From data patterns to mechanistic models in acute critical illness

Journal of Critical Care

Volumn 29, Issue 4, 2014, Pages 604-610

  Aerts, Jean Marie ^a   Haddad, Wassim M ^b   An, Gary ^c   Vodovotz, Yoram ^d,e  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b Georgia Institute of Technology   (United States)

^c UNIVERSITY OF CHICAGO MEDICAL CENTER   (United States)

^d UNIVERSITY OF PITTSBURGH MEDICAL CENTER   (United States)

^e UNIVERSITY OF PITTSBURGH   (United States)

Author keywords

Acute critical illness; Anesthesia; Inflammation; Mathematical models; Sepsis; Trauma

Indexed keywords

ACUTE DISEASE; CRITICAL ILLNESS; DATA ANALYSIS; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST; DISEASE MODEL; EARLY DIAGNOSIS; GENERAL ANESTHESIA; HUMAN; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MOLECULAR BIOLOGY; MULTIPLE ORGAN FAILURE; MULTIVARIATE ANALYSIS; NEUROSCIENCE; NONHUMAN; PATHOPHYSIOLOGY; POSITIVE FEEDBACK; PROGNOSIS; REVIEW; SYSTEMIC INFLAMMATORY RESPONSE SYNDROME; BIOLOGICAL MODEL; COMPUTER SIMULATION; HEMORRHAGE; INFECTION; MEDICAL SOCIETY; ORGANIZATION AND MANAGEMENT; THEORETICAL MODEL; TRANSLATIONAL RESEARCH;

BIOLOGICAL MARKER;

ACUTE DISEASE; ANESTHESIA, GENERAL; BIOLOGICAL MARKERS; COMPUTER SIMULATION; CRITICAL ILLNESS; HEMORRHAGE; HUMANS; INFECTION; MODELS, NEUROLOGICAL; MODELS, THEORETICAL; NEUROSCIENCES; SOCIETIES, MEDICAL; TRANSLATIONAL MEDICAL RESEARCH;

EID: 84902254038     PISSN: 08839441     EISSN: 15578615     Source Type: Journal    
DOI: 10.1016/j.jcrc.2014.03.018     Document Type: Review

References (140)

1. Angus D.C., Linde-Zwirble W.T., Lidicker J., Clermont G., Carcillo J., Pinsky M.R. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med 2001, 29:1303-1310.
2. Namas R., Ghuma A., Hermus L., Zamora R., Okonkwo D.O., Billiar T.R., 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.
3. Patton G.C., Coffey C., Sawyer S.M., Viner R.M., Haller D.M., Bose K., et al. Global patterns of mortality in young people: a systematic analysis of population health data. Lancet 2009, 374:881-892.
4. World Health Organization report: young people: health risks and solutions 2011.
5. Mitka M. Drug for severe sepsis is withdrawn from market, fails to reduce mortality. JAMA 2011, 306:2439-2440.
6. Angus D.C. The search for effective therapy for sepsis: back to the drawing board?. JAMA 2011, 306:2614-2615.
7. Vodovotz Y., Constantine G., Rubin J., Csete M., Voit E.O., An G. Mechanistic simulations of inflammation: current state and future prospects. Math Biosci 2009, 217:1-10.
8. 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.).
9. Vodovotz Y. Translational systems biology of inflammation and healing. Wound Repair Regen 2010, 18:3-7.
10. Parker S.J., Watkins P.E. Experimental models of gram-negative sepsis. Br J Surg 2001, 88:22-30.
11. Marshall J.C., Deitch E., Moldawer L.L., Opal S., Redl H., Poll T.V. Preclinical models of shock and sepsis: what can they tell us?. Shock 2005, 24(Suppl 1):1-6.
12. Vodovotz Y., Chow C.C., Bartels J., Lagoa C., Prince J., Levy R., et al. In silico models of acute inflammation in animals. Shock 2006, 26:235-244.
13. 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.
14. Buchman T.G., Cobb J.P., Lapedes A.S., Kepler T.B. Complex systems analysis: a tool for shock research. Shock 2001, 16:248-251.
15. Tjardes T., Neugebauer E. Sepsis research in the next millennium: concentrate on the software rather than the hardware. Shock 2002, 17:1-8.
16. Xiao W., Mindrinos M.N., Seok J., Cuschieri J., Cuenca A.G., Gao H., et al. A genomic storm in critically injured humans. J Exp Med 2011, 208:2581-2590.
17. Gang Y., Malik M. Heart rate variability in critical care medicine. Curr Opin Crit Care 2002, 8:371-375.
18. Moorman J.R., Lake D.E., Griffin M.P. Heart rate characteristics monitoring for neonatal sepsis. IEEE Trans Biomed Eng 2006, 53:126-132.
19. Voss A., Schulz S., Schroeder R., Baumert M., Caminal P. Methods derived from nonlinear dynamics for analysing heart rate variability. Philos Transact A Math Phys Eng Sci 2009, 367:277-296.
20. Bravi A., Longtin A., Seely A.J. Review and classification of variability analysis techniques with clinical applications. Biomed Eng Online 2011, 10:90.
21. Godin P.J., Buchman T.G. Uncoupling of biological oscillators: a complementary hypothesis concerning the pathogenesis of multiple organ dysfunction syndrome. Crit Care Med 1996, 24:1107-1116.
22. Annane D., Trabold F., Sharshar T., Jarrin I., Blanc A.S., Raphael J.C., et al. Inappropriate sympathetic activation at onset of septic shock: a spectral analysis approach. Am J Respir Crit Care Med 1999, 160:458-465.
23. Korach M., Sharshar T., Jarrin I., Fouillot J.P., Raphael J.C., Gajdos P., et al. Cardiac variability in critically ill adults: influence of sepsis. Crit Care Med 2001, 29:1380-1385.
24. Piepoli M., Garrard C.S., Kontoyannis D.A., Bernardi L. Autonomic control of the heart and peripheral vessels in human septic shock. Intensive Care Med 1995, 21:112-119.
25. Fairchild K.D., Saucerman J.J., Raynor L.L., Sivak J.A., Xiao Y., Lake D.E., et al. Endotoxin depresses heart rate variability in mice: cytokine and steroid effects. Am J Physiol Regul Integr Comp Physiol 2009, 297:R1019-R1027.
26. 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, 17:354-381.
27. Kleiger R.E., Stein P.K., Bigger J.T. Heart rate variability: measurement and clinical utility. Ann Noninvasive Electrocardiol 2005, 10:88-101.
28. Pomeranz B., Macaulay R.J., Caudill M.A., Kutz I., Adam D., Gordon D., et al. Assessment of autonomic function in humans by heart rate spectral analysis. Am J Physiol 1985, 248:H151-H153.
29. Godin P.J., Fleisher L.A., Eidsath A., Vandivier R.W., Preas H.L., Banks S.M., et al. Experimental human endotoxemia increases cardiac regularity: results from a prospective, randomized, crossover trial. Crit Care Med 1996, 24:1117-1124.
30. Barnaby D., Ferrick K., Kaplan D.T., Shah S., Bijur P., Gallagher E.J. Heart rate variability in emergency department patients with sepsis. Acad Emerg Med 2002, 9:661-670.
31. Chen W.L., Kuo C.D. Characteristics of heart rate variability can predict impending septic shock in emergency department patients with sepsis. Acad Emerg Med 2007, 14:392-397.
32. 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, 18:156-163.
33. 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:e6642.
34. Magder S. Bench-to-bedside review: ventilatory abnormalities in sepsis. Crit Care 2009, 13:202.
35. Preas H.L., Jubran A., Vandivier R.W., Reda D., Godin P.J., Banks S.M., et al. Effect of endotoxin on ventilation and breath variability: role of cyclooxygenase pathway. Am J Respir Crit Care Med 2001, 164:620-626.
36. An G. Phenomenological issues related to the measurement, mechanisms and manipulation of complex biological systems. Crit Care Med 2006, 34:245-246.
37. Chung T.P., Laramie J.M., Province M., Cobb J.P. Functional genomics of critical illness and injury. Crit Care Med 2002, 30:S51-S57.
38. Cobb J.P., O'Keefe G.E. Injury research in the genomic era. Lancet 2004, 363:2076-2083.
39. Wurfel M.M. Microarray-based analysis of ventilator-induced lung injury. Proc Am Thorac Soc 2007, 4:77-84.
40. Winkelman C. Inflammation and genomics in the critical care unit. Crit Care Nurs Clin North Am 2008, 20:213-221.
41. Nguyen A., Yaffe M.B. Proteomics and systems biology approaches to signal transduction in sepsis. Crit Care Med 2003, 31:S1-S6.
42. Bauer M., Reinhart K. Molecular diagnostics of sepsis-where are we today?. Int J Med Microbiol 2010, 300:411-413.
43. Claus R.A., Otto G.P., Deigner H.P., Bauer M. Approaching clinical reality: markers for monitoring systemic inflammation and sepsis. Curr Mol Med 2010, 10:227-235.
44. Langley R.J., Tsalik E.L., van Velkinburgh J.C., Glickman S.W., Rice B.J., Wang C., et al. An integrated clinico-metabolomic model improves prediction of death in sepsis. Sci Transl Med 2013, 5:195ra95.
45. Serkova N.J., Standiford T.J., Stringer K.A. The emerging field of quantitative blood metabolomics for biomarker discovery in critical illnesses. Am J Respir Crit Care Med 2011, 184:647-655.
46. Gough N.R., Yaffe M.B. Focus issue: conquering the data mountain. Sci Signal 2011, 4:eg2.
47. Calvano S.E., Xiao W., Richards D.R., Felciano R.M., Baker H.V., Cho R.J., et al. A network-based analysis of systemic inflammation in humans. Nature 2005, 437:1032-1037.
48. Liu T., Qian W.J., Gritsenko M.A., Xiao W., Moldawer L.L., Kaushal A., et al. High dynamic range characterization of the trauma patient plasma proteome. Mol Cell Proteomics 2006, 5:1899-1913.
49. McDunn J.E., Husain K.D., Polpitiya A.D., 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:e1564.
50. Warren H.S., Elson C.M., Hayden D.L., Schoenfeld D.A., Cobb J.P., Maier R.V., et al. A genomic score prognostic of outcome in trauma patients. Mol Med 2009, 15:220-227.
51. Cobb J.P., Moore E.E., Hayden D.L., Minei J.P., Cuschieri J., Yang J., et al. Validation of the riboleukogram to detect ventilator-associated pneumonia after severe injury. Ann Surg 2009, 250:531-539.
52. Qian W.J., Petritis B.O., Kaushal A., Finnerty C.C., Jeschke M.G., Monroe M.E., et al. Plasma proteome response to severe burn injury revealed by 18O-labeled "universal" reference-based quantitative proteomics. J Proteome Res 2010, 9:4779-4789.
53. 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 U S A 2010, 107:9923-9928.
54. 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.
55. 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.
56. Zaaqoq A.M., 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.
57. 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.
58. Namas R., Zamora R., Namas R., An G., Doyle J., Dick T.E., et al. Sepsis: Something old, something new, and a systems view. J Crit Care 2012, 27:314e1-314e11.
59. An G., Namas R., Vodovotz Y. Sepsis: From pattern to mechanism and back. Crit Rev Biomed Eng 2012, 40:341-351.
60. Dick T.E., Molkov Y., Nieman G., Hsieh Y., Jacono F.J., Doyle J., et al. Linking inflammation and cardiorespiratory variability in sepsis via computational modeling. Front Physiol 2012, 3:222.
61. Kwan A., Hubank M., Rashid A., Klein N., Peters M.J. Transcriptional instability during evolving sepsis may limit biomarker based risk stratification. PLoS One 2013, 8:e60501.
62. Ljung L. System identification: theory for the user 1987, Prentice Hall, Englewood Cliffs, NJ.
63. Janes K.A., Yaffe M.B. Data-driven modelling of signal-transduction networks. Nat Rev Mol Cell Biol 2006, 7:820-828.
64. Young P.C. Recursive estimation and time series analysis 2011, Springer Berlin Heidelberg, Berlin, Germany.
65. Nicholson A.J. An outline of the dynamics of animal populations. Aust J Zool 1954, 2:9-65.
66. Aerts J.M., 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.
67. Ingolia N.T., Weissman J.S. Systems biology: reverse engineering the cell. Nature 2008, 454:1059-1062.
68. Amirpour Haredasht S., Barrios J.M., Maes P., Verstraeten W.W., Clement J., Ducoffre G., et al. A dynamic data-based model describing nephropathia epidemica in Belgium. Biosyst Eng 2011, 109:77-89.
69. Scheffer M., Bascompte J., Brock W.A., Brovkin V., Carpenter S.R., Dakos V., et al. Early-warning signals for critical transitions. Nature 2009, 461:53-59.
70. Van L.K., Guiza F., Meyfroidt G., Aerts J.M., Ramon J., Blockeel H., et al. Prediction of clinical conditions after coronary bypass surgery using dynamic data analysis. J Med Syst 2010, 34:229-239.
71. Janes K.A., Albeck J.G., Gaudet S., Sorger P.K., Lauffenburger D.A., Yaffe M.B. A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science 2005, 310:1646-1653.
72. 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.
73. Camacho E.F., Bordons C. Model predictive control 2004, Springer-Verlag, Berlin, Germany.
74. Aerts J.M., Lippens M., De Groote G., Buyse J., Decuypere E., Vranken E., et al. Recursive prediction of broiler growth response to feed intake by using a time-variant parameter estimation method. Poult Sci 2003, 82:40-49.
75. Taylor C.J., Pedregal D.J., Young P.C., Tych W. Environmental time series analysis and forecasting with the CAPTAIN toolbox. Environ Model Softw 2007, 22:797-814.
76. 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.
77. An G., Nieman G., Vodovotz Y. Toward computational identification of multiscale tipping points in multiple organ failure. Ann Biomed Eng 2012, 40:2412-2424.
78. Wiskwo J.P., 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.
79. Tambuyzer T., Ahmed T., Taylor C.J., Berckmans D., Balschun D., Aerts J.M. System identification of mGluR-dependent long-term depression. Neural Comput 2013, 25:650-670.
80. Willems J.C. The behavioral approach to open interconnected systems. IEEE Control Syst Mag 2007, 27:46-99.
81. Young P.C. The data-based mechanistic approach to the modelling, forecasting and control of environmental systems. Annu Rev Control 2006, 30:169-182.
82. Mora F.A., Passariello G., Carrault G., Le Pichon J.P. Intelligent patient and monitoring systems: a review. IEEE Eng Med Biol Mag 1993, 124:23-33.
83. Cangar O., Aerts J.M., Vranken E., Berckmans D. Effects of different target trajectories on the broiler performance in growth control. Poult Sci 2008, 87:2196-2207.
84. Vodovotz Y., Csete M., Bartels J., Chang S., An G. Translational systems biology of inflammation. PLoS Comput Biol 2008, 4:1-6.
85. Alt W., Lauffenburger D.A. Transient behavior of a chemotaxis system modelling certain types of tissue inflammation. J Math Biol 1987, 24:691-722.
86. Kumar R., Clermont G., Vodovotz Y., Chow C.C. The dynamics of acute inflammation. J Theor Biol 2004, 230:145-155.
87. Reynolds A., Rubin J., Clermont G., Day J., Vodovotz Y., Ermentrout G.B. 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.
88. Day J., Rubin J., Vodovotz Y., Chow C.C., 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.
89. Fiala D., Lomas K.J., Stohrer M. A computer model of human thermoregulation for a wide range of environmental conditions: the passive system. J Appl Physiol 1985, 87:1957-1972.
90. Bruce J.M., Clark J.J. Models of heat production and critical temperature for growing pigs. Anim Prod 1979, 28:353-369.
91. Black J.L., Campbell R.G., Williams I.H., James K.J., Davies G.T. Simulation of energy and amino acid utilisation in the pig. Res Dev Agric 1986, 3:121-145.
92. Kendall B.E., Briggs C.J., Murdoch W.W., Turchin P., Ellner S.P., McCauley E., et al. Why do populations cycle? A synthesis of statistical and mechanistic modeling approaches. Ecology 1999, 80:1789-1805.
93. 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.
94. An G. Closing the scientific loop: bridging correlation and causality in the petaflop age. Sci Transl Med 2010, 2:41ps34.
95. Bridges T.C., Gates R.S., Chao K.L., Turner L.W., Minagawa H. Techniques for development of swine performance response surfaces. Trans ASAE 1995, 38:1505-1511.
96. Aerts J.M., van Buggenhout S., Lippens M., Buyse J., Decuypere E., Vranken E., et al. Active control of the growth trajectory of broiler chickens based on on-line animal responses. Poult Sci 2003, 82:1853-1862.
97. 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.
98. 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.
99. Foteinou P.T., Calvano S.E., Lowry S.F., Androulakis I.P. Modeling endotoxin-induced systemic inflammation using an indirect response approach. Math Biosci 2009, 217:27-42.
100. An G., Faeder J.R. Detailed qualitative dynamic knowledge representation using a BioNetGen model of TLR-4 signaling and preconditioning. Math Biosci 2009, 217:53-63.
101. 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.
102. Yang Q., Calvano S.E., Lowry S.F., Androulakis I.P. A dual negative regulation model of Toll-like receptor 4 signaling for endotoxin preconditioning in human endotoxemia. Math Biosci 2011, 232:151-163.
103. Fu Y., Glaros T., Zhu M., Wang P., Wu Z., Tyson J.J., et al. Network topologies and dynamics leading to endotoxin tolerance and priming in innate immune cells. PLoS Comput Biol 2012, 8:e1002526.
104. Song S.O., Hogg J., Peng Z.Y., Parker R., Kellum J.A., Clermont G. Ensemble models of neutrophil trafficking in severe sepsis. PLoS Comput Biol 2012, 8:e1002422.
105. 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.
106. An G. In-silico experiments of existing and hypothetical cytokine-directed clinical trials using agent based modeling. Crit Care Med 2004, 32:2050-2060.
107. Li N.Y.K., Verdolini K., Clermont G., Mi Q., Hebda P.A., Vodovotz Y. A patient-specific in silico model of inflammation and healing tested in acute vocal fold injury. PLoS One 2008, 3:e2789.
108. 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.
109. Mi Q., Li N.Y.K., 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.
110. Hua F., Hautaniemi S., Yokoo R., Lauffenburger D.A. Integrated mechanistic and data-driven modelling for multivariate analysis of signalling pathways. J R Soc Interface 2006, 3:515-526.
111. Song D., Marmarelis V.Z., Berger T.W. Parametric and non-parametric modeling of short-term synaptic plasticity. Part I: computational study. J Comput Neurosci 2009, 26:1-19.
112. Hameroff S. The entwined mysteries of anesthesia and consciousness. Is there a common underlying mechanism?. Anesthesiology 2006, 105:400-412.
113. Mashour G.A. Consciousness unbound: toward a paradigm of general anesthesia. Anesthesiology 2004, 100:428-433.
114. Zecharia A.Y., Franks N.P. General anesthesia and ascending arousal pathways. Anesthesiology 2009, 111:695-696.
115. Sonner J.M., Antognini J.F., Dutton R.C., Flood P., Gray A.T., Harris R.A., et al. Inhaled anesthetics and immobility: mechanisms, mysteries, and minimum alveolar anesthetic concentration. Anesth Analg 2003, 97:718-740.
116. Campagna J.A., Miller K.W., Forman S.A. Mechanisms of actions of inhaled anesthetics. N Engl J Med 2003, 348:2110-2124.
117. John E.R., Prichep L.S. The anesthetic cascade: a theory of how anesthesia suppresses consciousness. Anesthesiology 2005, 102:447-471.
118. Kitamura A., Marszalec W., Yeh J.Z., Narahashi T. Effects of halothane and propofol on excitatory and inhibitory synaptic transmission in rat cortical neurons. J Pharmacol Exp Ther 2003, 304:162-171.
119. Hutt A., Longtin A. Effects of the anesthetic agent propofol on neural populations. Cogn Neurodyn 2010, 4:37-59.
120. Tinker J.H., Sharbrough F.W., Michenfelder J.D. Anterior shift of the dominant EEG rhythm during anesthesia in the Java monkey: correlation with anesthetic potency. Anesthesiology 1977, 46:252-259.
121. Rampil I.J. A primer for EEG signal processing in anesthesia. Anesthesiology 1998, 89:980-1002.
122. Vuyk J., Lim T., Engbers F.H., Burm A.G., Vletter A.A., Bovill J.G. Pharmacodynamics of alfentanil as a supplement to propofol or nitrous oxide for lower abdominal surgery in female patients. Anesthesiology 1993, 78:1036-1045. [discussion 23A].
123. Gray C.M. Synchronous oscillations in neuronal systems: mechanisms and functions. J Comput Neurosci 1994, 1:11-38.
124. Bullock T.H., McClune M.C., Achimowicz J.Z., Iragui-Madoz V.J., Duckrow R.B., Spencer S.S. Temporal fluctuations in coherence of brain waves. Proc Natl Acad Sci U S A 1995, 92:11568-11572.
125. Buzski G. Rhythms of the brain 2006, Oxford University Press, New York, NY.
126. Voss L.J., Sleigh J.W., Barnard J.P., Kirsch H.E. The howling cortex: seizures and general anesthetic drugs. Anesth Analg 2008, 107:1689-1703.
127. Steyn-Ross M.L., Steyn-Ross D.A., Sleigh J.W. Modelling general anaesthesia as a first-order phase transition in the cortex. Prog Biophys Mol Biol 2004, 85:369-385.
128. Hui Q., Haddad W.M., Bailey J.M., Hayakawa T. A stochastic mean field model for an excitatory and inhibitory synaptic drive cortical neuronal network. IEEE Trans Neural Netw 2013, 25:751-763.
129. Hui Q., Haddad W.M., Bailey J.M. Multistability, bifurcations, and biological neural networks: a Synaptic drive firing model for cerebral cortex transition in the induction of general anesthesia. Nonlinear Analy Hybrid Syst 2011, 5:554-572.
130. Haddad W.M. A unification between dynamical system theory and thermodynamics involving an energy, mass, and entropy state space formalism. Entropy 2013, 15:1821-1846.
131. Godsil C., Royle G. Algebraic graph theory 2001, Springer-Verlag, New York, NY.
132. Macklem P.T., Seely A. Towards a definition of life. Perspect Biol Med 2010, 53:330-340.
133. Seely A.J., Macklem P. Fractal variability: an emergent property of complex dissipative systems. Chaos 2012, 22:013108.
134. Bircher J. Towards a dynamic definition of health and disease. Med Health Care Philos 2005, 8:335-341.
135. Goldberger A.L., Rigney D.R., West B.J. Chaos and fractals in human physiology. Sci Am 1990, 262:42-49.
136. Goldberger A.L., Peng C.K., Lipsitz L.A. What is physiologic complexity and how does it change with aging and disease?. Neurobiol Aging 2002, 23:23-27.
137. Haddad W.M., Chellaboina V., Nersesov S.G. Thermodynamics. A dynamical systems approach 2005, Princeton University Press, Princeton, NJ.
138. Haddad W.M., Chellaboina V., Nersesov S.G. Time-reversal symmetry, Poincaré recurrence, irreversibility, and the entropic arrow of time: from mechanics to system thermodynamics. Nonlinear Anal Real World Appl 2008, 9:250-271.
139. Haddad W.M. Temporal asymmetry, entropic irreversibility, and finite-time thermodynamics: from Parmenides-Einstein time-reversal symmetry to the Heraclitan entropic arrow of time. Entropy 2012, 14:407-455.
140. Zeng X., Hui Q., Haddad W.M., Hayakawa T. Synchronization of biological neural network systems with stochastic perturbations and time delay. J Franklin Inst 2013, [In Press].