Mechanistic simulations of inflammation: Current state and future prospects

Mathematical Biosciences

Volumn 217, Issue 1, 2009, Pages 1-10

  Vodovotz, Yoram ^a,b   Constantine, Gregory ^b   Rubin, Jonathan ^b   Csete, Marie ^c   Voit, Eberhard O ^d   An, Gary ^b,e  

^a UNIVERSITY OF PITTSBURGH MEDICAL CENTER   (United States)

^b UNIVERSITY OF PITTSBURGH   (United States)

^c California Institute for Regenerative Medicine   (United States)

^d GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^e NORTHWESTERN UNIVERSITY   (United States)

Author keywords

Agent based modeling; Computer simulation; Endotoxin; Infection; Inflammation; Mathematical modeling; Ordinary differential equations; Parameter estimation; Personalized medicine; Preconditioning; Sepsis; Stochastic simulations; Toll like receptors; Trauma

Indexed keywords

COMPUTER SIMULATION; MATHEMATICAL MODELS; METAL ANALYSIS; MODEL STRUCTURES; PARAMETER ESTIMATION; PATHOLOGY; STOCHASTIC CONTROL SYSTEMS;

AGENT-BASED MODELING; ENDOTOXIN; INFECTION; INFLAMMATION; MATHEMATICAL MODELING; PERSONALIZED MEDICINE; PRECONDITIONING; SEPSIS; STOCHASTIC SIMULATIONS; TOLL-LIKE RECEPTORS; TRAUMA;

ORDINARY DIFFERENTIAL EQUATIONS;

COMPUTER SIMULATION; HOMEOSTASIS; INFECTIOUS DISEASE; NUMERICAL MODEL; PERTURBATION; PHYSIOLOGY; STOCHASTICITY; TOXIN;

ARTICLE; AUTOIMMUNE DISEASE; COMPUTER SIMULATION; DISEASE SIMULATION; HOMEOSTASIS; INFECTION; INFLAMMATION; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; SIGNAL TRANSDUCTION;

COMPUTER SIMULATION; HUMANS; INFLAMMATION; MODELS, BIOLOGICAL;

ANIMALIA;

EID: 57749172576     PISSN: 00255564     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.mbs.2008.07.013     Document Type: Article

References (81)

1. Schwartsburd P.M. Age-promoted creation of a pro-cancer microenvironment by inflammation: pathogenesis of dyscoordinated feedback control. Mech. Ageing Dev. 125 (2004) 581
2. Krabbe K.S., Pedersen M., and Bruunsgaard H. Inflammatory mediators in the elderly. Exp. Gerontol. 39 (2004) 687
3. Caruso C., Lio D., Cavallone L., and Franceschi C. Aging, longevity, inflammation, and cancer. Ann. N. Y. Acad. Sci. 1028 (2004) 1
4. Cottam D.R., Mattar S.G., Barinas-Mitchell E., Eid G., Kuller L., Kelley D.E., and Schauer P.R. The chronic inflammatory hypothesis for the morbidity associated with morbid obesity: implications and effects of weight loss. Obes. Surg. 14 (2004) 589
5. Dandona P., Aljada A., and Bandyopadhyay A. Inflammation: the link between insulin resistance, obesity and diabetes. Trends Immunol. 25 (2004) 4
6. Yan S.F., Ramasamy R., and Schmidt A.M. Mechanisms of disease: advanced glycation end-products and their receptor in inflammation and diabetes complications. Nat. Clin. Pract. Endocrinol. Metab. (2008)
7. Tsirpanlis G. Inflammation in atherosclerosis and other conditions: a response to danger. Kidney Blood Press Res. 28 (2005) 211
8. Nathan C. Points of control in inflammation. Nature 420 (2002) 846
9. Vodovotz Y., Clermont G., Hunt C.A., Lefering R., Bartels J., Seydel R., Hotchkiss J., Ta'asan S., Neugebauer E., and An G. Evidence-based modeling of critical illness: an initial consensus from the Society for Complexity in Acute Illness. J. Crit. Care 22 (2007) 77
10. An G., and Vodovotz Y. Translational systems biology: introduction of an engineering approach to the pathophysiology of the burn patient. J. Burn Care Res. 29 (2008) 277
11. Vodovotz Y., Csete M., Bartels J., Chang S., and An G. Translational systems biology of inflammation. PLoS. Comput. Biol. 4 (2008) 1
12. Alt W., and Lauffenburger D.A. Transient behavior of a chemotaxis system modelling certain types of tissue inflammation. J. Math. Biol. 24 (1987) 691
13. Kumar R., Clermont G., Vodovotz Y., and Chow C.C. The dynamics of acute inflammation. J. Theor. Biol. 230 (2004) 145
14. Reynolds A., Rubin J., Clermont G., Day J., Vodovotz Y., and Ermentrout G.B. A reduced mathematical model of the acute inflammatory response. I. Derivation of model and analysis of anti-inflammation. J. Theor. Biol. 242 (2006) 220
15. Day J., Rubin J., Vodovotz Y., Chow C.C., Reynolds A., and Clermont G. A reduced mathematical model of the acute inflammatory response. II. Capturing scenarios of repeated endotoxin administration. J. Theor. Biol. 242 (2006) 237
16. Vodovotz Y. Deciphering the complexity of acute inflammation using mathematical models. Immunol. Res. 36 (2006) 237
17. An G., Hunt C.A., Clermont G., Neugebauer E., and Vodovotz Y. Challenges and rewards on the road to translational systems biology in acute illness: four case reports from interdisciplinary teams. J. Crit. Care 22 (2007) 169
18. Prince J.M., Levy R.M., Bartels J., Baratt A., Kane III J.M., Lagoa C., Rubin J., Day J., Wei J., Fink M.P., Goyert S.M., Clermont G., Billiar T.R., and Vodovotz Y. In silico and in vivo approach to elucidate the inflammatory complexity of CD14-deficient mice. Mol. Med. 12 (2006) 88
19. Daun S., Rubin J., Vodovotz Y., Roy A., Parker R., and Clermont G. An ensemble of models of the acute inflammatory response to bacterial lipopolysaccharide in rats: results from parameter space reduction. J. Theor. Biol. 253 (2008) 843
20. G. An, I. Lee, Complexity, emergence and pathophysiology: using agent based computer simulation to characterize the non-adaptive inflammatory response (manuscript #344), Int. J. Complex Syst. http://www.interjournal.org, May 2000.
21. An G. Agent-based computer simulation and SIRS: building a bridge between basic science and clinical trials. Shock 16 (2001) 266
22. An G. In silico experiments of existing and hypothetical cytokine-directed clinical trials using agent based modeling. Crit. Care Med. 32 (2004) 2050
23. Mi Q., Rivière B., Clermont G., Steed D.L., and Vodovotz Y. Agent-based model of inflammation and wound healing: insights into diabetic foot ulcer pathology and the role of transforming growth factor-β1. Wound Rep. Reg. 15 (2007) 617
24. Li N.Y.K., Verdolini K., Clermont G., Mi Q., Hebda P.A., and Vodovotz Y. A patient-specific in silico model of inflammation and healing tested in acute vocal fold injury. PLoS ONE 3 (2008) e2789
25. Gillespie D.T. The chemical Langevin equation. J. Chem. Phys. 113 (2000) 297
26. Vodovotz Y., Clermont G., Chow C., and An G. Mathematical models of the acute inflammatory response. Curr. Opin. Crit. Care 10 (2004) 383
27. Fisher J., and Henzinger T.A. Executable cell biology. Nat. Biotechnol. 25 (2007)
28. Ben David I., Price S.E., Bortz D.M., Greineder C.F., Cohen S.E., Bauer A.L., Jackson T.L., and Younger J.G. Dynamics of intrapulmonary bacterial growth in a murine model of repeated microaspiration. Am. J Respir. Cell Mol. Biol. 33 (2005) 476
29. Chow C.C., Clermont G., Kumar R., Lagoa C., Tawadrous Z., Gallo D., Betten B., Bartels J., Constantine G., Fink M.P., Billiar T.R., and Vodovotz Y. The acute inflammatory response in diverse shock states. Shock 24 (2005) 74
30. Lagoa C.E., Bartels J., Baratt A., Tseng G., Clermont G., Fink M.P., Billiar T.R., and Vodovotz Y. The role of initial trauma in the host's response to injury and hemorrhage: insights from a comparison of mathematical simulations and hepatic transcriptomic analysis. Shock 26 (2006) 592
31. Lipniacki T., Paszek P., Brasier A.R., Luxon B.A., and Kimmel M. Stochastic regulation in early immune response. Biophys. J. 90 (2006)
32. Lipniacki T., Paszek P., Marciniak-Czochra A., Brasier A.R., and Kimmel M. Transcriptional stochasticity in gene expression. J. Theor. Biol. 238 (2006)
33. Ridgway D., Broderick G., Lopez-Campistrous A., Ru'aini M., Winter P., Hamilton M., Boulanger P., Kovalenko A., and Ellison M.J. Coarse-grained molecular simulation of diffusion and reaction kinetics in a crowded virtual cytoplasm. Biophys. J. (2008)
34. Hlavacek W.S., Faeder J.R., Blinov M.L., Posner R.G., Hucka M., and Fontana W. Rules for modeling signal-transduction systems. Sci. STKE 2006 (2006) re6
35. Chiam K.H., Tan C.M., Bhargava V., and Rajagopal G. Hybrid simulations of stochastic reaction-diffusion processes for modeling intracellular signaling pathways. Phys. Rev. E 74 (2006) 051910
36. Wylie D.C., Hori Y., Dinner A.R., and Chakraborty A.K. A hybrid deterministic-stochastic algorithm for modeling cell signaling dynamics in spatially inhomogeneous environments and under the influence of external fields. J. Phys. Chem. 110 (2006)
37. J. Wu, E.O. Voit, Hybrid Modeling In biochemical systems theory by means of functional Petri nets, J. Bioinformatics Comput. Biol. (2008), in press.
38. Goldstein B., Faeder J.R., and Hlavacek W.S. Mathematical and computational models of immune-receptor signalling. Nat. Rev. Immunol. 4 (2004) 445
39. Raes P.G., and Ballaro B. Reaction-diffusion equations for simulation of calcium signalling in cell systems. Riv. Biol. 97 (2004) 443
40. Ermentrout G.B., and Edelstein-Keshet L. Cellular automata approaches to biological modeling. J. Theor. Biol. 160 (1993) 97
41. Bonabeau E. Agent-based modeling: methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. USA 99 Suppl. 3 (2002) 7280
42. G. An, I. Lee, Complexity, emergence and pathophysiology: Using agent based computer simulation to characterize the non-adaptive inflammatory response (manuscript #344), Int. J. Complex Syst., May 2000.
43. Beutler B., and Rietschel E.T. Innate immune sensing and its roots: the story of endotoxin. Nat. Rev. Immunol. 3 (2003) 169
44. West M.A., and Heagy W. Endotoxin tolerance: a review. Crit. Care Med. 30 (2002) S64
45. Cavaillon J.M., Adrie C., Fitting C., and dib-Conquy M. Endotoxin tolerance: is there a clinical relevance?. J. Endotoxin. Res. 9 (2003) 101
46. Means T.K., Golenbock D.T., and Fenton M.J. The biology of Toll-like receptors. Cytokine Growth Factor Rev. 11 (2000) 219
47. Beutler B. Innate immunity: an overview. Mol. Immunol. 40 (2004) 845
48. Rivière B., Epshteyn Y., Swigon D., and Vodovotz Y. A simple mathematical model of signaling resulting from the binding of lipopolysaccharide with Toll-like receptor 4 demonstrates inherent preconditioning behavior. Math. Biosci. 217 (2008) 19-26
49. Shin H.J., Lee H., Park J.D., Hyun H.C., Sohn H.O., Lee D.W., and Kim Y.S. Kinetics of binding of LPS to recombinant CD14, TLR4, and MD-2 proteins. Mol. Cells 24 (2007) 119
50. Foteinou P.T., Calvano S.E., Lowry S.F., and Androulakis I.P. Modeling endotoxin-induced systemic inflammation using an indirect response approach. Math. Biosci. 217 (2008) 27-42
51. Clermont G., Bartels J., Kumar R., Constantine G., Vodovotz Y., and Chow C. In silico design of clinical trials: a method coming of age. Crit. Care Med. 32 (2004) 2061
52. An G. A model of TLR4 signaling and tolerance using a qualitative, particle event-based method: Introduction of Spatially Configured Stochastic Reaction Chambers (SCSRC). Math. Biosci. 217 (2008) 43-52
53. An G., and Faeder J.R. Detailed qualitative dynamic knowledge representation using a BioNetGen model of TLR-4 signaling and preconditioning. Math. Biosci. 217 (2008) 53-63
54. Faeder J.R., Blinov M.L., and Hlavacek W.S. Rule-based modeling of biochemical systems with BioNetGen. In: Maly I.V. (Ed). Methods in Molecular Biology: Systems Biology (2008), Humana, Totowa, NJ
55. Gutenkunst R.N., Waterfall J.J., Casey F.P., Brown K.S., Myers C.R., and Sethna J.P. Universally sloppy parameter sensitivities in systems biology models. PLoS. Comput. Biol. 3 (2007) 1871
56. de Jong H., Goiuze J.-L., Hernandez C., Page M., Sari T., and Geiselmann J. Qualitative simulation of genetic regulatory networks using piecewise-linear models. Bull. Math. Biol. 66 (2004) 301
57. Veber P., Le Borgne M., Siegel A., Lagarrigue S., and Radulescu O. Complex qualitative models in biology: a new approach. Complexus 2 (2004) 140
58. An G. Introduction of a agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation. Theor. Biol. Med. Model. 5 (2008)
59. An G. Dynamic knowledge representation using agent based modeling: ontology instantiation and verification of conceptual models. In: Maly I. (Ed). Systems Biology: Methods in Molecular Biology Series (2008), Humana, Totowa, NJ
60. Voit E.O. A systems-theoretical framework for health and disease. Math. Biosci. 217 (2008) 11-18
61. Klune J.R., Billiar T.R., and Tsung A. HMGB1 preconditioning: therapeutic application for a danger signal?. J. Leukoc. Biol. 83 (2008) 558
62. Wang H., Bloom O., Zhang M., Vishnubhakat J.M., Ombrellino M., Che J., Frazier A., Yang H., Ivanova S., Borovikova L., Manogue K.R., Faist E., Abraham E., Andersson J., Andersson U., Molina P.E., Abumrad N.N., Sama A., and Tracey K.J. HMG-1 as a late mediator of endotoxin lethality in mice. Science 285 (1999) 248
63. Yang H., Ochani M., Li J., Qiang X., Tanovic M., Harris H.E., Susarla S.M., Ulloa L., Wang H., DiRaimo R., Czura C.J., Wang H., Roth J., Warren H.S., Fink M.P., Fenton M.J., Andersson U., and Tracey K.J. Reversing established sepsis with antagonists of endogenous high-mobility group box 1. Proc. Natl. Acad. Sci. USA 101 (2004) 296
64. Tsung A., Sahai R., Tanaka H., Nakao A., Fink M.P., Lotze M.T., Yang H., Li J., Tracey K.J., Geller D.A., and Billiar T.R. The nuclear factor HMGB1 mediates hepatic injury after murine liver ischemia-reperfusion. J.Exp. Med. 201 (2005) 1135
65. Rifkin I.R., Leadbetter E.A., Busconi L., Viglianti G., and Marshak-Rothstein A. Toll-like receptors, endogenous ligands, and systemic autoimmune disease. Immunol. Rev. 204 (2005) 27
66. Izuishi K., Tsung A., Jeyabalan G., Critchlow N.D., Li J., Tracey K.J., DeMarco R.A., Lotze M.T., Fink M.P., Geller D.A., and Billiar T.R. Cutting edge: high-mobility group box 1 preconditioning protects against liver ischemia-reperfusion injury. J. Immunol. 176 (2006) 7154
67. Tsung A., Zheng N., Jeyabalan G., Izuishi K., Klune J.R., Geller D.A., Lotze M.T., Lu L., and Billiar T.R. Increasing numbers of hepatic dendritic cells promote HMGB1-mediated ischemia-reperfusion injury. J. Leukoc. Biol. 81 (2007) 119
68. Matzinger P. The danger model: a renewed sense of self. Science 296 (2002) 301
69. Raman K.G., Sappington P.L., Yang R., Levy R.M., Prince J.M., Liu S., Watkins S.K., Schmidt A.M., Billiar T.R., and Fink M.P. The role of RAGE in the pathogenesis of intestinal barrier dysfunction after hemorrhagic shock. Am. J Physiol. Gastrointest. Liver Physiol. 291 (2006) G556
70. Schulz A.R. Enzyme Kinetics: From Diastase to Multi-Enzyme Systems (1994), Cambridge University, Cambridge, UK
71. Savageau M.A. Biochemical systems analysis. I. Some mathematical properties of the rate law for the component enzymatic reactions. J. Theor. Biol. 25 (1969)
72. Savageau M.A. Biochemical Systems Analysis: A Study of Function and Design in Molecular Biology (1976), Addison-Wesley Pub. Co. Advanced Book Program, Reading, MA
73. Torres N.V., and Voit E.O. Pathway Analysis and Optimization in Metabolic Engineering (2002), Cambridge University, Cambridge, UK
74. Voit E.O. Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists (2000), Cambridge University, Cambridge, UK
75. Constantine G., Bartels J., Buliga M., Clermont G., and Vodovotz Y. An optimization algorithm based on optimal linear codes. J. Pure Appl. Math. (2007) 10.1007/s10589-007-9118-9
76. Kumar R., Chow C.C., Bartels J., Clermont G., and Vodovotz Y. A mathematical simulation of the inflammatory response to anthrax infection. Shock 29 (2008) 104
77. Cohen A.M., and Hersh W.R. A survey of current work in biomedical text mining. Brief Bioinform. 6 (2005) 57
78. Spasic I., Ananiadou S., McNaught J., and Kumar A. Text mining and ontologies in biomedicine: making sense of raw text. Brief Bioinform. 6 (2005) 239
79. Marshall J.C. Through a glass darkly: the brave new world of in silico modeling. Crit. Care Med. 32 (2004) 2157
80. Food and Drug Administration. Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products. Ref. Type: Report. 2004, pp. 1-38.
81. NIH Roadmap for Medical Research: Research Teams, 2006. http://nihroadmap.nih.gov/2008initiatives.asp.