approaches appealing[2]. As a result, various approaches have been proposed to simulate the underlying complexity of the inflammatory response including both equation based models (EBM) and agent based models (ABM)[3].

Although both modeling approaches (ODE and ABM) might provide significant insight regarding the behavior of complex biological processes, the underlying complexity of the dynamics of AIR pose a significant challenge in that the conventional reductionist approach using continuous, deterministic equation based models (EBM) is insufficient to capture the stochastic nature and dynamic transitional states in biological systems[4]. The recognition that nature is predicated upon cellular processes with stochastic properties has benefited the development of agent based modeling. Agent based models reflect the discrete stochastic nature of interactions and provides a realistic description of sub-cellular events. The key elements in ABMs are the agents, which are entities that represent a certain aspect of the system, for instance a family of cells and/or molecules, who are able to adapt and interact with the environment and with each other based on a specific set of rules[5].

To meet this challenge, an agent based modeling framework (ABM) is proposed to study the complex, non-linear dynamics of human acute inflammation. Predicated upon the essential interactions that define the development of deterministic human inflammation models[6, 7], we opt to translate them into an integrated ABM framework. Accordingly, interacting agents involve either inflammation specific molecules or cells essential for the propagation of the inflammatory reaction across the system. Therefore, spatial orientation of molecule interactions involved in elaborate signaling cascades as well as cellular heterogeneity are particularly taken into account. The proposed ABM framework is naturally stochastic in that interactions are designed to be based upon probabilities providing significant insight on how the stochastic fluctuations of molecular species aggregate to engender the overall systemic response.

The proposed in silico model is evaluated through its ability to reproduce a self-limited inflammatory response successfully as well as a series of scenarios indicative of the non-linear dynamics of the response. These scenarios involve either a persistent (non)infectious response or tolerance and potentiation effects followed by perturbations in intracellular signaling molecules and cascades. Such modeling effort would potentially provide significant insight on the stochastic interactions of the species involved in the propagation of the inflammatory reaction on the cellular response level; thus making it a critical enabler for improving our understanding of how manipulating the behavior of molecular species could manifest into the emergent behavior of the overall system.

1. Vodovotz, Y., et al., Mechanistic simulations of inflammation: Current state and future prospects. Math Biosci, 2008.

2. Clermont, G., et al., In silico design of clinical trials: a method coming of age. Crit Care Med, 2004. 32(10): p. 2061-70.

3. Vodovotz, Y., et al., Mathematical models of the acute inflammatory response. Curr Opin Crit Care, 2004. 10(5): p. 383-90.

4. Lowry, S.F., et al., Challenges for modeling and interpreting the complex biology of severe injury and inflammation. J Leukoc Biol, 2008. 83(3): p. 553-7.

5. Vodovotz, Y., et al., Translational systems biology of inflammation. PLoS Comput Biol, 2008. 4(4): p. e1000014.

6. Foteinou, P.T., et al., Modeling endotoxin-induced systemic inflammation using an indirect response approach. Math Biosci, 2008. 217(1): p. 27-42.

7. Foteinou, P.T., et al., In silico simulation of corticosteroids effect on an NFkB- dependent physicochemical model of systemic inflammation PLoS ONE, doi: 10.1371/journal.pone.0004706 2009 4(3).