Agent-based modeling of endotoxin-induced acute inflammatory response in human blood leukocytes

• Published in: PloS one Vol. 5; no. 2; p. e9249
• Main Authors: Dong, Xu, Foteinou, Panagiota T, Calvano, Steven E, Lowry, Stephen F, Androulakis, Ioannis P
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18-02-2010; Public Library of Science (PLoS)
• Subjects: Acute Disease; Agent-based models; Analysis; Bioinformatics; Biomedical engineering; Cascades; Complexity; Computational Biology/Systems Biology; Computational Biology/Transcriptional Regulation; Computer simulation; Critical Care and Emergency Medicine/Sepsis and Multiple Organ Failure; Cytokines; Dynamic tests; Dynamical systems; Endotoxins; Endotoxins - administration & dosage; Endotoxins - immunology; Gene Expression Profiling; Genes; Heterogeneity; Human behavior; Humans; Immune system; Immune System - drug effects; Immune System - immunology; Immunological tolerance; Inflammation; Inflammation - blood; Inflammation - chemically induced; Inflammation - immunology; Inflammatory response; Intracellular signalling; Kinases; Leukocytes; Leukocytes - drug effects; Leukocytes - immunology; Leukocytes - metabolism; Lipopolysaccharides - administration & dosage; Lipopolysaccharides - immunology; Macrophages - drug effects; Macrophages - immunology; Macrophages - metabolism; Metabolism; Models, Immunological; NF-kappa B - metabolism; Nonlinear dynamics; Nonlinear response; Nonlinear systems; Phosphorylation; Physiological aspects; Potentiation; Propagation; Protein Binding - drug effects; Rodents; Sepsis; Signal transduction; Signal Transduction - drug effects; Signal Transduction - immunology; Simulation; Software; Stochasticity; Surgery; Th1 Cells - drug effects; Th1 Cells - immunology; Th1 Cells - metabolism; Th2 Cells - drug effects; Th2 Cells - immunology; Th2 Cells - metabolism; Toll-Like Receptor 4 - metabolism; Transcription; Transcription factors; Transcription, Genetic - drug effects; United States > US; New Brunswick New Jersey
• ISSN: 1932-6203; 1932-6203
• 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.
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.
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.