A model of TLR4 signaling and tolerance using a qualitative, particle-event-based method: Introduction of spatially configured stochastic reaction chambers (SCSRC)

Mathematical Biosciences

Volumn 217, Issue 1, 2009, Pages 43-52

  An, Gary ^a  

^a NORTHWESTERN UNIVERSITY   (United States)

Author keywords

Agent based model; Intracellular signaling; Knowledge representation; Preconditioning; Systems biology; Toll like receptor

Indexed keywords

ABSTRACTING; BIOCHEMICAL ENGINEERING; BIOLOGY; CONTROL THEORY; DYNAMICS; FEEDBACK; INFORMATION THEORY; MARKUP LANGUAGES; MILITARY OPERATIONS; POLYSACCHARIDES; RATE CONSTANTS; REACTION RATES; SIGNAL TRANSDUCTION; STOCHASTIC MODELS;

AGENT-BASED MODELING; BIOCHEMICAL REACTION RATES; DOSE DEPENDENTS; DYNAMIC COMPLEXITIES; ESSENTIAL DYNAMICS; EXPRESSION DATUMS; FUTURE WORKS; IN SILICO EXPERIMENTS; INFLAMMATORY RESPONSES; INFLAMMATORY SIGNALING; INTRACELLULAR DYNAMICS; INTRACELLULAR PROCESSES; INTRACELLULAR SIGNALING; LIPOPOLYSACCHARIDE; MODELING AND SIMULATIONS; MODELING METHODS; MODULATING FACTORS; MOLECULAR EVENTS; NEGATIVE FEEDBACKS; PRECONDITIONING; REACTION CHAMBERS; REGULATORY MECHANISMS; RULE SYSTEMS; SIGNALING CASCADES; SOFTWARE PLATFORMS; SPATIALLY EXPLICIT; STOCHASTIC NOISES; STOCHASTIC SIGNALS; SYNTHETIC COMPOUNDS; SYNTHETIC EVENTS; SYNTHETIC PATHWAYS; SYSTEMS BIOLOGY; SYSTEMS BIOLOGY MARKUP LANGUAGES; TOLL-LIKE RECEPTOR; TUMOR NECROSIS FACTORS;

KNOWLEDGE REPRESENTATION;

LIPOPOLYSACCHARIDE; TOLL LIKE RECEPTOR 4; TUMOR NECROSIS FACTOR;

COMPLEXITY; NUMERICAL MODEL; SIGNALING; SOFTWARE; STOCHASTICITY;

ANTIGEN EXPRESSION; ARTICLE; COMPUTER PROGRAM; COMPUTER SIMULATION; CYTOKINE RELEASE; INFLAMMATION; SIGNAL TRANSDUCTION; STOCHASTIC MODEL;

COMPUTER SIMULATION; HUMANS; IMMUNE TOLERANCE; LIPOPOLYSACCHARIDES; MODELS, BIOLOGICAL; SIGNAL TRANSDUCTION; STOCHASTIC PROCESSES; TOLL-LIKE RECEPTOR 4; TUMOR NECROSIS FACTOR-ALPHA;

EID: 57849103868     PISSN: 00255564     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.mbs.2008.10.001     Document Type: Article

References (41)

1. Tjardes T., and Neugebauer E. Sepsis research in the next millennium: concentrate on the software rather than the hardware. Shock 17 1 (2002) 1
2. An G., Faeder J., and Vodovotz Y. Translational systems biology: introduction of an engineering approach to the pathophysiology of the burn patient. J. Burn Care Rehabil. 29 2 (2008) 277
3. Vodovotz Y., et al. Evidence-based modeling of critical illness: an initial consensus from the Society for Complexity in Acute Illness. J. Crit. Care 22 1 (2007) 77
4. Kirschner D.E., et al. Toward a multiscale model of antigen presentation in immunity. Immunol. Rev. 216 (2007) 93
5. Southern J., et al. Multi-scale computational modelling in biology and physiology. Prog. Biophys. Mol. Biol. 96 1-3 (2008) 60
6. Ridgway D., Broderick G., and Ellison M.J. Accommodating space, time and randomness in network simulation. Curr. Opin. Biotechnol. 17 5 (2006) 493
7. Yan L., et al. In silico representation of the liver-connecting function to anatomy, physiology and heterogeneous microenvironments. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2 (2004) 853
8. An G. Introduction of an agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation. Theor. Biol. Med. Model. 5 (2008) 11
9. Babaoglu O., et al. Design patterns for biology for distributed computing. ACM Trans. Autonom. Adaptive Syst. 1 1 (2006) 26
10. Remy E., et al. A description of dynamical graphs associated to elementary regulatory circuits. Bioinformatics 19 Suppl. 2 (2003) ii172
11. Simao E., et al. Qualitative modelling of regulated metabolic pathways: application to the tryptophan biosynthesis in E. coli. Bioinformatics 21 Suppl. 2 (2005) ii190
12. Steggles L.J., et al. Qualitatively modelling and analysing genetic regulatory networks: a Petri net approach. Bioinformatics 23 3 (2007) 336
13. Kitano H., and Oda K. Robustness trade-offs and host-microbial symbiosis in the immune system. Mol. Syst. Biol. 2 (2006) 20060022
14. Fisher J., and Henzinger T.A. Executable cell biology. Nat. Biotechnol. 25 11 (2007) 1239
15. Ridgway D., et al. Coarse-grained molecular simulation of diffusion and reaction kinetics in a crowded virtual cytoplasm. Biophys. J. 94 (2008) 3748
16. Bonabeau E. Agent-based modeling: methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. USA 99 Suppl. 3 (2002) 7280
17. Bankes S.C. Agent-based modeling: a revolution?. Proc. Natl. Acad. Sci. USA 99 Suppl. 3 (2002) 7199
18. Grimm V., et al. Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310 (2005) 987
19. Zingarelli B. Nuclear factor-kappaB. Crit. Care Med. 33 Suppl. 12 (2005) S414
20. Warren H.S. Toll-like receptors. Crit. Care Med. 33 Suppl. 12 (2005) S457
21. Han J., and Ulevitch R.J. Limiting inflammatory responses during activation of innate immunity. Nat. Immunol. 6 12 (2005) 1198
22. Klune J.R., Billiar T.R., and Tsung A. HMGB1 preconditioning: therapeutic application for a danger signal?. J. Leukoc. Biol. 83 3 (2008) 558
23. Fan H., and Cook J.A. Molecular mechanisms of endotoxin tolerance. J. Endotoxin Res. 10 2 (2004) 71
24. Wheeler D.S., et al. Induction of endotoxin tolerance enhances bacterial clearance and survival in murine polymicrobial sepsis. Shock 30 3 (2008) 267
25. Matzinger P. The danger model: a renewed sense of self. Science 296 5566 (2002) 301
26. Li C.H., Wang J.H., and Redmond H.P. Bacterial lipoprotein-induced self-tolerance and cross-tolerance to LPS are associated with reduced IRAK-1 expression and MyD88-IRAK complex formation. J. Leukoc. Biol. 79 4 (2006) 867
27. Oda K., and Kitano H. A comprehensive map of the Toll-like receptor signaling network. Mol. Syst. Biol. 2 (2006) 20060015
28. Lipniacki T., et al. Stochastic regulation in early immune response. Biophys. J. 90 3 (2006) 725
29. Zhang G., and Ghosh S. Negative regulation of Toll-like receptor-mediated signaling by Tollip. J. Biol. Chem. 277 9 (2002) 7059
30. Lowe E.L., et al. Ubiquitination and de-ubiquitination: role in regulation of signaling by Toll-like receptors. J. Endotoxin Res. 12 6 (2006) 337
31. Lang T., and Mansell A. The negative regulation of Toll-like receptor and associated pathways. Immunol. Cell Biol. 85 6 (2007) 425
32. Hu X., et al. Lipopolysaccharide induces the antiapoptotic molecules, A1 and A20, in microvascular endothelial cells. Blood 92 8 (1998) 2759
33. Banerjee A., and Gerondakis S. Coordinating TLR-activated signaling pathways in cells of the immune system. Immunol. Cell Biol. 85 6 (2007) 420
34. Marshall J.C. Sepsis: rethinking the approach to clinical research. J. Leukoc. Biol. 83 3 (2008) 471
35. U. Wilensky, NetLogo, 1999. Available from: http://ccl.northwestern.edu/netlogo (accessed 2003).
36. Michie H.R., et al. Detection of circulating tumor necrosis factor after endotoxin administration. N. Engl. J. Med. 318 23 (1988) 1481
37. Solomkin J.S., et al. Alterations of neutrophil responses to tumor necrosis factor alpha and interleukin-8 following human endotoxemia. Infect. Immun. 62 3 (1994) 943
38. Taubner C., et al. Modelling and simulation of the TLR4 pathway with coloured Petri nets. Conf. Proc. IEEE Eng. Med. Biol. Soc. 1 (2006) 2009
39. Kier L.B., Bonchev D., and Buck G.A. Modeling biochemical networks: a cellular-automata approach. Chem. Biodivers. 2 2 (2005) 233
40. Wurthner J., Mukhopadhyay A., and Peimann C.-j. A cellular automaton model of cellular signal transduction. Comput. Biol. Med. 30 (2000) 1
41. R.S. Richards et al., Data-parallel techniques for agent-based tissue modeling on graphical processing units, in: Design Engineering Technical Conference and Computers and Information in Engineering Conference, New York City, NY, 2008.