title: A quantitative model of thermal injury-induced acute inflammation.
abstract: Severe burns are among the most common causes of death from unintentional injury. The induction and resolution of the burn-induced systemic inflammatory response are mediated by a network of factors and regulatory proteins. Numerous mechanisms operate simultaneously, thus requiring a systems level approach to characterize their overall impact. Towards this goal, we propose an in silico semi-mechanistic model of burn-induced systemic inflammation using liver-specific gene expression from a rat burn model. Transcriptional responses are coupled with extracellular signals through a receptor mediated indirect response (IDR) and transit compartment model. The activation of the innate immune system in response to the burn stimulus involves the interaction between extracellular signals and critical receptors which triggers downstream signal transduction cascades leading to transcriptional changes. The resulting model consists of fifteen (15) coupled ordinary differential equations capturing key aspects of inflammation such as pro-inflammation, anti-inflammation and hypermetabolism. The model was then evaluated through a series of biologically relevant scenarios aiming at revealing the non-linear behavior of acute inflammation including: investigating the implication of effect of different severity of thermal injury; examining possible mechanistic dysregulation of IKK-NF-κB system which may reflect secondary effects that lead to potential malfunction of the response; and exploring the outcome of administration of receptor antagonist or anti-body to significant cytokines.
authors: Chemical Engineering, Rutgers University, 98 Brett Road, Piscataway, NJ 08854, USA.
authors: Qian
authors: Q
authors: Yang
authors: Francois
authors: F
authors: Berthiaume
authors: Ioannis P
authors: IP
authors: Androulakis
mesh_terms: D000465:Algorithms; D000818:Animals; D002056:Burns / Q000150:complications* / Q000175:diagnosis / Q000188:drug therapy / Q000378:metabolism*; D016000:Cluster Analysis; D003198:Computer Simulation; D015536:Down-Regulation / Q000235:genetics; D020869:Gene Expression Profiling; D051550:I-kappa B Kinase / Q000235:genetics / Q000378:metabolism; D020672:I-kappa B Proteins / Q000235:genetics / Q000378:metabolism; D007113:Immunity, Innate / Q000502:physiology; D007249:Inflammation / Q000188:drug therapy / Q000209:etiology* / Q000378:metabolism*; D007375:Interleukin-1 / Q000037:antagonists & inhibitors / Q000235:genetics; D007700:Kinetics; D008099:Liver / Q000378:metabolism; D008297:Male; D008954:Models, Biological; D000072000:NF-KappaB Inhibitor alpha; D016328:NF-kappa B / Q000235:genetics / Q000378:metabolism; D051381:Rats; D017207:Rats, Sprague-Dawley; D017472:Receptors, Interleukin-1 / Q000037:antagonists & inhibitors; D047888:Receptors, Tumor Necrosis Factor, Type I / Q000235:genetics / Q000378:metabolism; D015398:Signal Transduction / Q000276:immunology; D014409:Tumor Necrosis Factor-alpha / Q000037:antagonists & inhibitors / Q000097:blood / Q000235:genetics / Q000378:metabolism; D015854:Up-Regulation / Q000235:genetics
chemical_list: D020672:I-kappa B Proteins; D007375:Interleukin-1; D016328:NF-kappa B; C000606206:Nfkbia protein, rat; D017472:Receptors, Interleukin-1; D047888:Receptors, Tumor Necrosis Factor, Type I; C506237:Tnfrsf1a protein, rat; D014409:Tumor Necrosis Factor-alpha; D000072000:NF-KappaB Inhibitor alpha; D051550:I-kappa B Kinase