Milestones of the Modeling of Human Physiology

Rafik D. Grygoryan (Head of department “Human systems modeling”, Cybernetics Center, Institute of software systems of National Academy of Sciences, Kiev, Ukraine)

Article ID: 1905

DOI: https://doi.org/10.30564/jhp.v2i1.1905

Abstract


An overview of about 70-year research efforts in area of mathematical modeling of human physiology is provided. The overview has two goals: i) to recognize the main advantages and causes of disadvantages or disappointments; ii) to distinguish the most promising approach for creating future models. Until recently, efforts in the modeling of quantitative physiology were concentrated on the solving of three main types of tasks: 1) how to establish the input-output dynamic characteristics of a given isolated organ or isolated anatomical-functional system (AFS); 2) how to create a computer-based simulator of physiological complex systems (PCM) containing many organs and AFSs; and 3) how to create multi-scale models capable of simulating and explaining causalities in organs, AFSs, PCMs, and in the entire organism in terms that will allow using such models for simulating pathological scenarios (the “Physiome” project) too.  The critical analysis of the modeling experience and recent physiological concepts convinced us that the platform provided by the paradigm of physiological super-systems (PPS) looks like the most promising platform for further modeling. PPS causally combines activities of specific intracellular mechanisms (self-tunable but of limited capacities) with their extracellular enhancers. The enhancement appears due to the increase of nutrients incomes toward cells affected because of low energy and inadequate chemical composition of cytoplasm. Every enhancer has its activator chemicals released by the affected cells. In fact, PPS, indicating causal relationships between cell-scale and upper-scales (in organs, AFSs, PCMs) physiological activities, is the single platform for future models. They must definitely describe when and how the bottom-to-up information flows do appear and how is the organism-scale adaptation activated against destructive trends in cells.


Keywords


Computer simulations;Visualization;Theoretical Analysis;Quantification;Multi-scale modeling;Physiome

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References


[1] Cannon WB. Organization for physiological homeostasis. Physiol. Rev., 1929, 9,399-431.

[2] Wiener N., Rosenblueth A. The mathematical formulation of the problem of conduction of impulses in a network of connected excitable elements, specifically in cardiac muscle. Arch. Inst. Cardiologia de Mexico. 1946. XVI. 3–4. 205–265.

[3] Wiener N. Cybernetics: Or Control and Communication in the Animal and the Machine. 1948,Technology Press.

[4] Schrodinger E. What is life? The Physical Aspect of the Living Cell. 1944, ‎Cambridge University Press,

[5] Grodins FS, Gray JS, Schroeder KR, Norins AL, Jones RW. Respiratory responses to CO2 inhalation. A theoretical study of a nonlinear biological regulator. J Appl Physiol. 1954, 7(3):283–308.

[6] Grodins F. Control Theory and Biological Systems. N. Y.: Columbia. Univ. Press, 1963.

[7] Beneken GW. Investigation on the regulatory system of the blood circulation. In: Circulatory Analog Computers. -Amsterdam: North-Holland Publishing company, 1963, 16-28.

[8] Beneken GW. A mathematical approach to cardiovascularfunction. The uncontrolled human system. Drukkerij Elinknjk Utrecht, 1965.

[9] Defares YJ, Osborn JJ, Hara HH. Theoretical synthesis of the cardiovascular system. Acta Physiol, Pharmacol., -Neerl., 1962, 12, 189-265.

[10] De Pater L., van den Berg J.W. An Electrical Analogue of the Entire Human Circulatory System. Medical Electronics and Biological Engineering, 1964, 11, 161-166.

[11] Scher AM, Young AC. Servoanalysis of carotid sinus effects on peripheral resistance. Circ. Res., 12, 1963, 152-162.

[12] Defares Y.J. Examples of cardiovascular models simulation of the cardiovascular system. J. Chron. Dis., 1966, 19, 401-410.

[13] Guyton AC, Coleman TG, Granger HJ. Circulation. Overall Regulation. Annual Review of Physiology, 1972, vol. 34, pp. 13-46.

[14] Sagawa K. Comparative models of overall circulatory mechanics. In: Brown JHV, Dikson JP Advances in Bio-med. Eng. Academ. Press. - New-York, London, 1973, 3, 1-96.

[15] Sagawa K. Concerning "gain". Am J Physiol. 1978;235(2):H117.

[16] Guyton AC. Renal servo-control of arterial blood pressure. J. Appl. Physiol, 1967,22, 139–142.

[17] Guyton AC. Long-term arterial pressure control: an analysis from animal experiments and ... in the pathogenesis of hypertension, N Engl J Med 356:2007, 1966.

[18] Osborn JW, Jacob F, Guzman P. A neural set point for the long-term control of arterial pressure: beyond the arterial baroreceptor reflex. Am J Physiol Regul Integr Comp Physiol. 2005 Apr; 288(4):R846-855.

[19] Brands MW. Chronic blood pressure control. Compr Physiol. 2012, 2(4): 2481-2494.

[20] Dorrington KL, Pandit JJ. The obligatory role of the kidney in long-term arterial blood pressure control: extending Guyton's model of the circulation. Anaesthesia. 2009 Nov; 64(11):1218-1228.

[21] Nishida Y, Tandai-Hiruma, M, Kemuriyama, T. et al. Long-term blood pressure control: is there a set-point in the brain?. J Physiol Sci. 2012, 62, 147–161. https://doi.org/10.1007/s12576-012-0192-0.

[22] BensonJF, SchoemanJP, VenterFJ, Ker JA, ZeilerGE, Bester L, van Niekerk J, Tintinger GR. Aortic Arch Baroreceptor Stimulation in an Experimental Goat Model: A Novel Method to Lower Blood Pressure Front. Cardiovasc. Med., 2019 | https://doi.org/10.3389/fcvm.2018.00193.

[23] Burgoyne S, Georgakopoulos D, Belenkie I, Tyberg JV. Systemic vascular effects of acute electrical baroreflex stimulation. AJP Hear Circ Physiol. 2014, 307:H236–241. doi: 10.1152/ajpheart.00422.2013.

[24] Hori D, Max L, Laflam A, Brown C, Neufeld KJ, Adachi H, Sciortino C, Conte JV, Cameron DE, Hogue CW Jr, Mandal K. Blood Pressure Deviations From Optimal Mean Arterial Pressure During Cardiac Surgery Measured With a Novel Monitor of Cerebral Blood Flow and Risk for Perioperative Delirium: A Pilot Study. J Cardiothorac Vasc Anesth. 2016,30(3):606-612. doi: 10.1053/j.jvca.2016.01.012.

[25] Höcht C. Blood Pressure Variability: Prognostic Value and Therapeutic Implications. International Scholarly Research Notices. 2013, Notices. https://doi.org/10.5402/2013/398485.

[26] Raven PB, Chapleau MW. Blood pressure regulation: overview and future research directions. Eur J Appl Physiol. 2014,114(3):579‐586. doi:10.1007/s00421-014-2823-z.

[27] Nishi EE, Bergamaschi CT, Campos RR. The crosstalk between the kidney and the central nervous system: the role of renal nerves in blood pressure regulation. Exp Physiol. 2015;100(5):479-484.

[28] doi:10.1113/expphysiol.2014.079889.

[29] Averina VA, Othmer HG, Fink GD, Osborn JW. A new conceptual paradigm for the haemodynamics of salt-sensitive hypertension: a mathematical modelling approach. J Physiol. 2012; 590(23):5975-5992.

[30] Bolívar JJ. Essential Hypertension: An Approach to Its Etiology and Neurogenic Pathophysiology. Int J Hypertens. 2013; doi: 10.1155/2013/547809.

[31] Ivy JR, Bailey MA. Pressure natriuresis and the renal control of arterial blood pressure. J Physiol. 2014; 15;592(18):3955-67. doi: 10.1113/jphysiol.2014.271676.

[32] Grygoryan R.D. Comprehension of individual adaptation mechanisms: endogenous tuning of constants determining optimal physiological states. Slovak international scientific journal, 2019,32:67-72.

[33] Grygoryan R.D., Sagach V.F. The concept of physiological supersystems: New stage of integrative physiology. Intern. J. of Physiol. and Pathophysiology, 2018: 9,2: 169-180.

[34] Grygoryan RD. Endogenous variators of individual health corridor. Znanstvena misel journal, 2019, 35: 31-37.

[35] Grygoryan RD. The Optimal Coexistence of Cells: How Could Human Cells Create The Integrative Physiology. Journal of Human Physiology.2019, 1 (01):8-28. DOI 10.30564/jhp.v1i1.1386.

[36] Kokalari I. Review on lumped parameter method for modeling the blood flow in systemic arteries. Journal of Biomedical Science and Engineering 2013, 06(01): 92-99. DOI: 10.4236/jbise.2013.61012.

[37] Shimizu S. Une D, KawadaT, HayamaY, KamiyaA, Shishido T, Sugimachi T. Lumped parameter model for hemodynamic simulation of congenital heart diseases The Journal of Physiological Sciences, 2018; 68:103–111. https://doi.org/10.1007/s12576-017-0585-1.

[38] Corsini C, Baker C, Kung E, Schievano S, Arbia G, Baretta A, Biglino G, Migliavacca F, Dubini G, Pennati G, Marsden A, Vignon-Clementel I, Taylor A, Hsia TY, Dorfman A, Modeling of Congenital Hearts Alliance (MOCHA) Investigators: An integrated approach to patient-specifc predictive modeling for single ventricle heart palliation. Comput Methods Biomech Biomed Engin. 2014; 17:1572–1589.

[39] Dampney RAL. Resetting of the Baroreflex Control of Sympathetic Vasomotor Activity during Natural Behaviors: Description and Conceptual Model of Central Mechanisms. Front Neurosci. 2017;11:461. Published 2017. doi:10.3389/fnins.2017.00461.

[40] Wallin BG, Charkoudian N. Sympathetic neural control of integrated cardiovascular function: insights from measurement of human sympathetic nerve activity. Muscle Nerve. 2007; 36(5):595-614.

[41] Pope SR, Ellwein L, Zapata EC, Novak V., Kelley CT, Olufsen MS. Estimation and identification of parameters in a lumped cerebrovascular model, Mathematical Biosciences and Engineering 2009,6 (1), 93–115.

[42] Rideout V. Mathematical and computer modeling of physiological systems, Prentice Hall, Englewood Cliffs, NJ, 1991.

[43] Sunagawa K, Sagawa K. Models of ventricular contraction based on time-varying elastance, Critical Revies in Biomedical Engineering 1982,7 (3), 193–228.

[44] Timischl S., A global model of the cardiovascular and respiratory system, Ph.D. thesis, University of Graz, Institute for Mathematics and Scientific Computing, 1998.

[45] Grygoryan RD, Degoda AG, Dzhurinsky EA, Kharsun DS. A simulator of a pulsatile heart. Problrms in programming,2017,4,98-108.

[46] DOI: https://doi.org/10.15407/pp2017.04.098 .

[47] Cowley A. W., Jr. (1992). Long-term control of arterial blood pressure. Physiol Rev 72,231–300.

[48] The IUPS Physiome Project (http://www.iups.org/physiome-project/).

[49] Hunter PJ, Viceconti M.The VPH-Physiome Project: Standards and Tools for Multiscale Modeling in Clinical Applications. 2009 IEEE Reviews in Biomedical Engineering 2:40 – 53 DOI: 10.1109/RBME.2009.2036204.

[50] Bassingthwaighte JB and Chinn TM. Re-examining Michaelis-Menten enzyme kinetics for xanthine oxidase. Adv Physiol Educ, 37: 37-48, 2013.

[51] Bassingthwaighte JB, Butterworth E, Jardine B, and Raymond G. Compartmental modeling in the analysis of biological systems. In: Computational Toxicology (Methods in Molecular Biology Series), edited by Brad Reisfeld and Arthur N Mayeno. New York NY: Springer Science+Business Media LLC, 2012, 929, 391-438.

[52] Bassingthwaighte JB, Beard DA, Carlson BE, Dash RK, and Vinnakota K. Modeling to link regional myocardial work, metabolism and blood flows. Ann Biomed Eng. 2012 Nov;40(11):2379-98. doi: 10.1007/s10439-012-0613-5.

[53] Niederer FM, Cherry EM, Fenton FH, Koivumäki JT, Seemann G, Thul R, Zhang H, Sachse FB, Beard D, et al. Prog Biophys Mol Biol. 2011; 104(1-3):2-21.

[54] Maas AH, Rozendaal YJ, van Pul C, Hilbers PA, Cottaar WJ, Haak HR, van Riel NA. A physiology-based model describing heterogeneity in glucose metabolism: the core of the Eindhoven Diabetes Education Simulator (E-DES). J Diabetes Sci Technol. 2015 Mar; 9(2):282-292.

[55] Shiang KD, Kandeel F. A computational model of the human glucose-insulin regulatory system. J Biomed Res. 2010;24(5):347‐364. doi:10.1016/S1674-8301(10)60048-6.

[56] De Gaetano A, Gaz C, Panunzi S. Consistency of compact and extended models of glucose-insulin homeostasis: The role of variable pancreatic reserve. PLoS ONE, 2019, https://doi.org/10.1371/journal.pone.0211331.

[57] González AA, Voos H, Darouach M. Glucose-Insulin System Based on Minimal Model: A Realistic Approach. 2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim), Cambridge, 2015, 55-60. doi: 10.1109/UKSim.2015.65.

[58] Grygoryan R.D. High sustained G-tolerance model development.STCU#P-078 EOARD# 01-8001 Agreement: Final Report, 2002.

[59] Grygoryan RD, Hargens AR. A virtual multicellular organism with homeostatic and adaptive properties. In: Adaptation Biology and Medicine: Health Potentials. Ed. L. Lukyanova, N.Takeda, P.K. Singal. – New Delhi: Narosa Publishing House,2008, 5:261 –282.

[60] White RJ, Leonard JI, Rummel JA, Leach CS. A systems approach to the physiology of weightlessness. Journal of medical systems, 1982, 6 (4), 343-358.

[61] Hensley DW1, Mark AE, Abella JR, Netscher GM, Wissler EH, Diller KR.

[62] years of computer simulation of the human thermoregulatory system. J Biomech Eng. 2013;135(2):021006. doi: 10.1115/1.4023383.

[63] Alzeer AH, Wissler EH.Theoretical analysis of evaporative cooling of classic heat stroke patients. Int J Biometeorol. 2018; 62(9):1567-1574. doi: 10.1007/s00484-018-1551-1.

[64] Wagar LE, DiFazio RM. Davis MM. Advanced model systems and tools for basic and translational human immunology. Genome Med. 2018. 10, 73. https://doi.org/10.1186/s13073-018-0584-8.

[65] Kerepesi C, Bakács T, Szabado, T. MiStImm: an agent-based simulation tool to study the self-nonself discrimination of the adaptive immune response. Theor Biol Med Model 2019,16, 9. https://doi.org/10.1186/s12976-019-0105-5.

[66] Amosov NM, Paleys BL, Agapov BT, Yermakova II, Lyabakh KG, Solovev AA. Theoretical Investigations of Human Physiology. 1977, Kiev, Naukova Duma.

[67] Shim EB, Leem CH, Abe Y, Noma A. A new multi-scale simulation model of the circulation: from cells to system. Philos Trans A Math Phys Eng Sci. 2006; 364(1843):1483-1500. https://doi.org/10.1098/rsta.2006.1782.

[68] Grygoryan RD. The Energy basis of reversible adaptation. N.Y.: Nova Science, 2012: 254 p. ISBN 978-1-62081-093-4.

[69] Grygoryan RD. The optimal circulation: cells contribution to arterial pressure. N.Y.: Nova Science, 2017: 287p. ISBN 978-1-53612-295-4.

[70] Trayanova NA, Winslow R. Whole-Heart Modeling. Applications to Cardiac Electrophysiology and Electromechanics. Circulation Research. 2011;108:113–128. https://doi.org/10.1161/CIRCRESAHA.110.223610.

[71] Noble D, Rudy Y. Models of cardiac ventricular action potentials: iterative interaction between experiment and simulation. Philos Trans R Soc Lond A. 2001; 359:1127–1142.

[72] Rice JJ, Wang F, Bers DM, de Tombe PP. Approximate model of cooperative activation and crossbridge cycling in cardiac muscle using ordinary differential equations. Biophys J. 2008; 95:2368–2390.

[73] Niederer SA, Hunter PJ, Smith NP. A quantitative analysis of cardiac myocyte relaxation: a simulation study. Biophys J. 2006; 90:1697–1722.

[74] Trayanova NA, Winslow R. Whole-Heart Modeling: Applications to Cardiac Electrophysiology and Electromechanics. Circulation Research. 2011;108:113–128. https://doi.org/10.1161/CIRCRESAHA.110.223610.

[75] Kerckhoffs RC, Neal ML, Gu Q, Bassingthwaighte JB, Omens JH, McCulloch AD. Coupling of a 3D finite element model of cardiac ventricular mechanics to lumped systems models of the systemic and pulmonic circulation. Ann Biomed Eng. 2007; 35:1–18. DOI:10.1007/s10439-006-9212-7.

[76] Walpole J, Papin JA, Peirce SM. Multiscale computational models of complex biological systems. Annu Rev Biomed Eng. 2013;15:137‐154. doi:10.1146/annurev-bioeng-071811-150104.

[77] Jie X, Gurev V, Trayanova N. Mechanisms of mechanically induced spontaneous arrhythmias in acute regional ischemia. Circ Res. 2010; 106:185–192. https://doi.org/10.1161/CIRCRESAHA.109.210864.

[78] A. Hosoi, T. Washio, J. Okada, Y. Kadooka, K. Nakajima and T. Hisada, "A Multi-Scale Heart Simulation on Massively Parallel Computers," SC '10: Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, New Orleans, LA, 2010, 1-11, doi: 10.1109/SC.2010.5.

[79] . Grygoryan R.D., Aksenova T.V., Degoda A.G. A simulator of mechanisms providing energy balance in human cells. Cybernetics and Computing Technologies. 2017, 2: 67–76. DOI: https://doi.org/10.15407/kvt188.02.065.


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