Translational Systems Biology: Concepts and Practice for the Future of Biomedical Research

Translational Systems Biology: Concepts and Practice for the Future of Biomedical Research

Volumn , Issue , 2014, Pages 1-162

  Vodovotz, Yoram ^a   An, Gary ^b  

^a MCGOWAN INSTITUTE FOR REGENERATIVE MEDICINE   (United States)

^b UNIVERSITY OF CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84941703249     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1016/C2012-0-00091-9     Document Type: Book

References (475)

1. U.S. Food and Drug Administration. Innovation or stagnation: challenge and opportunity on the critical path to new medical products; March 2004.
2. Brennan RJ, Nandy R Complex humanitarian emergencies: a major global health challenge. Emerg Med (Fremantle) 2001, 13(2):147-156.
3. Hewitson J, Brink J, Zilla P The challenge of pediatric cardiac services in the developing world. Semin Thorac Cardiovasc Surg 2002, 14(4):340-345.
4. Bittles AH, Black ML Evolution in health and medicine Sackler colloquium: consanguinity, human evolution, and complex diseases. Proc Natl Acad Sci USA 2010, 107(Suppl. 1):1779-1786.
5. de Montmollin E, Annane D Year in review 2010: critical care-multiple organ dysfunction and sepsis. Crit Care 2011, 15(6):236.
6. Gustot T Multiple organ failure in sepsis: prognosis and role of systemic inflammatory response. Curr Opin Crit Care 2011, 17(2):153-159.
7. An G, Namas R, Vodovotz Y Sepsis: from pattern to mechanism and back. Crit Rev Biomed Eng 2012, 40:341-351.
8. Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med 2001, 29(7):1303-1310.
9. Namas R, Ghuma A, Hermus L, Zamora R, Okonkwo DO, Billiar TR, et al. The acute inflammatory response in trauma/hemorrhage and traumatic brain injury: current state and emerging prospects. Libyan J Med 2009, 4:97-103.
10. Patton GC, Coffey C, Sawyer SM, Viner RM, Haller DM, Bose K, et al. Global patterns of mortality in young people: a systematic analysis of population health data. Lancet 2009, 374(9693):881-892.
11. World Health Organization. Young people: health risks and solutions; 2011. Fact Sheet No. 345 (Updated May 2014 ). http://www.who.int/mediacentre/factsheets/fs345/en/.
12. Nathan C Points of control in inflammation. Nature 2002, 420(6917):846-852.
13. Namas R, Ghuma A, Torres A, Polanco P, Gomez H, Barclay D, et al. An adequately robust early TNFα response is a hallmark of survival following trauma/hemorrhage. PLoS One 2009, 4(12):e8406.
14. Neunaber C, Zeckey C, Andruszkow H, Frink M, Mommsen P, Krettek C, et al. Immunomodulation in polytrauma and polymicrobial sepsis-where do we stand?. Recent Pat Inflamm Allergy Drug Discov 2011, 5(1):17-25.
15. An G, Nieman G, Vodovotz Y Computational and systems biology in trauma and sepsis: current state and future perspectives. Int J Burns Trauma 2012, 2(1):1-10. PMID: 22928162.
16. Complex systems and computational biology approaches to acute inflammation 2013, Springer, New York, NY, http://dx.doi.org/10.1007/978-1-4614-8008-2. G. An, Y. Vodovotz (Eds.).
17. Vodovotz Y, Billiar TR In silico modeling: methods and applications to trauma and sepsis. Crit Care Med 2013, 41:2008-2014.
18. Vodovotz Y, Csete M, Bartels J, Chang S, An G Translational systems biology of inflammation. PLoS Comput Biol 2008, 4:1-6.
19. Beard J. 50 Year of Bridging the Gap: DARPA's Bio-Revolution. Defense Advanced Research Project Agency, ed.
20. Nathan C Points of control in inflammation. Nature 2002, 420(6917):846-852.
21. Nathan C, Sporn M Cytokines in context. J Cell Biol 1991, 113:981.
22. Tracey KJ The inflammatory reflex. Nature 2002, 420(6917):853-859.
23. Seok J, Warren HS, Cuenca AG, Mindrinos MN, Baker HV, Xu W, et al. Genomic responses in mouse models poorly mimic human inflammatory diseases. Proc Natl Acad Sci USA 2013, 110(9):3507-3512.
24. An G, Bartels J, Vodovotz Y In silico augmentation of the drug development pipeline: examples from the study of acute inflammation. Drug Dev Res 2011, 72:1-14.
25. Csete ME, Doyle JC Reverse engineering of biological complexity. Science 2002, 295(5560):1664-1669.
26. Mesarovic MD, Sreenath SN, Keene JD Search for organising principles: understanding in systems biology. Syst Biol (Stevenage) 2004, 1(1):19-27.
27. Osuchowski MF, Remick DG, Lederer JA, Lang CH, Aasen AO, Aibiki M, et al. Abandon the mouse research ship? Not just yet!. Shock 2014, 41(6):463-475.
28. Hausheer FH, Kochat H, Parker AR, Ding D, Yao S, Hamilton SE, et al. New approaches to drug discovery and development: a mechanism-based approach to pharmaceutical research and its application to BNP7787, a novel chemoprotective agent. Cancer Chemother Pharmacol 2003, 52(Suppl. 1):S3-S15.
29. Michelson S, Sehgal A, Friedrich C In silico prediction of clinical efficacy. Curr Opin Biotechnol 2006, 17(6):666-670.
30. Vedani A, Dobler M, Lill MA The challenge of predicting drug toxicity in silico. Basic Clin Pharmacol Toxicol 2006, 99(3):195-208.
31. Vodovotz Y, et al. Translational systems biology of inflammation. PLoS Comput Biol 2008, 4:1-6.
32. Vodovotz Y, An G Systems biology and inflammation. Systems biology in drug discovery and development: methods and protocols 2009, 181-201. Springer Science and Business Media, Totowa, NJ. Q. Yan (Ed.).
33. Grosser T, Yu Y, Fitzgerald GA Emotion recollected in tranquility: lessons learned from the COX-2 saga. Annu Rev Med 2010, 61:17-33.
34. Stronks K, Wieringa NF, Hardon A Confronting diversity in the production of clinical evidence goes beyond merely including under-represented groups in clinical trials. Trials 2013, 14:177.
35. Raimond V, Josselin JM, Rochaix L HTA agencies facing model biases: the case of type 2 diabetes. Pharmacoeconomics 2014.
36. Schiller P, et al. Quality of reporting of clinical non-inferiority and equivalence randomised trials-update and extension. Trials 2012, 13:214.
37. Smith GC, Pell JP Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of randomised controlled trials. BMJ 2003, 327(7429):1459-1461.
38. Vodovotz Y, Clermont G, Hunt CA, Lefering R, Bartels J, Seydel R, et al. Evidence-based modeling of critical illness: an initial consensus from the Society for Complexity in Acute Illness. J Crit Care 2007, 22:77-84.
39. An G, Hunt CA, Clermont G, Neugebauer E, Vodovotz Y Challenges and rewards on the road to translational systems biology in acute illness: four case reports from interdisciplinary teams. J Crit Care 2007, 22:169-175.
40. Csete ME, Doyle JC Reverse engineering of biological complexity. Science 2002, 295(5560):1664-1669.
41. Godin PJ, Buchman TG Uncoupling of biological oscillators: a complementary hypothesis concerning the pathogenesis of multiple organ dysfunction syndrome. Crit Care Med 1996, 24(7):1107-1116.
42. Goldberger AL, Rigney DR, West BJ Chaos and fractals in human physiology. Sci Am 1990, 262(2):42-49.
43. Dick TE, Molkov Y, Nieman G, Hsieh Y, Jacono FJ, Doyle J, et al. Linking inflammation and cardiorespiratory variability in sepsis via computational modeling. Front Physiol 2012, 3:222.
44. An G, Bartels J, Vodovotz Y In silico augmentation of the drug development pipeline: examples from the study of acute inflammation. Drug Dev Res 2011, 72:1-14.
45. Vodovotz Y, Clermont G, Hunt CA, Lefering R, Bartels J, Seydel R, et al. Evidence-based modeling of critical illness: an initial consensus from the Society for Complexity in Acute Illness. J Crit Care 2007, 22:77-84.
46. Kitano H Computational systems biology. Nature 2002, 420(6912):206-210.
47. Kitano H Systems biology: a brief overview. Science 2002, 295(5560):1662-1664.
48. Sauer U, Heinemann M, Zamboni N Genetics. Getting closer to the whole picture. Science 2007, 316(5824):550-551.
49. Abrams WR, Barber CA, McCann K, Tong G, Chen Z, Mauk MG, et al. Development of a microfluidic device for detection of pathogens in oral samples using upconverting phosphor technology (UPT). Ann NY Acad Sci 2007, 1098:375-388.
50. Ahrens CH, Wagner U, Rehrauer HK, Turker C, Schlapbach R Current challenges and approaches for the synergistic use of systems biology data in the scientific community. EXS 2007, 97:277-307.
51. Barker PE, Wagner PD, Stein SE, Bunk DM, Srivastava S, Omenn GS Standards for plasma and serum proteomics in early cancer detection: a needs assessment report from the national institute of standards and technology-National Cancer Institute Standards, Methods, Assays, Reagents and Technologies Workshop, August 18-19, 2005. Clin Chem 2006, 52(9):1669-1674.
52. Brownstein BH, Logvinenko T, Lederer JA, Cobb JP, Hubbard WJ, Chaudry IH, et al. Commonality and differences in leukocyte gene expression patterns among three models of inflammation and injury. Physiol Genomics 2006, 24(3):298-309.
53. Calvano SE, Xiao W, Richards DR, Felciano RM, Baker HV, Cho RJ, et al. A network-based analysis of systemic inflammation in humans. Nature 2005, 437(7061):1032-1037.
54. Cobb JP, Mindrinos MN, Miller-Graziano C, Calvano SE, Baker HV, Xiao W, et al. Application of genome-wide expression analysis to human health and disease. Proc Natl Acad Sci USA 2005, 102(13):4801-4806.
55. Cobb JP, O'Keefe GE Injury research in the genomic era. Lancet 2004, 363(9426):2076-2083.
56. Klass M, Gavrikov V, Drury D, Stewart B, Hunter S, Denson DD, et al. Intravenous mononuclear marrow cells reverse neuropathic pain from experimental mononeuropathy. Anesth Analg 2007, 104(4):944-948.
57. Kourtidis A, Eifert C, Conklin DS RNAi applications in target validation. Ernst Schering Res Found Workshop 2007, (61):1-21.
58. Liu T, Qian WJ, Gritsenko MA, Xiao W, Moldawer LL, Kaushal A, et al. High dynamic range characterization of the trauma patient plasma proteome. Mol Cell Proteomics 2006, 5(10):1899-1913.
59. Steinfath M, Repsilber D, Scholz M, Walther D, Selbig J Integrated data analysis for genome-wide research. EXS 2007, 97:309-329.
60. Tanke HJ Genomics and proteomics: the potential role of oral diagnostics. Ann NY Acad Sci 2007, 1098:330-334.
61. Nathan C Points of control in inflammation. Nature 2002, 420(6917):846-852.
62. Hart J Inflammation. 2: its role in the healing of chronic wounds. J Wound Care 2002, 11(7):245-249.
63. Hart J Inflammation. 1: its role in the healing of acute wounds. J Wound Care 2002, 11(6):205-209.
64. An G, Hunt CA, Clermont G, Neugebauer E, Vodovotz Y Challenges and rewards on the road to translational systems biology in acute illness: four case reports from interdisciplinary teams. J Crit Care 2007, 22(2):169-175.
65. An G, Nieman G, Vodovotz Y Computational and systems biology in trauma and sepsis: current state and future perspectives. Int J Burns Trauma 2012, 2:1-10.
66. An G, Nieman G, Vodovotz Y Computational and systems biology in trauma and sepsis: current state and future perspectives. Int J Burns Trauma 2012, 2:1-10.
67. An G, Namas R, Vodovotz Y Sepsis: from pattern to mechanism and back. Crit Rev Biomed Eng 2012, 40:341-351.
68. An G, Nieman G, Vodovotz Y Toward computational identification of multiscale tipping points in multiple organ failure. Ann Biomed Eng 2012, 40:2412-2424.
69. Kitano H Systems biology: a brief overview. Science 2002, 295(5560):1662-1664.
70. Csete ME, Doyle JC Reverse engineering of biological complexity. Science 2002, 295(5560):1664-1669.
71. Young SS, Lam RL, Welch WJ Initial compound selection for sequential screening. Curr Opin Drug Discov Dev 2002, 5(3):422-427.
72. Rovira X, Pin JP, Giraldo J The asymmetric/symmetric activation of GPCR dimers as a possible mechanistic rationale for multiple signalling pathways. Trends Pharmacol Sci 2010, 31(1):15-21.
73. An G Introduction of an agent based multi-scale modular architecture for dynamic knowledge representation of acute inflammation. Theor Biol Med Model 2008, 5:11.
74. An G Closing the scientific loop: bridging correlation and causality in the petaflop age. Sci Transl Med 2010, 2:41ps34.
75. Vodovotz Y, Constantine G, Faeder J, Mi Q, Rubin J, Sarkar J, et al. Translational systems approaches to the biology of inflammation and healing. Immunopharmacol Immunotoxicol 2010, 32:181-195.
76. Vodovotz Y Translational systems biology of inflammation and healing. Wound Repair Regen 2010, 18(1):3-7.
77. Mi Q, Li NYK, Ziraldo C, Ghuma A, Mikheev M, Squires R, et al. Translational systems biology of inflammation: potential applications to personalized medicine. Per Med 2010, 7:549-559.
78. Vodovotz Y, Csete M, Bartels J, Chang S, An G Translational systems biology of inflammation. PLoS Comput Biol 2008, 4:1-6.
79. Vodovotz Y, An G Systems biology and inflammation. Systems biology in drug discovery and development: methods and protocols 2009, 181-201. Springer Science & Business Media, Totowa, NJ. Q. Yan (Ed.).
80. An G, Bartels J, Vodovotz Y In silico augmentation of the drug development pipeline: examples from the study of acute inflammation. Drug Dev Res 2011, 72:1-14.
81. Vodovotz Y, Clermont G, Chow C, An G Mathematical models of the acute inflammatory response. Curr Opin Crit Care 2004, 10:383-390.
82. National Research Council (U.S.), Committee on A Framework for Developing a New Taxonomy of Disease Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease 2011, National Academies Press, Washington, DC, xiii, 128 pp.
83. Clermont G, Bartels J, Kumar R, Constantine G, Vodovotz Y, Chow C In silico design of clinical trials: a method coming of age. Crit Care Med 2004, 32:2061-2070.
84. An G In silico experiments of existing and hypothetical cytokine-directed clinical trials using agent based modeling. Crit Care Med 2004, 32:2050-2060.
85. An G Agent-based computer simulation and SIRS: building a bridge between basic science and clinical trials. Shock 2001, 16(4):266-273.
86. Weston AD, Hood L Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J Proteome Res 2004, 3(2):179-196.
87. Buchman TG, Cobb JP, Lapedes AS, Kepler TB Complex systems analysis: a tool for shock research. Shock 2001, 16(4):248-251.
88. Tjardes T, Neugebauer E Sepsis research in the next millennium: concentrate on the software rather than the hardware. Shock 2002, 17(1):1-8.
89. Hong FT The role of pattern recognition in creative problem solving: a case study in search of new mathematics for biology. Prog Biophys Mol Biol 2013, 113(1):181-215.
90. Godin PJ, Buchman TG Uncoupling of biological oscillators: a complementary hypothesis concerning the pathogenesis of multiple organ dysfunction syndrome. Crit Care Med 1996, 24(7):1107-1116.
91. Annane D, Baudrie V, Blanc AS, Laude D, Raphael JC, Elghozi JL Short-term variability of blood pressure and heart rate in Guillain-Barre syndrome without respiratory failure. Clin Sci 1999, 96(6):613-621.
92. Korach M, Sharshar T, Jarrin I, Fouillot JP, Raphael JC, Gajdos P, et al. Cardiac variability in critically ill adults: influence of sepsis. Crit Care Med 2001, 29(7):1380-1385.
93. Piepoli M, Garrard CS, Kontoyannis DA, Bernardi L Autonomic control of the heart and peripheral vessels in human septic shock. Intensive Care Med 1995, 21(2):112-119.
94. Fairchild KD, Saucerman JJ, Raynor LL, Sivak JA, Xiao Y, Lake DE, et al. Endotoxin depresses heart rate variability in mice: cytokine and steroid effects. Am J Physiol Regul Integr Comp Physiol 2009, 297(4):R1019-R1027.
95. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Eur Heart J 1996;17(3):354-81.
96. Kleiger RE, Stein PK, Bigger JT Heart rate variability: measurement and clinical utility. Ann Noninvasive Electrocardiol 2005, 10(1):88-101.
97. Pomeranz B, Macaulay RJ, Caudill MA, Kutz I, Adam D, Gordon D, et al. Assessment of autonomic function in humans by heart rate spectral analysis. Am J Physiol 1985, 248(1 Pt 2):H151-H153.
98. Barnaby D, Ferrick K, Kaplan DT, Shah S, Bijur P, Gallagher EJ Heart rate variability in emergency department patients with sepsis. Acad Emerg Med 2002, 9(7):661-670.
99. Chen WL, Kuo CD Characteristics of heart rate variability can predict impending septic shock in emergency department patients with sepsis. Acad Emerg Med 2007, 14(5):392-397.
100. Pontet J, Contreras P, Curbelo A, Medina J, Noveri S, Bentancourt S, et al. Heart rate variability as early marker of multiple organ dysfunction syndrome in septic patients. J Crit Care 2003, 18(3):156-163.
101. Ahmad S, Ramsay T, Huebsch L, Flanagan S, McDiarmid S, Batkin I, et al. Continuous multi-parameter heart rate variability analysis heralds onset of sepsis in adults. PLoS One 2009, 4(8):e6642.
102. Magder S Bench-to-bedside review: ventilatory abnormalities in sepsis. Crit Care 2009, 13(1):202.
103. Preas HL, Jubran A, Vandivier RW, Reda D, Godin PJ, Banks SM, et al. Effect of endotoxin on ventilation and breath variability: role of cyclooxygenase pathway. Am J Respir Crit Care Med 2001, 164(4):620-626.
104. Aerts JM, Haddad WM, An G, Vodovotz Y From data patterns to mechanistic models in acute critical illness. J Crit Care 2014.
105. Ayton GS, Noid WG, Voth GA Multiscale modeling of biomolecular systems: in serial and in parallel. Curr Opin Struct Biol 2007, 17(2):192-198.
106. Hunt CA, Ropella GE, Lam T, Gewitz AD Relational grounding facilitates development of scientifically useful multiscale models. Theor Biol Med Model 2011, 8:35.
107. Food and Drug Administration. Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products. 2004 March 2004.
108. Gang Y, Malik M Heart rate variability in critical care medicine. Curr Opin Crit Care 2002, 8(5):371-375.
109. Moorman JR, Lake DE, Griffin MP Heart rate characteristics monitoring for neonatal sepsis. IEEE Trans Biomed Eng 2006, 53(1):126-132.
110. An G Phenomenological issues related to the measurement, mechanisms and manipulation of complex biological systems. Crit Care Med 2006, 34(1):245-246.
111. Goldberger AL, Amaral LA, Hausdorff JM, Ivanov P, Peng CK, Stanley HE Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci USA 2002, 99(Suppl. 1):2466-2472.
112. Chung TP, Laramie JM, Province M, Cobb JP Functional genomics of critical illness and injury. Crit Care Med 2002, 30(Suppl. 1):S51-S57.
113. Cobb JP, O'Keefe GE Injury research in the genomic era. Lancet 2004, 363(9426):2076-2083.
114. Wurfel MM Microarray-based analysis of ventilator-induced lung injury. Proc Am Thorac Soc 2007, 4(1):77-84.
115. Winkelman C Inflammation and genomics in the critical care unit. Crit Care Nurs Clin North Am 2008, 20(2):213-221. vi.
116. Nguyen A, Yaffe MB Proteomics and systems biology approaches to signal transduction in sepsis. Crit Care Med 2003, 31(Suppl. 1):S1-S6.
117. Bauer M, Reinhart K Molecular diagnostics of sepsis-where are we today?. Int J Med Microbiol 2010, 300(6):411-413.
118. Claus RA, Otto GP, Deigner HP, Bauer M Approaching clinical reality: markers for monitoring systemic inflammation and sepsis. Curr Mol Med 2010, 10(2):227-235.
119. Langley RJ, Tsalik EL, van Velkinburgh JC, Glickman SW, Rice BJ, Wang C, et al. An integrated clinico-metabolomic model improves prediction of death in sepsis. Sci Transl Med 2013, 5(195):195ra95.
120. Serkova NJ, Standiford TJ, Stringer KA The emerging field of quantitative blood metabolomics for biomarker discovery in critical illnesses. Am J Respir Crit Care Med 2011, 184:647-655.
121. Gough NR, Yaffe MB Focus issue: conquering the data mountain. Sci Signal 2011, 4(160):eg2.
122. Calvano SE, Xiao W, Richards DR, Felciano RM, Baker HV, Cho RJ, et al. A network-based analysis of systemic inflammation in humans. Nature 2005, 437:1032-1037.
123. Liu T, Qian WJ, Gritsenko MA, Xiao W, Moldawer LL, Kaushal A, et al. High dynamic range characterization of the trauma patient plasma proteome. Mol Cell Proteomics 2006, 5(10):1899-1913.
124. McDunn JE, Husain KD, Polpitiya AD, Burykin A, Ruan J, Li Q, et al. Plasticity of the systemic inflammatory response to acute infection during critical illness: development of the riboleukogram. PLoS One 2008, 3(2):e1564.
125. Warren HS, Elson CM, Hayden DL, Schoenfeld DA, Cobb JP, Maier RV, et al. A genomic score prognostic of outcome in trauma patients. Mol Med 2009, 15(7-8):220-227.
126. Cobb JP, Moore EE, Hayden DL, Minei JP, Cuschieri J, Yang J, et al. Validation of the riboleukogram to detect ventilator-associated pneumonia after severe injury. Ann Surg 2009, 250:531-539.
127. Qian WJ, Petritis BO, Kaushal A, Finnerty CC, Jeschke MG, Monroe ME, et al. Plasma proteome response to severe burn injury revealed by 18O-labeled "universal" reference-based quantitative proteomics. J Proteome Res 2010, 9(9):4779-4789.
128. Zhou B, Xu W, Herndon D, Tompkins R, Davis R, Xiao W, et al. Analysis of factorial time-course microarrays with application to a clinical study of burn injury. Proc Natl Acad Sci USA 2010, 107(22):9923-9928.
129. Mi Q, Constantine G, Ziraldo C, Solovyev A, Torres A, Namas R, et al. A dynamic view of trauma/hemorrhage-induced inflammation in mice: principal drivers and networks. PLoS One 2011, 6:e19424.
130. Janes KA, Albeck JG, Gaudet S, Sorger PK, Lauffenburger DA, Yaffe MB A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science 2005, 310(5754):1646-1653.
131. Namas R, Namas R, Lagoa C, Barclay D, Mi Q, Zamora R, et al. Hemoadsorption reprograms inflammation in experimental gram-negative septic fibrin peritonitis: insights from in vivo and in silico studies. Mol Med 2012, 18:1366-1374.
132. Nieman K, Brown D, Sarkar J, Kubiak B, Ziraldo C, Vieau C, et al. A two-compartment mathematical model of endotoxin-induced inflammatory and physiologic alterations in swine. Crit Care Med 2012, 40:1052-1063.
133. Khatri P, Sirota M, Butte AJ Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 2012, 8(2):e1002375.
134. Ljung L System identification: theory for the user 1987, Prentice Hall, Englewood Cliffs, NJ.
135. Janes KA, Yaffe MB Data-driven modelling of signal-transduction networks. Nat Rev Mol Cell Biol 2006, 7(11):820-828.
136. Young PC Recursive estimation and time series analysis 2011, Springer, Berlin, Germany. 2nd ed.
137. Nicholson AJ An outline of the dynamics of animal populations. Aust J Zool 1954, 2:9-65.
138. Aerts JM, Berckmans D, Saevels P, Decuypere E, Buyse J Modelling the static and dynamic response of total heat production of broiler chickens to step changes in air temperature and light intensity. Br Poult Sci 2000, 41:651-659.
139. Ingolia NT, Weissman JS Systems biology: reverse engineering the cell. Nature 2008, 454(7208):1059-1062.
140. Amirpour Haredasht S, Barrios JM, Maes P, Verstraeten WW, Clement J, Ducoffre G, et al. A dynamic data-based model describing nephropathia epidemica in Belgium. Biosyst Eng 2011, 109:77-89.
141. Scheffer M, Bascompte J, Brock WA, Brovkin V, Carpenter SR, Dakos V, et al. Early-warning signals for critical transitions. Nature 2009, 461(7260):53-59.
142. Van LK, Guiza F, Meyfroidt G, Aerts JM, Ramon J, Blockeel H, et al. Prediction of clinical conditions after coronary bypass surgery using dynamic data analysis. J Med Syst 2010, 34(3):229-239.
143. Namas R, Zamora R, Namas R, An G, Doyle J, Dick TE, et al. Sepsis: something old, something new, and a systems view. J Crit Care 2012, 27:314e1-314e11.
144. An G, Namas R, Vodovotz Y Sepsis: from pattern to mechanism and back. Crit Rev Biomed Eng 2012, 40:341-351.
145. Dick TE, Molkov Y, Nieman G, Hsieh Y, Jacono FJ, Doyle J, et al. Linking inflammation and cardiorespiratory variability in sepsis via computational modeling. Front Physiol 2012, 3:222.
146. Kwan A, Hubank M, Rashid A, Klein N, Peters MJ Transcriptional instability during evolving sepsis may limit biomarker based risk stratification. PLoS One 2013, 8(3):e60501.
147. Azhar N, Ziraldo C, Barclay D, Rudnick D, Squires R, Vodovotz Y Analysis of serum inflammatory mediators identifies unique dynamic networks associated with death and spontaneous survival in pediatric acute liver failure. PLoS One 2013, 8:e78202.
148. An G, Nieman G, Vodovotz Y Toward computational identification of multiscale tipping points in multiple organ failure. Ann Biomed Eng 2012, 40:2412-2424.
149. Ziraldo C, Vodovotz Y, Namas RA, Almahmoud K, Tapias V, Mi Q, et al. Central role for MCP-1/CCL2 in injury-induced inflammation revealed by in vitro, in silico, and clinical studies. PLoS One 2013, 8(12):e79804.
150. Zaaqoq AM, Namas R, Almahmoud K, Krishnan S, Azhar N, Mi Q, et al. IP-10, a potential driver of neurally controlled IL-10 and morbidity in human blunt trauma. Crit Care Med 2014.
151. Dehmer M, Mueller LAJ, Emmert-Streib F Quantitative network measures as biomarkers for classifying prostate cancer disease states: a systems approach to diagnostic biomarkers. PLoS One 2013, 8(11):e77602.
152. Aldridge BB, Haller G, Sorger PK, Lauffenburger DA Direct Lyapunov exponent analysis enables parametric study of transient signalling governing cell behaviour. Syst Biol(Stevenage) 2006, 153(6):425-432.
153. Aldridge BB, Burke JM, Lauffenburger DA, Sorger PK Physicochemical modelling of cell signalling pathways. Nat Cell Biol 2006, 8(11):1195-1203.
154. Saez-Rodriguez J, Alexopoulos LG, Epperlein J, Samaga R, Lauffenburger DA, Klamt S, et al. Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Mol Syst Biol 2009, 5:331.
155. Cosgrove BD, King BM, Hasan MA, Alexopoulos LG, Farazi PA, Hendriks BS, et al. Synergistic drug-cytokine induction of hepatocellular death as an in vitro approach for the study of inflammation-associated idiosyncratic drug hepatotoxicity. Toxicol Appl Pharmacol 2009, 237(3):317-330.
156. Alexopoulos LG, Saez-Rodriguez J, Cosgrove BD, Lauffenburger DA, Sorger PK Networks inferred from biochemical data reveal profound differences in toll-like receptor and inflammatory signaling between normal and transformed hepatocytes. Mol Cell Proteomics 2010, 9(9):1849-1865.
157. Cosgrove BD, Alexopoulos LG, Hang TC, Hendriks BS, Sorger PK, Griffith LG, et al. Cytokine-associated drug toxicity in human hepatocytes is associated with signaling network dysregulation. Mol Biosyst 2010, 6(7):1195-1206.
158. Saez-Rodriguez J, Alexopoulos LG, Zhang M, Morris MK, Lauffenburger DA, Sorger PK Comparing signaling networks between normal and transformed hepatocytes using discrete logical models. Cancer Res 2011, 71(16):5400-5411.
159. Wiskwo JP, Prokop A, Baudenbacher F, Cliffel D, Csukas B, Velkovsky M Engineering challenges of BioNEMS: the integration of microfluids, micro- and nanodevices, models and external control for systems biology. IEE Proc Nanobiotechnol 2006, 153:81-101.
160. Willems JC The behavioral approach to open interconnected systems. IEEE Control Syst Mag 2007, 27:46-99.
161. Young PC The data-based mechanistic approach to the modelling, forecasting and control of environmental systems. Annu Rev Control 2006, 30:169-182.
162. Ideker T, Galitski T, Hood L A new approach to decoding life: systems biology. Annu Rev Genomics Hum Genet 2001, 2:343-372.
163. Mesarovic MD, Sreenath SN, Keene JD Search for organising principles: understanding in systems biology. Syst Biol(Stevenage) 2004, 1(1):19-27.
164. Janes KA, Yaffe MB Data-driven modelling of signal-transduction networks. Nat Rev Mol Cell Biol 2006, 7(11):820-828.
165. Kitano H Systems biology: a brief overview. Science 2002, 1662-1664.
166. Arkin AP, Schaffer DV Network news: innovations in 21st century systems biology. Cell 2011, 144(6):844-849.
167. Vodovotz Y, Clermont G, Chow C, An G Mathematical models of the acute inflammatory response. Curr Opin Crit Care 2004, 10:383-390.
168. Vodovotz Y, Clermont G, Hunt CA, Lefering R, Bartels J, Seydel R, et al. Evidence-based modeling of critical illness: an initial consensus from the Society for Complexity in Acute Illness. J Crit Care 2007, 22:77-84.
169. An G, Hunt CA, Clermont G, Neugebauer E, Vodovotz Y Challenges and rewards on the road to translational systems biology in acute illness: four case reports from interdisciplinary teams. J Crit Care 2007, 22:169-175.
170. Vodovotz Y, An G Systems biology and inflammation. Systems biology in drug discovery and development: methods and protocols 2009, 181-201. Springer Science and Business Media, Totowa, NJ. Q. Yan (Ed.).
171. Mi Q, Li NYK, Ziraldo C, Ghuma A, Mikheev M, Squires R, et al. Translational systems biology of inflammation: potential applications to personalized medicine. Per Med 2010, 7:549-559.
172. An G, Bartels J, Vodovotz Y In silico augmentation of the drug development pipeline: examples from the study of acute inflammation. Drug Dev Res 2011, 72:1-14.
173. Namas R, Zamora R, Namas R, An G, Doyle J, Dick TE, et al. Sepsis: something old, something new, and a systems view. J Crit Care 2012, 27:314e1-314e11.
174. An G, Nieman G, Vodovotz Y Computational and systems biology in trauma and sepsis: current state and future perspectives. Int J Burns Trauma 2012, 2:1-10.
175. An G, Namas R, Vodovotz Y Sepsis: from pattern to mechanism and back. Crit Rev Biomed Eng 2012, 40:341-351.
176. An G, Nieman G, Vodovotz Y Toward computational identification of multiscale tipping points in multiple organ failure. Ann Biomed Eng 2012, 40:2412-2424.
177. Vodovotz Y, Billiar TR In silico modeling: methods and applications to trauma and sepsis. Crit Care Med 2013, 41:2008-2014.
178. Azhar N, Mi Q, Ziraldo C, Buliga M, Constantine G, Vodovotz Y Integrating data driven and mechanistic models of the inflammatory response in sepsis and trauma 2013, Springer, New York, NY. Y. Vodovotz, G. An (Eds.).
179. Mac Nally R Regression and model-building in conservation biology, biogeography and ecology: the distinction between-and reconciliation of-"predictive" and "explanatory" models. Biodivers Conserv 2000, 9(5):655-671.
180. Mi Q, Constantine G, Ziraldo C, Solovyev A, Torres A, Namas R, et al. A dynamic view of trauma/hemorrhage-induced inflammation in mice: principal drivers and networks. PLoS One 2011, 6:e19424.
181. Zamora R, Azhar N, Namas R, Metukuri MR, Clermont T, Gladstone C, et al. Identification of a novel pathway of TGF-beta1 regulation by extracellular NAD+in mouse macrophages: in vitro and in silico studies. J Biol Chem 2012, 287:31003-31014.
182. Lagoa CE, Bartels J, Baratt A, Tseng G, Clermont G, Fink MP, et al. 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 2006, 26:592-600.
183. Ruffolo DC Delayed splenic rupture: understanding the threat. J Trauma Nurs 2002, 9(2):34-40.
184. Fouche Y, Sikorski R, Dutton RP Changing paradigms in surgical resuscitation. Crit Care Med 2010, 38(9 Suppl.):S411-S420.
185. Bramos A, Velmahos GC, Butt UM, Fikry K, Smith RM, Chang Y Predictors of bleeding from stable pelvic fractures. Arch Surg 2011, 146(4):407-411.
186. Chinnaiyan AM, Huber-Lang M, Kumar-Sinha C, Barrette TR, Shankar-Sinha S, Sarma VJ, et al. Molecular signatures of sepsis: multiorgan gene expression profiles of systemic inflammation. Am J Pathol 2001, 159(4):1199-1209.
187. Yu SL, Chen HW, Yang PC, Peck K, Tsai MH, Chen JJ, et al. Differential gene expression in gram-negative and gram-positive sepsis. Am J Respir Crit Care Med 2004, 169(10):1135-1143.
188. Brownstein BH, Logvinenko T, Lederer JA, Cobb JP, Hubbard WJ, Chaudry IH, et al. Commonality and differences in leukocyte gene expression patterns among three models of inflammation and injury. Physiol Genomics 2006, 24(3):298-309.
189. Edmonds RD, Vodovotz Y, Lagoa C, Dutta-Moscato J, Ching Y, Fink MP, et al. Transcriptomic response of murine liver to severe injury and hemorrhagic shock: a dual platform microarray analysis. Physiol Genomics 2011, 43:1170-1183.
190. Prucha M, Ruryk A, Boriss H, Moller E, Zazula R, Herold I, et al. Expression profiling: toward an application in sepsis diagnostics. Shock 2004, 22(1):29-33.
191. Pachot A, Lepape A, Vey S, Bienvenu J, Mougin B, Monneret G Systemic transcriptional analysis in survivor and non-survivor septic shock patients: a preliminary study. Immunol Lett 2006, 106(1):63-71.
192. Shanley TP, Cvijanovich N, Lin R, Allen GL, Thomas NJ, Doctor A, et al. Genome-level longitudinal expression of signaling pathways and gene networks in pediatric septic shock. Mol Med 2007, 13(9-10):495-508.
193. Calvano SE, Xiao W, Richards DR, Felciano RM, Baker HV, Cho RJ, et al. A network-based analysis of systemic inflammation in humans. Nature 2005, 437:1032-1037.
194. Xiao W, Mindrinos MN, Seok J, Cuschieri J, Cuenca AG, Gao H, et al. A genomic storm in critically injured humans. J Exp Med 2011, 208(13):2581-2590.
195. Azhar N, Ziraldo C, Barclay D, Rudnick D, Squires R, Vodovotz Y Analysis of serum inflammatory mediators identifies unique dynamic networks associated with death and spontaneous survival in pediatric acute liver failure. PLoS One 2013, 8:e78202.
196. Cohen MJ, Grossman AD, Morabito D, Knudson MM, Butte AJ, Manley GT Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis. Crit Care 2010, 14(1):R10.
197. Zhou B, Xu W, Herndon D, Tompkins R, Davis R, Xiao W, et al. Analysis of factorial time-course microarrays with application to a clinical study of burn injury. Proc Natl Acad Sci USA 2010, 107(22):9923-9928.
198. Namas R, Namas R, Lagoa C, Barclay D, Mi Q, Zamora R, et al. Hemoadsorption reprograms inflammation in experimental gram-negative septic fibrin peritonitis: insights from in vivo and in silico studies. Mol Med 2012, 18:1366-1374.
199. Nieman K, Brown D, Sarkar J, Kubiak B, Ziraldo C, Vieau C, et al. A two-compartment mathematical model of endotoxin-induced inflammatory and physiologic alterations in swine. Crit Care Med 2012, 40:1052-1063.
200. Samaga R, Klamt S Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks. Cell Commun Signal 2013, 11(1):43.
201. Kunkel SL, Strieter RM, Chensue SW, Basha M, Standiford T, Ham J, et al. Tumor necrosis factor-alpha, interleukin-8 and chemotactic cytokines. Prog Clin Biol Res 1990, 349:433-444.
202. Elias JA, Freundlich B, Kern JA, Rosenbloom J Cytokine networks in the regulation of inflammation and fibrosis in the lung. Chest 1990, 97(6):1439-1445.
203. Miossec P An update on the cytokine network in rheumatoid arthritis. Curr Opin Rheumatol 2004, 16(3):218-222.
204. Stavitsky AB The innate immune response to infection, toxins and trauma evolved into networks of interactive, defensive, reparative, regulatory, injurious and pathogenic pathways. Mol Immunol 2007, 44(11):2787-2799.
205. Vasto S, Candore G, Balistreri CR, Caruso M, Colonna-Romano G, Grimaldi MP, et al. Inflammatory networks in ageing, age-related diseases and longevity. Mech Ageing Dev 2007, 128(1):83-91.
206. Foteinou PT, Yang E, Androulakis IP Networks, biology and systems engineering: a case study in inflammation. Comput Chem Eng 2009, 33(12):2028-2041.
207. Alexopoulos LG, Saez-Rodriguez J, Cosgrove BD, Lauffenburger DA, Sorger PK Networks inferred from biochemical data reveal profound differences in toll-like receptor and inflammatory signaling between normal and transformed hepatocytes. Mol Cell Proteomics 2010, 9(9):1849-1865.
208. Somvanshi PR, Venkatesh KV A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics. Syst Synth Biol 2014, 8(1):99-116.
209. Husmeier D Reverse engineering of genetic networks with Bayesian networks. Biochem Soc Trans 2003, 31(Pt 6):1516-1518.
210. Jiang D, Tang C, Zhang A Cluster analysis for gene expression data: a survey. IEEE Trans Knowl Data Eng 2004, 16:1370-1386.
211. Shah A, Tenzen T, McMahon AP, Woolf PJ Using mechanistic Bayesian networks to identify downstream targets of the sonic hedgehog pathway. BMC Bioinformatics 2009, 10:433.
212. Rawool SB, Venkatesh KV Steady state approach to model gene regulatory networks-simulation of microarray experiments. Biosystems 2007, 90(3):636-655.
213. Mihara M, Hashizume M, Yoshida H, Suzuki M, Shiina M IL-6/IL-6 receptor system and its role in physiological and pathological conditions. Clin Sci (Lond) 2012, 122(4):143-159.
214. Kopf M, Ramsay A, Brombacher F, Baumann H, Freer G, Galanos C, et al. Pleiotropic defects of IL-6-deficient mice including early hematopoiesis, T and B cell function, and acute phase responses. Ann NY Acad Sci 1995, 762:308-318.
215. Opal SM, DePalo VA Anti-inflammatory cytokines. Chest 2000, 117(4):1162-1172.
216. Yudkin JS, Kumari M, Humphries SE, Mohamed-Ali V Inflammation, obesity, stress and coronary heart disease: is interleukin-6 the link?. Atherosclerosis 2000, 148(2):209-214.
217. Fausto N Liver regeneration. J Hepatol 2000, 32(1 Suppl.):19-31.
218. Naka T, Nishimoto N, Kishimoto T The paradigm of IL-6: from basic science to medicine. Arthritis Res 2002, 4(Suppl. 3):S233-S242.
219. Diehl S, Rincon M The two faces of IL-6 on Th1/Th2 differentiation. Mol Immunol 2002, 39(9):531-536.
220. Menger MD, Vollmar B Surgical trauma: hyperinflammation versus immunosuppression?. Langenbecks Arch Surg 2004, 389(6):475-484.
221. Kritchevsky SB, Cesari M, Pahor M Inflammatory markers and cardiovascular health in older adults. Cardiovasc Res 2005, 66(2):265-275.
222. Mastorakos G, Ilias I Interleukin-6: a cytokine and/or a major modulator of the response to somatic stress. Ann NY Acad Sci 2006, 1088:373-381.
223. Scheller J, Rose-John S Interleukin-6 and its receptor: from bench to bedside. Med Microbiol Immunol 2006, 195(4):173-183.
224. Knupfer H, Preiss R Serum interleukin-6 levels in colorectal cancer patients-a summary of published results. Int J Colorectal Dis 2010, 25(2):135-140.
225. Jawa RS, Anillo S, Huntoon K, Baumann H, Kulaylat M Interleukin-6 in surgery, trauma, and critical care part II: clinical implications. J Intensive Care Med 2011, 26(2):73-87.
226. Ziraldo C, Vodovotz Y, Namas RA, Almahmoud K, Tapias V, Mi Q, et al. Central role for MCP-1/CCL2 in injury-induced inflammation revealed by in vitro, in silico, and clinical studies. PLoS One 2013, 8(12):e79804.
227. Peitzman AB, Billiar TR, Harbrecht BG, Kelly E, Udekwu AO, Simmons RL Hemorrhagic shock. Curr Probl Surg 1995, 32(11):925-1002.
228. McCloskey CA, Kameneva MV, Uryash A, Gallo DJ, Billiar TR Tissue hypoxia activates JNK in the liver during hemorrhagic shock. Shock 2004, 22(4):380-386.
229. Husmeier D Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 2003, 19(17):2271-2282.
230. Park Y, Moore C, Bader JS Dynamic networks from hierarchical Bayesian graph clustering. PLoS One 2010, 5(1):e8118.
231. Grzegorczyk M, Husmeier D Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes. Bioinformatics 2011, 27(5):693-699.
232. Wang SQ, Li HX Bayesian inference based modelling for gene transcriptional dynamics by integrating multiple source of knowledge. BMC Syst Biol 2012, 6(Suppl. 1):S3.
233. Godsey B Improved inference of gene regulatory networks through integrated Bayesian clustering and dynamic modeling of time-course expression data. PLoS One 2013, 8(7):e68358.
234. Zaaqoq AM, Namas R, Almahmoud K, Krishnan S, Azhar N, Mi Q, et al. IP-10, a potential driver of neurally controlled IL-10 and morbidity in human blunt trauma. Crit Care Med 2014, 42:1487-1497.
235. Narkewicz MR, Dell OD, Karpen SJ, Murray KF, Schwarz K, Yazigi N, et al. Pattern of diagnostic evaluation for the causes of pediatric acute liver failure: an opportunity for quality improvement. J Pediatr 2009, 155(6):801-806.
236. Sundaram SS, Alonso EM, Narkewicz MR, Zhang S, Squires RH Characterization and outcomes of young infants with acute liver failure. J Pediatr 2011, 159(5):813-818.
237. Dehmer M, Mueller LAJ, Emmert-Streib F Quantitative network Measures as biomarkers for classifying prostate cancer disease states: a systems approach to diagnostic biomarkers. PLoS One 2013, 8(11):e77602.
238. Tracey KJ The inflammatory reflex. Nature 2002, 420(6917):853-859.
239. Dick TE, Molkov Y, Nieman G, Hsieh Y, Jacono FJ, Doyle J, et al. Linking inflammation and cardiorespiratory variability in sepsis via computational modeling. Front Physiol 2012, 3:222.
240. Vodovotz Y, Csete M, Bartels J, Chang S, An G Translational systems biology of inflammation. PLoS Comput Biol 2008, 4:1-6.
241. Emr B, Sadowsky D, Azhar N, Gatto L, An G, Nieman G, et al. Removal of inflammatory ascites is associated with dynamic modification of local and systemic inflammation along with prevention of acute lung injury: in vivo and in silico studies. Shock 2014, 41:317-323.
242. Vodovotz Y, An G Systems biology and inflammation. Systems biology in drug discovery and development: methods and protocols 2009, 181-201. Springer Science and Business Media, Totowa, NJ. Q. Yan (Ed.).
243. Vodovotz Y, Billiar TR In silico modeling: methods and applications to trauma and sepsis. Crit Care Med 2013, 41:2008-2014.
244. An G, Faeder J, Vodovotz Y Translational systems biology: introduction of an engineering approach to the pathophysiology of the burn patient. J Burn Care Res 2008, 29:277-285.
245. Vodovotz Y, Csete M, Bartels J, Chang S, An G Translational systems biology of inflammation. PLoS Comput Biol 2008, 4:1-6.
246. An G In silico experiments of existing and hypothetical cytokine-directed clinical trials using agent based modeling. Crit Care Med 2004, 32:2050-2060.
247. Kumar R, Clermont G, Vodovotz Y, Chow CC The dynamics of acute inflammation. J Theor Biol 2004, 230:145-155.
248. Clermont G, Bartels J, Kumar R, Constantine G, Vodovotz Y, Chow C In silico design of clinical trials: a method coming of age. Crit Care Med 2004, 32:2061-2070.
249. Reynolds A, Rubin J, Clermont G, Day J, Vodovotz Y, Ermentrout GB A reduced mathematical model of the acute inflammatory response: I. Derivation of model and analysis of anti-inflammation. J Theor Biol 2006, 242:220-236.
250. Kumar R, Chow CC, Bartels J, Clermont G, Vodovotz Y A mathematical simulation of the inflammatory response to anthrax infection. Shock 2008, 29:104-111.
251. Chow CC, Clermont G, Kumar R, Lagoa C, Tawadrous Z, Gallo D, et al. The acute inflammatory response in diverse shock states. Shock 2005, 24:74-84.
252. Day J, Rubin J, Vodovotz Y, Chow CC, Reynolds A, Clermont G A reduced mathematical model of the acute inflammatory response: II. Capturing scenarios of repeated endotoxin administration. J Theor Biol 2006, 242:237-256.
253. Prince JM, Levy RM, Bartels J, Baratt A, Kane JM, Lagoa C, et al. In silico and in vivo approach to elucidate the inflammatory complexity of CD14-deficient mice. Mol Med 2006, 12:88-96.
254. Daun S, Rubin J, Vodovotz Y, Roy A, Parker R, Clermont G An ensemble of models of the acute inflammatory response to bacterial lipopolysaccharide in rats: results from parameter space reduction. J Theor Biol 2008, 253:843-853.
255. Foteinou PT, Calvano SE, Lowry SF, Androulakis IP Modeling endotoxin-induced systemic inflammation using an indirect response approach. Math Biosci 2009, 217:27-42.
256. Foteinou PT, Calvano SE, Lowry SF, Androulakis IP In silico simulation of corticosteroids effect on an NFkB-dependent physicochemical model of systemic inflammation. PLoS One 2009, 4(3):e4706.
257. An G, Faeder JR Detailed qualitative dynamic knowledge representation using a BioNetGen model of TLR-4 signaling and preconditioning. Math Biosci 2009, 217:53-63.
258. 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 2009, 217:43-52.
259. Rivière B, Epshteyn Y, Swigon D, 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 2009, 217:19-26.
260. Nieman K, Brown D, Sarkar J, Kubiak B, Ziraldo C, Vieau C, et al. A two-compartment mathematical model of endotoxin-induced inflammatory and physiologic alterations in swine. Crit Care Med 2012, 40:1052-1063.
261. Dong X, Foteinou PT, Calvano SE, Lowry SF, Androulakis IP Agent-based modeling of endotoxin-induced acute inflammatory response in human blood leukocytes. PLoS One 2010, 5(2):e9249.
262. Foteinou PT, Calvano SE, Lowry SF, Androulakis IP Multiscale model for the assessment of autonomic dysfunction in human endotoxemia. Physiol Genomics 2010, 42(1):5-19.
263. Scheff JD, Calvano SE, Lowry SF, Androulakis IP Modeling the influence of circadian rhythms on the acute inflammatory response. J Theor Biol 2010, 264(3):1068-1076.
264. Foteinou PT, Calvano SE, Lowry SF, Androulakis IP A physiological model for autonomic heart rate regulation in human endotoxemia. Shock 2011, 35:229-239.
265. Yang Q, Calvano SE, Lowry SF, Androulakis IP A dual negative regulation model of Toll-like receptor 4 signaling for endotoxin preconditioning in human endotoxemia. Math Biosci 2011, 232(2):151-163.
266. Lagoa CE, Bartels J, Baratt A, Tseng G, Clermont G, Fink MP, et al. 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 2006, 26:592-600.
267. Torres A, Bentley T, Bartels J, Sarkar J, Barclay D, Namas R, et al. Mathematical modeling of post-hemorrhage inflammation in mice: studies using a novel, computer-controlled, closed-loop hemorrhage apparatus. Shock 2009, 32:172-178.
268. Arciero J, Rubin J, Upperman J, Vodovotz Y, Ermentrout GB Using a mathematical model to analyze the role of probiotics and inflammation in necrotizing enterocolitis. PLoS One 2010, 5:e10066.
269. Kim M, Christley S, Alverdy JC, Liu D, An G Immature oxidative stress management as a unifying principle in the pathogenesis of necrotizing enterocolitis: insights from an agent-based model. Surg Infect (Larchmt) 2012, 13(1):18-32.
270. Arciero J, Ermentrout GB, Siggers R, Afrazi A, Hackam D, Vodovotz Y, et al. Modeling the interactions of bacteria and Toll-like receptor-mediated inflammation in necrotizing enterocolitis. J Theor Biol 2013, 321:83-99.
271. Barber J, Tronzo M, Harold HC, Clermont G, Upperman J, Vodovotz Y, et al. A three-dimensional mathematical and computational model of necrotizing enterocolitis. J Theor Biol 2013, 322:17-32.
272. Edelstein-Keshet L Mathematical models in biology 1988, Random House, New York, NY.
273. Clermont G Translational equation-based modeling. Complex systems and computational approaches to acute inflammation 2013, Springer, New York, NY. Y. Vodovotz, G. An (Eds.).
274. Namas R, Ghuma A, Hermus L, Zamora R, Okonkwo DO, Billiar TR, et al. The acute inflammatory response in trauma/hemorrhage and traumatic brain injury: current state and emerging prospects. Libyan J Med 2009, 4:97-103.
275. Price I, Ermentrout B, Zamora R, Wang B, Azhar N, Mi Q, et al. In vivo, in vitro, and in silico studies suggest a conserved immune module that may regulate malaria parasite transmission from mammals to mosquitoes. J Theor Biol 2013, 334:173-186.
276. Zamora R, Azhar N, Namas R, Metukuri MR, Clermont T, Gladstone C, et al. Identification of a novel pathway of TGF-beta1 regulation by extracellular NAD+in mouse macrophages: in vitro and in silico studies. J Biol Chem 2012, 287:31003-31014.
277. Kitano H Systems biology: a brief overview. Science 2002, 295(5560):1662-1664.
278. Angeli D, Ferrell J, Sontag ED Detection of multistability, bifurcations, and hysteresis in a large class of biological positive-feedback systems. Proc Natl Acad Sci USA 2004, 101:1822-1827.
279. Bagci EZ, Vodovotz Y, Billiar TR, Ermentrout GB, Bahar I Bistability in apoptosis: roles of Bax, Bcl-2 and mitochondrial permeability transition pores. Biophys J 2006, 90:1546-1559.
280. Marino S, Hogue IB, Ray CJ, Kirschner DE A methodology for performing global uncertainty and sensitivity analysis in systems biology. J Theor Biol 2008, 254(1):178-196.
281. Levi MI, Smirnova OA, Stepanova NV [Mathematical models of the primary immunological reaction (a review)]. Zh Mikrobiol Epidemiol Immunobiol 1974, 11:113-120.
282. Alt W, Lauffenburger DA Transient behavior of a chemotaxis system modelling certain types of tissue inflammation. J Math Biol 1987, 24(6):691-722.
283. Nelson GW, Perelson AS A mechanism of immune escape by slow-replicating HIV strains. J Acquir Immune Defic Syndr 1992, 5(1):82-93.
284. An G, Hunt CA, Clermont G, Neugebauer E, Vodovotz Y Challenges and rewards on the road to translational systems biology in acute illness: four case reports from interdisciplinary teams. J Crit Care 2007, 22:169-175.
285. Natanson C, Hoffman WD, Suffredini AF, Eichacker PQ, Danner RL Selected treatment strategies for septic shock based on proposed mechanisms of pathogenesis. Ann Intern Med 1994, 120:771-783.
286. Angus DC The search for effective therapy for sepsis: back to the drawing board?. JAMA 2011, 306(23):2614-2615.
287. Nathan C Points of control in inflammation. Nature 2002, 420(6917):846-852.
288. Freeman BD, Natanson C Anti-inflammatory therapies in sepsis and septic shock. Expert Opin Investig Drugs 2000, 9(7):1651-1663.
289. Opal SM, DePalo VA Anti-inflammatory cytokines. Chest 2000, 117(4):1162-1172.
290. Vodovotz Y, Constantine G, Rubin J, Csete M, Voit EO, An G Mechanistic simulations of inflammation: current state and future prospects. Math Biosci 2009, 217:1-10.
291. Vodovotz Y At the interface between acute and chronic inflammation: insights from computational modeling. Chronic inflammation: nutritional and therapeutic interventions 2013, Taylor & Francis, Florence, KY. S. Roy, C. Sen (Eds.).
292. Namas RA, Bartels J, Hoffman R, Barclay D, Billiar TR, Zamora R, et al. Combined in silico, in vivo, and in vitro studies shed insights into the acute inflammatory response in middle-aged mice. PLoS One 2013, 8:e67419.
293. Mi Q, Rivière B, Clermont G, Steed DL, Vodovotz Y Agent-based model of inflammation and wound healing: insights into diabetic foot ulcer pathology and the role of transforming growth factorβ1. Wound Repair Regen 2007, 15:617-682.
294. Li NYK, Verdolini K, Clermont G, Mi Q, Hebda PA, Vodovotz Y A patient-specific in silico model of inflammation and healing tested in acute vocal fold injury. PLoS One 2008, 3:e2789.
295. An G, Mi Q, Dutta-Moscato J, Solovyev A, Vodovotz Y Agent-based models in translational systems biology. WIRES 2009, 1:159-171.
296. Li NYK, Vodovotz Y, Hebda PA, Verdolini K Biosimulation of inflammation and healing in surgically injured vocal folds. Ann Otol Rhinol Laryngol 2010, 119:412-423.
297. Li NYK, Vodovotz Y, Kim KH, Mi Q, Hebda PA, Verdolini Abbott K Biosimulation of acute phonotrauma: an extended model. Laryngoscope 2011, 121:2418-2428.
298. Brown BN, Price IM, Toapanta FR, Dealmeida DR, Wiley CA, Ross TM, et al. An agent-based model of inflammation and fibrosis following particulate exposure in the lung. Math Biosci 2011, 231:186-196.
299. Solovyev A, Mi Q, Tzen Y-T, Brienza D, Vodovotz Y Hybrid equation-/agent-based model of ischemia-induced hyperemia and pressure ulcer formation predicts greater propensity to ulcerate in subjects with spinal cord injury. PLoS Comput Biol 2013, 9:e1003070.
300. Dutta-Moscato J, Solovyev A, Mi Q, Nishikawa T, Soto-Gutierrez A, Fox IJ, et al. A multiscale agent-based in silico model of liver fibrosis progression. Front Bioeng Biotechnol 2014, 2:1-10. (Article 18).
301. Kox WJ, Volk T, Kox SN, Volk HD Immunomodulatory therapies in sepsis. Intensive Care Med 2000, 26(Suppl. 1):S124-S128.
302. Marshall JC Clinical trials of mediator-directed therapy in sepsis: what have we learned?. Intensive Care Med 2000, 26(Suppl. 1):S75-S83.
303. Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med 2001, 29(7):1303-1310.
304. Reinhart K, Karzai W Anti-tumor necrosis factor therapy in sepsis: update on clinical trials and lessons learned. Crit Care Med 2001, 29(7 Suppl.):S121-S125.
305. Wenner KA, Kenner JR Anthrax. Dermatol Clin 2004, 22(3):247-256. v.
306. Turnbull PC, Leppla SH, Broster MG, Quinn CP, Melling J Antibodies to anthrax toxin in humans and guinea pigs and their relevance to protective immunity. Med Microbiol Immunol (Berl) 1988, 177(5):293-303.
307. Iacono-Connors LC, Welkos SL, Ivins BE, Dalrymple JM Protection against anthrax with recombinant virus-expressed protective antigen in experimental animals. Infect Immun 1991, 59(6):1961-1965.
308. Ivins BE, Welkos SL, Little SF, Crumrine MH, Nelson GO Immunization against anthrax with Bacillus anthracis protective antigen combined with adjuvants. Infect Immun 1992, 60(2):662-668.
309. Barnard JP, Friedlander AM Vaccination against anthrax with attenuated recombinant strains of Bacillus anthracis that produce protective antigen. Infect Immun 1999, 67(2):562-567.
310. Brossier F, Weber-Levy M, Mock M, Sirard JC Protective antigen-mediated antibody response against a heterologous protein produced in vivo by Bacillus anthracis. Infect Immun 2000, 68(10):5731-5734.
311. Friedlander AM, Welkos SL, Ivins BE Anthrax vaccines. Curr Top Microbiol Immunol 2002, 271:33-60.
312. Csete ME, Doyle JC Reverse engineering of biological complexity. Science 2002, 295(5560):1664-1669.
313. Kurata H, El-Samad H, Iwasaki R, Ohtake H, Doyle JC, Grigorova I, et al. Module-based analysis of robustness tradeoffs in the heat shock response system. PLoS Comput Biol 2006, 2(7):e59.
314. An G, Nieman G, Vodovotz Y Computational and systems biology in trauma and sepsis: current state and future perspectives. Int J Burns Trauma 2012, 2:1-10.
315. Cavaillon JM The nonspecific nature of endotoxin tolerance. Trends Microbiol 1995, 3(8):320-324.
316. Cavaillon JM, Adrie C, Fitting C, Adib-Conquy M Endotoxin tolerance: is there a clinical relevance?. J Endotoxin Res 2003, 9(2):101-107.
317. Eng RH, Smith SM, Fan-Havard P, Ogbara T Effect of antibiotics on endotoxin release from gram-negative bacteria. Diagn Microbiol Infect Dis 1993, 16(3):185-189.
318. Dani C, Biadaioli R, Bertini G, Martelli E, Rubaltelli FF Probiotics feeding in prevention of urinary tract infection, bacterial sepsis and necrotizing enterocolitis in preterm infants. A prospective double-blind study. Biol Neonate 2002, 82(2):103-108.
319. Land MH, Rouster-Stevens K, Woods CR, Cannon ML, Cnota J, Shetty AK Lactobacillus sepsis associated with probiotic therapy. Pediatrics 2005, 115(1):178-181.
320. Marshall JC, Deitch E, Moldawer LL, Opal S, Redl H, Poll TV Preclinical models of shock and sepsis: what can they tell us?. Shock 2005, 24(Suppl. 1):1-6.
321. Mathew S, Bartels J, Banerjee I, Vodovotz Y Global sensitivity analysis of a mathematical model of acute inflammation identifies nonlinear dependence of cumulative tissue damage on host interleukin-6 responses. J Theor Biol 2014, 358:132-148.
322. Namas R, Namas R, Lagoa C, Barclay D, Mi Q, Zamora R, et al. Hemoadsorption reprograms inflammation in experimental gram-negative septic fibrin peritonitis: insights from in vivo and in silico studies. Mol Med 2012, 18:1366-1374.
323. Peitzman AB, Billiar TR, Harbrecht BG, Kelly E, Udekwu AO, Simmons RL Hemorrhagic shock. Curr Probl Surg 1995, 32(11):925-1002.
324. Chaudry IH, Ayala A, Ertel W, Stephan RN Hemorrhage and resuscitation: immunological aspects. Am J Physiol 1990, 259(4 Pt 2):R663-R678.
325. Chaudry IH, Ayala A Mechanism of increased susceptibility to infection following hemorrhage. Am J Surg 1993, 165(2A Suppl.):59S-67S.
326. Galmarini S, Bianconi R, Klug W, Mikkelsen T, Addis R, Andronopoulos S, et al. Can the confidence in long range atmospheric transport models be increased? The pan-European experience of ensemble. Radiat Prot Dosimetry 2004, 109(1-2):19-24.
327. Palmer TN, Raisanen J Quantifying the risk of extreme seasonal precipitation events in a changing climate. Nature 2002, 415(6871):512-514.
328. Diffenbaugh NS, Field CB Changes in ecologically critical terrestrial climate conditions. Science 2013, 341(6145):486-492.
329. Schewe J, Heinke J, Gerten D, Haddeland I, Arnell NW, Clark DB, et al. Multimodel assessment of water scarcity under climate change. Proc Natl Acad Sci USA 2014, 111(9):3245-3250.
330. Prudhomme C, Giuntoli I, Robinson EL, Clark DB, Arnell NW, Dankers R, et al. Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment. Proc Natl Acad Sci USA 2014, 111(9):3262-3267.
331. Gneiting T, Raftery AE Atmospheric science: weather forecasting with ensemble methods. Science 2005, 310(5746):248-249.
332. Thyagarajan T, Totey S, Danton MJ, Kulkarni AB Genetically altered mouse models: the good, the bad, and the ugly. Crit Rev Oral Biol Med 2003, 14(3):154-174.
333. Osuchowski MF, Remick DG, Lederer JA, Lang CH, Aasen AO, Aibiki M, et al. Abandon the mouse research ship? Not just yet!. Shock 2014, 41(6):463-475.
334. An G, Bartels J, Vodovotz Y In silico augmentation of the drug development pipeline: examples from the study of acute inflammation. Drug Dev Res 2011, 72:1-14.
335. Ferrucci L, Ble A, Bandinelli S, Lauretani F, Suthers K, Guralnik JM A flame burning within. Aging Clin Exp Res 2004, 16(3):240-243.
336. Plackett TP, Boehmer ED, Faunce DE, Kovacs EJ Aging and innate immune cells. J Leukoc Biol 2004, 76(2):291-299.
337. Franceschi C, Bonafe M, Valensin S, Olivieri F, De LM, Ottaviani E, et al. Inflamm-aging. An evolutionary perspective on immunosenescence. Ann NY Acad Sci 2000, 908:244-254.
338. Namas R, Ghuma A, Torres A, Polanco P, Gomez H, Barclay D, et al. An adequately robust early TNFα response is a hallmark of survival following trauma/hemorrhage. PLoS One 2009, 4(12):e8406.
339. Scheff JD, Mavroudis PD, Foteinou PT, An G, Calvano SE, Doyle J, et al. A multiscale modeling approach to inflammation: a case study in human endotoxemia. Shock 2013, 244:279-289.
340. 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 2009, 217(1):43-52.
341. Bankes SC Agent-based modeling: a revolution?. Proc Natl Acad Sci USA 2002, 99(Suppl. 3):7199-7200.
342. Bonabeau E Agent-based modeling: methods and techniques for simulating human systems. Proc Natl Acad Sci USA 2002, 99(Suppl. 3):7280-7287.
343. Hunt CA, Ropella GE, Lam TN, Tang J, Kim SH, Engelberg JA, et al. At the biological modeling and simulation frontier. Pharm Res 2009, 26(11):2369-2400.
344. Walker DC, Southgate J The virtual cell-a candidate co-ordinator for 'middle-out' modeling of biological systems. Brief Bioinform 2009, 10(4):450-461.
345. Zhang L, Athale CA, Deisboeck TS Development of a three-dimensional multiscale agent-based tumor model: simulating gene-protein interaction profiles, cell phenotypes and multicellular patterns in brain cancer. J Theor Biol 2007, 244(1):96-107.
346. Santoni D, Pedicini M, Castiglione F Implementation of a regulatory gene network to simulate the TH1/2 differentiation in an agent-based model of hypersensitivity reactions. Bioinformatics 2008, 24(11):1374-1380.
347. Fallahi-Sichani M, El-Kebir M, Marino S, Kirschner DE, Linderman JJ Multiscale computational modeling reveals a critical role for TNF-alpha receptor 1 dynamics in tuberculosis granuloma formation. J Immunol 2011, 186(6):3472-3483.
348. Zaborin A, Romanowski K, Gerdes S, Holbrook C, Lepine F, Long J, et al. Red death in Caenorhabditis elegans caused by Pseudomonas aeruginosa PAO1. Proc Natl Acad Sci USA 2009, 106(15):6327-6332.
349. Hunt CA, Ropella GE, Yan L, Hung DY, Roberts MS Physiologically based synthetic models of hepatic disposition. J Pharmacokinet Pharmacodyn 2006, 33(6):737-772.
350. An G Introduction of an agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation. Theor Biol Med Model 2008, 5(1):11.
351. An G, Wilensky U From artificial life to in silico medicine: NetLogo as a means of translational knowledge representation in biomedical research. Artificial life in software 2009, 183-209. Springer-Verlag, London. 2nd ed. A. Adamatsky, M. Komosinski (Eds.).
352. An G Closing the scientific loop: bridging correlation and causality in the petaflop age. Sci Transl Med 2010, 2(41):41ps34.
353. Christley S, An G, editors. A proposed method for dynamic knowledge representation via agent-directed composition from biomedical and simulation ontologies: an example using gut mucus layer dynamics. Spring simulation multiconference/agent-directed simulation symposium. Boston, MA; 2011.
354. Graner F, Glazier JA Simulation of biological cell sorting using a two-dimensional extended Potts model. Phys Rev Lett 1992, 69(13):2013-2016.
355. Engelberg JA, Ropella GE, Hunt CA Essential operating principles for tumor spheroid growth. BMC Syst Biol 2008, 2(1):110.
356. Wendelsdorf KV, Alam M, Bassaganya-Riera J, Bisset K, Eubank S, Hontecillas R, et al. Enteric Immunity Simulator: a tool for in silico study of gastroenteric infections. IEEE Trans Nanobioscience 2012, 11(3):273-288.
357. Reynolds CW, editor. Flocks, herds, and schools: a distributed behavioral model in computer graphics. SIGGRAPH'87; 1987.
358. Lipniacki T, Paszek P, Brasier AR, Luxon BA, Kimmel M Stochastic regulation in early immune response. Biophys J 2006, 90(3):725-742.
359. Lipniacki T, Paszek P, Marciniak-Czochra A, Brasier AR, Kimmel M Transcriptional stochasticity in gene expression. J Theor Biol 2006, 238(2):348-367.
360. Vodovotz Y, Clermont G, Hunt CA, Lefering R, Bartels J, Seydel R, et al. Evidence-based modeling of critical illness: an initial consensus from the Society for Complexity in Acute Illness. J Crit Care 2007, 22(1):77-84.
361. Grimm V, Revilla E, Berger U, Jeltsch F, Mooij W, Railsback S, et al. Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 2005, 310:987-991.
362. An G Agent-based computer simulation and sirs: building a bridge between basic science and clinical trials. Shock 2001, 16(4):266-273.
363. An G In silico experiments of existing and hypothetical cytokine-directed clinical trials using agent-based modeling. Crit Care Med 2004, 32(10):2050-2060.
364. Mansury Y, Diggory M, Deisboeck TS Evolutionary game theory in an agent-based brain tumor model: exploring the 'Genotype-Phenotype' link. J Theor Biol 2006, 238(1):146-156.
365. Deisboeck TS, Berens ME, Kansal AR, Torquato S, Stemmer-Rachamimov AO, Chiocca EA Pattern of self-organization in tumour systems: complex growth dynamics in a novel brain tumour spheroid model. Cell Prolif 2001, 34(2):115-134.
366. Chen S, Ganguli S, Hunt CA An agent-based computational approach for representing aspects of in vitro multi-cellular tumor spheroid growth. Conf Proc IEEE Eng Med Biol Soc 2004, 1:691-694.
367. Thorne BC, Bailey AM, Benedict K, Peirce-Cottler S Modeling blood vessel growth and leukocyte extravasation in ischemic injury: an integrated agent-based and finite element analysis approach. J Crit Care 2006, 21(4):346.
368. Tang J, Ley KF, Hunt CA Dynamics of in silico leukocyte rolling, activation, and adhesion. BMC Syst Biol 2007, 1:14.
369. Tang J, Hunt CA, Mellein J, Ley K Simulating leukocyte-venule interactions-a novel agent-oriented approach. Conf Proc IEEE Eng Med Biol Soc 2004, 7:4978-4981.
370. Bailey AM, Thorne BC, Peirce SM Multi-cell agent-based simulation of the microvasculature to study the dynamics of circulating inflammatory cell trafficking. Ann Biomed Eng 2007, 35(6):916-936.
371. Bailey AM, Lawrence MB, Shang H, Katz AJ, Peirce SM Agent-based model of therapeutic adipose-derived stromal cell trafficking during ischemia predicts ability to roll on P-selectin. PLoS Comput Biol 2009, 5(2):e1000294.
372. Mi Q, Riviere B, Clermont G, Steed DL, Vodovotz Y Agent-based model of inflammation and wound healing: insights into diabetic foot ulcer pathology and the role of transforming growth factor-beta1. Wound Repair Regen 2007, 15(5):671-682.
373. Walker DC, Hill G, Wood SM, Smallwood RH, Southgate J Agent-based computational modeling of wounded epithelial cell monolayers. IEEE Trans Nanobioscience 2004, 3(3):153-163.
374. Adra S, Sun T, MacNeil S, Holcombe M, Smallwood R Development of a three dimensional multiscale computational model of the human epidermis. PLoS One 2010, 5(1):e8511.
375. Broderick G, Ru'aini M, Chan E, Ellison MJ A life-like virtual cell membrane using discrete automata. In Silico Biol 2005, 5(2):163-178.
376. Pogson M, Holcombe M, Smallwood R, Qwarnstrom E Introducing spatial information into predictive NF-kappaB modelling-an agent-based approach. PLoS One 2008, 3(6):e2367.
377. Pogson M, Smallwood R, Qwarnstrom E, Holcombe M Formal agent-based modelling of intracellular chemical interactions. Biosystems 2006, 85(1):37-45.
378. Ridgway D, Broderick G, Lopez-Campistrous A, Ru'aini M, Winter P, Hamilton M, et al. Coarse-grained molecular simulation of diffusion and reaction kinetics in a crowded virtual cytoplasm. Biophys J 2008, 94(10):3748-3759.
379. Troisi A, Wong V, Ratner MA An agent-based approach for modeling molecular self-organization. Proc Natl Acad Sci USA 2005, 102(2):255-260.
380. Dong X, Foteinou PT, Calvano SE, Lowry SF, Androulakis IP Agent-based modeling of endotoxin-induced acute inflammatory response in human blood leukocytes. PLoS One 2010, 5(2):e9249.
381. Hoehme S, Drasdo D A cell-based simulation software for multi-cellular systems. Bioinformatics 2010, 26(20):2641-2642.
382. Christley S, Alber MS, Newman SA Patterns of mesenchymal condensation in a multiscale, discrete stochastic model. PLoS Comput Biol 2007, 3(4):e76.
383. Vodovotz Y, Csete M, Bartels J, Chang S, An G Translational systems biology of inflammation. PLoS Comput Biol 2008, 4(4):e1000014.
384. An G, Mi Q, Dutta-Moscato J, Solovyev A, Vodovotz Y Agent-based models in translational systems biology. WIRES 2009, 1:159-171.
385. Clermont G, Bartels J, Kumar R, Constantine G, Vodovotz Y, Chow C In silico design of clinical trials: a method coming of age. Crit Care Med 2004, 32(10):2061-2070.
386. Kumar R, Clermont G, Vodovotz Y, Chow CC The dynamics of acute inflammation. J Theor Biol 2004, 230:145-155.
387. Namas R, Ghuma A, Torres A, Polanco P, Gomez H, Barclay D, et al. An adequately robust early TNF-a response is a hallmark of survival following trauma/hemorrhage. PLoS One 2009, 4(12):e8406.
388. Deitch EA Gut lymph and lymphatics: a source of factors leading to organ injury and dysfunction. Ann NY Acad Sci 2010, 1207(Suppl. 1):E103-E111.
389. Eichacker PQ, Parent C, Kalil A, Esposito C, Cui X, Banks SM, et al. Risk and the efficacy of antiinflammatory agents: retrospective and confirmatory studies of sepsis. Am J Respir Crit Care Med 2002, 166(9):1197-1205.
390. Kim M, Christley S, Alverdy JC, Liu D, An G Immature oxidative stress management as a unifying principle in the pathogenesis of necrotizing enterocolitis: insights from an agent-based model. Surg Infect (Larchmt) 2012, 13(1):18-32.
391. Arciero J, Rubin J, Upperman J, Vodovotz Y, Ermentrout GB Using a mathematical model to analyze the role of probiotics and inflammation in necrotizing enterocolitis. PLoS One 2010, 5:e10066.
392. Upperman JS, Lugo B, Camerini V, Yotov I, Rubin J, Clermont G, et al. Mathematical modeling in NEC-a new look at an ongoing problem. J Pediatr Surg 2007, 42:445-453.
393. Gopalakrishnan V, Kim M, An G Using an agent-based model to examine the pathogenesis of surgical site infection. Adv Wound Care 2013, 2(9):510-526.
394. Barie PS Surgical site infections: epidemiology and prevention. Surg Infect (Larchmt) 2002, 3(Suppl. 1):S9-S21.
395. Cruse P Wound infection surveillance. Rev Infect Dis 1981, 3(4):734-737.
396. Seal JB, Morowitz M, Zaborina O, An G, Alverdy JC The molecular Koch's postulates and surgical infection: a view forward. Surgery 2010, 147(6):757-765.
397. Trinchieri G Cancer and inflammation: an old intuition with rapidly evolving new concepts. Annu Rev Immunol 2012, 30:677-706.
398. Elinav E, Nowarski R, Thaiss CA, Hu B, Jin C, Flavell RA Inflammation-induced cancer: crosstalk between tumours, immune cells and microorganisms. Nat Rev Cancer 2013, 13(11):759-771.
399. Grivennikov SI, Greten FR, Karin M Immunity, inflammation, and cancer. Cell 2010, 140(6):883-899.
400. Grivennikov SI, Karin M Inflammation and oncogenesis: a vicious connection. Curr Opin Genet Dev 2010, 20(1):65-71.
401. Hanahan D, Weinberg RA Hallmarks of cancer: the next generation. Cell 2011, 144(5):646-674.
402. Algra AM, Rothwell PM Effects of regular aspirin on long-term cancer incidence and metastasis: a systematic comparison of evidence from observational studies versus randomised trials. Lancet Oncol 2012, 13(5):518-527.
403. Rothwell PM, Price JF, Fowkes FG, Zanchetti A, Roncaglioni MC, Tognoni G, et al. Short-term effects of daily aspirin on cancer incidence, mortality, and non-vascular death: analysis of the time course of risks and benefits in 51 randomised controlled trials. Lancet 2012, 379(9826):1602-1612.
404. Colotta F, Allavena P, Sica A, Garlanda C, Mantovani A Cancer-related inflammation, the seventh hallmark of cancer: links to genetic instability. Carcinogenesis 2009, 30(7):1073-1081.
405. Zitvogel L, Apetoh L, Ghiringhelli F, Andre F, Tesniere A, Kroemer G The anticancer immune response: indispensable for therapeutic success?. J Clin Invest 2008, 118(6):1991-2001.
406. Nathan C Points of control in inflammation. Nature 2002, 420(6917):846-852.
407. Nathan C, Ding A Nonresolving inflammation. Cell 2010, 140(6):871-882.
408. An G, Kulkarni S An agent-based modeling framework linking inflammation and cancer using evolutionary principles: description of a generative hierarchy for the hallmarks of cancer and developing a bridge between mechanism and epidemiological data. Math Biosci 2014, pii: S0025-5564(14)00142-4. doi: 10.1016/j.mbs.2014.07.009. [Epub ahead of print.].
409. Hanahan D, Weinberg RA The hallmarks of cancer. Cell 2000, 100(1):57-70.
410. Chapa J, Bourgo RJ, Greene GL, Kulkarni S, An G Examining the pathogenesis of breast cancer using a novel agent-based model of mammary ductal epithelium dynamics. PLoS One 2013, 8(5):e64091.
411. Kim SH, Debnath J, Mostov K, Park S, Hunt CA A computational approach to resolve cell level contributions to early glandular epithelial cancer progression. BMC Syst Biol 2009, 3:122.
412. Engelberg JA, Datta A, Mostov KE, Hunt CA MDCK cystogenesis driven by cell stabilization within computational analogues. PLoS Comput Biol 2011, 7(4):e1002030.
413. Dutta-Moscato J, Solovyev A, Mi Q, Nishikawa T, Soto-Gutierrez A, Fox IJ, et al. A multiscale agent-based in silico model of liver fibrosis progression. Front Bioeng Biotechnol 2014, 2:1-10. (Article 18).
414. Cockrell C, Christley S, An G Investigation of inflammation and tissue patterning in the gut using a Spatially Explicit General-purpose Model of Enteric Tissue (SEGMEnT). PLoS Comput Biol 2014, 10(3):e1003507.
415. Turner JR The gastrointestinal tract. Robbins and Cotran: pathological basis of disease 2004, Saunders-Elsevier, Philadelphia, PA. 8th ed. V. Kumar, A.K. Abbas, J.C. Aster, N. Fausto (Eds.).
416. Scoville DH, Sato T, He XC, Li L Current view: intestinal stem cells and signaling. Gastroenterology 2008, 134(3):849-864.
417. Pinto D, Gregorieff A, Begthel H, Clevers H Canonical Wnt signals are essential for homeostasis of the intestinal epithelium. Genes Dev 2003, 17(14):1709-1713.
418. Kuhnert F, Davis CR, Wang HT, Chu P, Lee M, Yuan J, et al. Essential requirement for Wnt signaling in proliferation of adult small intestine and colon revealed by adenoviral expression of Dickkopf-1. Proc Natl Acad Sci USA 2004, 101(1):266-271.
419. Gregorieff A, Clevers H Wnt signaling in the intestinal epithelium: from endoderm to cancer. Genes Dev 2005, 19(8):877-890.
420. Fevr T, Robine S, Louvard D, Huelsken J Wnt/beta-catenin is essential for intestinal homeostasis and maintenance of intestinal stem cells. Mol Cell Biol 2007, 27(21):7551-7559.
421. Kawano Y, Kypta R Secreted antagonists of the Wnt signalling pathway. J Cell Sci 2003, 116(Pt 13):2627-2634.
422. Investigating ontologies for simulation modeling. In: Proceedings of the thirty-seventh annual symposium on simulation 2004, IEEE Computer Society, Washington, DC. J.A. Miller, G.T. Baramidze, A.P. Sheth, P.A. Fishwick (Eds.).
423. Batts LE, Polk DB, Dubois RN, Kulessa H Bmp signaling is required for intestinal growth and morphogenesis. Dev Dyn 2006, 235(6):1563-1570.
424. Roberts AB, Sporn MB Transforming growth factor-b. The molecular and cellular biology of wound repair 1996, 275-308. Plenum Press, New York, NY. 2nd ed. R.A.F. Clark (Ed.).
425. Li NY, Vodovotz Y, Hebda PA, Abbott KV Biosimulation of inflammation and healing in surgically injured vocal folds. Ann Otol Rhinol Laryngol 2010, 119(6):412-423.
426. Solovyev A, Mi Q, Tzen Y-T, Brienza D, Vodovotz Y Hybrid equation-/agent-based model of ischemia-induced hyperemia and pressure ulcer formation predicts greater propensity to ulcerate in subjects with spinal cord injury. PLoS Comput Biol 2013, 9:e1003070.
427. Li NY, Verdolini AK, Rosen C, An G, Hebda PA, Vodovotz Y Translational systems biology and voice pathophysiology. Laryngoscope 2010, 120:511-515.
428. Li NYK, Verdolini K, Clermont G, Mi Q, Hebda PA, Vodovotz Y A patient-specific in silico model of inflammation and healing tested in acute vocal fold injury. PLoS One 2008, 3:e2789.
429. Regan MA, Teasell RW, Wolfe DL, Keast D, Mortenson WB, Aubut JA A systematic review of therapeutic interventions for pressure ulcers after spinal cord injury. Arch Phys Med Rehabil 2009, 90(2):213-231.
430. Bates-Jensen BM, Guihan M, Garber SL, Chin AS, Burns SP Characteristics of recurrent pressure ulcers in veterans with spinal cord injury. J Spinal Cord Med 2009, 32(1):34.
431. Bao F, Bailey CS, Gurr KR, Bailey SI, Rosas-Arellano MP, Dekaban GA, et al. Increased oxidative activity in human blood neutrophils and monocytes after spinal cord injury. Exp Neurol 2009, 215(2):308-316.
432. Frost F, Roach MJ, Kushner I, Schreiber P Inflammatory C-reactive protein and cytokine levels in asymptomatic people with chronic spinal cord injury. Arch Phys Med Rehabil 2005, 86(2):312-317.
433. Segal JL, Gonzales E, Yousefi S, Jamshidipour L, Brunnemann SR Circulating levels of IL-2 R, ICAM-1, and IL-6 in spinal cord injuries. Arch Phys Med Rehabil 1997, 78(1):44-47.
434. Edlich RF, Winters KL, Woodard CR, Buschbacher RM, Long WB, Gebhart JH, et al. Pressure ulcer prevention. J Long Term Eff Med Implants 2004, 14(4):285-304.
435. Lindblad WJ Considerations for selecting the correct animal model for dermal wound-healing studies. J Biomater Sci Polym Ed 2008, 19(8):1087-1096.
436. Peirce SM, Skalak TC, Rodeheaver GT Ischemia-reperfusion injury in chronic pressure ulcer formation: a skin model in the rat. Wound Repair Regen 2000, 8(1):68-76.
437. Vodovotz Y Translational systems biology of inflammation and healing. Wound Repair Regen 2010, 18(1):3-7.
438. Vodovotz Y At the interface between acute and chronic inflammation: insights from computational modeling. Chronic inflammation: nutritional and therapeutic interventions 2013, Taylor & Francis, Florence, KY. S. Roy, C. Sen (Eds.).
439. Kranke P, Bennett MH, Martyn-St James M, Schnabel A, Debus SE Hyperbaric oxygen therapy for chronic wounds. Cochrane Database Syst Rev 2012, 4:Cd004123.
440. Tzen YT, Brienza DM, Karg P, Loughlin P Effects of local cooling on sacral skin perfusion response to pressure: implications for pressure ulcer prevention. J Tissue Viability 2010, 19(3):86-97.
441. Tzen YT, Brienza DM, Karg PE, Loughlin PJ Effectiveness of local cooling for enhancing tissue ischemia tolerance in people with spinal cord injury. J Spinal Cord Med 2013, 36(4):357-364.
442. An G Concepts for developing a collaborative in silico model of the acute inflammatory response using agent-based modeling. J Crit Care 2006, 21(1):105-110. discussion 10-1, PMID: 16616634.
443. Uschold M, Gruninger M Ontologies: principles, methods and applications. Knowl Eng Rev 2009, 11:93-136.
444. Consortium TGO Gene ontology: tool for the unification of biology. Nat Genet 2000, 25(1):25-29.
445. Noy NF, Shah NH, Whetzel PL, Dai B, Dorf M, Griffith N, et al. BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res 2009, 1(37):170-173.
446. Rubin DL, Lewis SE, Mungall CJ, Misra S, Westerfield M, Ashburner M, et al. National Center for Biomedical Ontology: advancing biomedicine through structured organization of scientific knowledge. OMICS 2006, 10(2):185-198.
447. An G, Christley S Agent-based modeling and biomedical ontologies: a roadmap. Wiley Interdiscip Rev Comput Stat 2011, 3:343-356. ePublication: April 15. http://dx.doi.org/10.1002/wics.167.
448. Colasanti R, An G The Abstracted Biological Computational Unit (ABCU): introduction of a recursive descriptor for multi-scale computational modeling of biologica systems. J Crit Care 2009, 24:e35-e36.
449. Solovyev A, Mikheev M, Zhou L, Dutta-Moscato J, Ziraldo C, An G, et al. SPARK: a framework for multi-scale agent-based biomedical modeling. Int J Agent Technol Syst 2011, 2(3):18-31.
450. Railsback SF, Lytinen SL, Jackson SK Agent-based simulation platforms: review and development recommendations. Simulation 2006, 82(9):609-623.
451. Džeroski S, Langley P, Todorovski L Computational discovery of scientific knowledge. Lecture notes in computer science 2007, vol. 4660. Springer, Berlin/Heidelberg, p. 1-14.
452. Langley P The computational support of scientific discovery. Int J Hum Comput Stud 2000, 53(3):393-410.
453. Christley S, An G A proposal for augmenting biological model construction with a semi-intelligent computational modeling assistant. Comput Math Organ Theory 2012, 18(4):380-403.
454. Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, et al. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 2003, 19(4):524-531.
455. Schilstra MJ, Li L, Matthews J, Finney A, Hucka M, Le Novere N CellML2SBML: conversion of CellML into SBML. Bioinformatics 2006, 22(8):1018-1020.
456. Clavel M, Duran F, Eker S, Lincoln P, Marti-Oliet N, Meseguer J, et al. All about Maude-a high-performance logical framework 2007, Springer-Verlag, Berlin/Heidelberg.
457. Fishwick PA, Miller JA. Ontologies for modeling and simulation: issues and approaches. Simulation conference, 2004 proceedings of the 2004 Winter 2004;1: 264. doi:101109/WSC20041371324.
458. Investigating ontologies for simulation modeling. Proceedings of the thirty-seventh annual symposium on simulation 2004, IEEE Computer Society, Washington, DC. J.A. Miller, G.T. Baramidze, A.P. Sheth, P.A. Fishwick (Eds.).
459. Silver GA, Miller JA, Hybinette M, Baramidze G, York WS. DeMO: an ontology for discrete-event modeling and simulation. Simulation.
460. Cook DL, Mejino JLV, Neal ML, Gennari JH Bridging biological ontologies and biosimulation: the ontology of physics for biology. AMIA Annu Symp Proc 2008, 136-140.
461. Courtot M, Juty N, Knüpfer C, Waltemath D, Zhukova A, Dräger A, et al. Controlled vocabularies and semantics in systems biology. Mol Syst Biol 2011, 7:543.
462. Cuellar AA, Lloyd CM, Nielsen PF, Bullivant DP, Nickerson DP, Hunter PJ An overview of CellML 1.1, a biological model description language. Simulation 2003, 79(12):740-747.
463. Hoops S, Sahle S, Gauges R, Lee C, Pahle J, Simus N, et al. COPASI-a COmplex PAthway SImulator. Bioinformatics 2006, 22(24):3067-3074.
464. Matsuoka Y, Funahashi A, Ghosh S, Kitano H Modeling and simulation using CellDesigner. Methods Mol Biol 2014, 1164:121-145.
465. Izaguirre JA, Chaturvedi R, Huang C, Cickovski T, Coffland J, Thomas G, et al. CompuCell, a multi-model framework for simulation of morphogenesis. Bioinformatics 2004, 20(7):1129-1137.
466. Meseguer J Conditional rewriting logic as a unified model of concurrency. Theor Comput Sci 1992, 96:73-155.
467. An G, Nieman G, Vodovotz Y Computational and systems biology in trauma and sepsis: current state and future perspectives. Int J Burns Trauma 2012, 2(1):1-10. PMID: 22928162.
468. Fisher J, Henzinger TA Executable cell biology. Nat Biotechnol 2007, 25(11):1239-1249.
469. An G Introduction of an agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation. Theor Biol Med Model 2008, 5(1):11.
470. Hunt CA, Ropella GE, Lam TN, Tang J, Kim SH, Engelberg JA, et al. At the biological modeling and simulation frontier. Pharm Res 2009, 26:2369-2400.
471. Doyle J, Csete M Rules of engagement. Nature 2007, 446(7138):860.
472. Csete ME, Doyle JC Reverse engineering of biological complexity. Science 2002, 295(5560):1664-1669.
473. An G Closing the scientific loop: bridging correlation and causality in the petaflop age. Sci Transl Med 2010, 2(41):41ps34. PMID: 20650869. doi:10.1126/scitranslmed.3000390.
474. Bonabeau E, Bodick N, Armstrong RW A more rational approach to new-product development. Harv Bus Rev 2008, 86(3):96-102. 134.
475. U.S. Food and Drug Administration. Innovation or stagnation: challenge and opportunity on the critical path to new medical products; March 2004.