Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey, United States of America.
PLoS One. 2010 Feb 18;5(2):e9249. doi: 10.1371/journal.pone.0009249.
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.
炎症是由多种刺激引起的高度复杂的生物反应。模拟这一动态过程的一个持续挑战是反应的（非线性）性质，这排除了单变量假设。基于系统的方法为在其动态平衡环境下理解炎症提供了一个很有前景的可能性。为了研究急性炎症反应的潜在复杂性，开发了一种基于代理的框架，该框架将宿主的新兴反应建模为与复杂信号级联和细胞间免疫系统相互作用相关的协调相互作用的结果。
方法/主要发现：提出了一种基于代理的建模 (ABM) 框架来研究急性人类炎症的非线性动力学。该模型使用 NetLogo 软件实现。相互作用的代理涉及炎症特异性分子或对于炎症反应在整个系统中传播至关重要的细胞。进一步考虑了参与信号级联的分子相互作用的空间方向以及细胞异质性。通过成功复制自限性炎症反应以及一系列指示反应非线性动力学的场景来评估所提出的计算机模型。这些场景涉及持续（非）传染性反应或先天免疫耐受和增强效应，随后是细胞内信号分子和级联的干扰。
结论/意义：本研究中开发的 ABM 框架提供了关于内毒素信号在细胞反应水平传播过程中涉及的介质的随机相互作用的深入了解。模拟结果与我们之前的研究工作一致，该工作涉及包括转录动力学、信号和生理成分在内的确定性人类炎症模型的开发。本研究中探索的假设场景可能有助于我们更好地理解如何操纵分子物种的行为可以表现为整个系统的突发行为。
