Document Detail

Agent-based modeling of endotoxin-induced acute inflammatory response in human blood leukocytes.
MedLine Citation:
PMID:  20174629     Owner:  NLM     Status:  MEDLINE    
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.
Xu Dong; Panagiota T Foteinou; Steven E Calvano; Stephen F Lowry; Ioannis P Androulakis
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2010-02-18
Journal Detail:
Title:  PloS one     Volume:  5     ISSN:  1932-6203     ISO Abbreviation:  PLoS ONE     Publication Date:  2010  
Date Detail:
Created Date:  2010-02-22     Completed Date:  2010-09-30     Revised Date:  2013-05-30    
Medline Journal Info:
Nlm Unique ID:  101285081     Medline TA:  PLoS One     Country:  United States    
Other Details:
Languages:  eng     Pagination:  e9249     Citation Subset:  IM    
Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey, United States of America.
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MeSH Terms
Acute Disease
Endotoxins / administration & dosage,  immunology*
Gene Expression Profiling
Immune System / drug effects,  immunology
Inflammation / blood,  chemically induced,  immunology*
Leukocytes / drug effects,  immunology*,  metabolism
Lipopolysaccharides / administration & dosage,  immunology
Macrophages / drug effects,  immunology,  metabolism
Models, Immunological*
NF-kappa B / metabolism
Protein Binding / drug effects
Signal Transduction / drug effects,  immunology
Th1 Cells / drug effects,  immunology,  metabolism
Th2 Cells / drug effects,  immunology,  metabolism
Toll-Like Receptor 4 / metabolism
Transcription, Genetic / drug effects
Grant Support
Reg. No./Substance:
0/Endotoxins; 0/Lipopolysaccharides; 0/NF-kappa B; 0/Toll-Like Receptor 4

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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