Issue 2 (188), article 4


Kibern. vyčisl. teh., 2017, Issue 2 (188), pp.

Grygoryan R.D., Dr (in biology),
Chief of Department of human systems modeling and reliability
Aksenova T.V., Junior-researcher
Degoda A.G., Senior-researcher, PhD (in math.)
Institute of software systems of National Аcademy of Sciences of Ukraine,
40, Acad.Glushkov ave., corp.5, Kiev, 052187, Ukraine


Introduction. Human organism is a too complex system to be empirically examined and comprehended: there is no method for simultaneously measuring or integrally analyzing of billions of multi-scale life variables. Experts need models and information technologies that causally incorporate cell-scale and organism-scale biophysical and physiological data.

The purpose of the article is to describe a specialized simulator (SS) providing users of additional information concerning scenarios and multi-level mechanisms of energy optimization in the human organism.

Results. Multi-scale mechanisms providing cell energy balance (CEB) are in the basis of SS. At cell-level, providers of CEB form a battery of autonomous mechanisms (BAM). Under energy lack (EL), BAM increases the rate of ATP synthesis using local opportunities. If EL remains, extracellular providers of aerobic synthesis of ATP enlarge current potentials of the EL-cell. The SS provides simulation scenarios increasing the lung ventilation, the erythropoiesis, blood concentrations of carbohydrates, and of other nutrients for biogenesis of mitochondria. The role of the cardiovascular system is in regulating of blood incomes to EL-cells.

Conclusion. The SS is a novel informational technology of dual goals. Explaining the function of mechanisms-providers of CEB, the SS also can be used by applied physiologists and physicians for the planning of optimal scenarios for physical empowering of healthy people.

Keywords: mathematical models, mitochondria, glucose, integrative physiology, medicine.

Download full text (ru)!


  1. Skulachev V.A.,Bogachev A.V., Kasparinsky F.O. Principles of Bioenergetics. Springer-Verlag, Berlin Heidelberg, 2013. 436 p.
  2. Michiels C. Physiological and Pathological Responses to Hypoxia. Am J Pathol. 2004. №164. P. 1875–1882.
  3. Kandel J., Angelin A.A., Wallace D.C. Mitochondrial respiration is sensitive to cytoarchitectural breakdown. Integr. Biol. (Camb). 2016, 8 (11). P. 1170–1182.
  4. Finsterer J. Hematological manifestations of primary mitochondrial disorders. Acta Haematol. 2007. 118 (2). P. 88–98.
  5. Mali V.R., pan G., Deshpande M. Cardiac Mitochondrial Respiratory Dysfunction and Tissue Damage in Chronic Hyperglycemia Correlate with Reduced Aldehyde Dehydrogenase-2 Activity. PLoS One. 2016.11 (10):e0163158.
  6. Grygoryan R.D. The Energy Basis of Reversible Adaptation. N.Y.: Nova Science, 2012. 252 p.
  7. Grygoryan R.D., Lyabakh K.G. Arterial pressure: a comprehension. Kyiv: ISS of National Academy of Sciences of Ukraine. 2015. 458 p. (In Russian).
  8. Grygoryan R.D. The “floating” arterial pressure paradigm: a concept of physiological supersystems. Düsseldorf: Palmarium Academic Publishing. 2016. 417 p. (In Russian).
  9. Grygoryan R.D., Deriev I.I., Aksionova T.V. A software simulator of aerobe cell’s responses to energy imbalance. Problems in programming. 2014. № 1. P. 90–98. (In Russian).
  10. Grygoryan R.D., Aksionova T.V., Markevich R.A. A software simulator of pancreas. Problems in programming. 2013. № 1. P. 100–106. (In Russian).
  11. Grygoryan R.D., Aksionova T.V., Degoda A.G. Modeling of mechanisms and hemodynamic effects of heart hypertrophy. Cybernetics and computer engineering. 2016. Issue.184. P. 72–83. (In Russian).
  12. Grygoryan R.D., Aksionova T.V. Modeling of organism-scale mechanisms fighting against energy shortage in cells. Bulletin of University “Ukraine”. Series: Informatics, computers, and cybernetics. 2016. P. 91–99. (In Russian).
  13. Aksionova T.V. A software technology providing simulations of mathematical models of physiological systems. Problems in programming. 2012. №1. P. 110–120. (In Russian).
  14. Chada S.R., Hollenbeck P.J. Nerve growth factor signaling regulates motility and docking of axonal mitochondria. Curr. Biol. 2004. V.14. P. 1272–1276.
  15. Ramamurthy S., Ronnett G. AMP-activated protein kinase (AMPK) and energy-sensing in the brain. Neurobiol. 2012. 21, № 2. P. 52–60.

Recieved 15.03.2017

Issue 184, article 6


KVT, 2016, Issue 184, pp.72-84

UDC 519.6+612


Grygoryan R.D., Aksenova T.V., Degoda A.G.

Institute of Software Systems of National Academy of Sciences of Ukraine, Kiev, Ukraine , ,

Introduction. A term cardiac hypertrophy (HH) refers to the phenomenon of increasing of the size of the heart, or its separate part. HH’s initiators can be both mechanisms adapting the entire organism to different modes of blood supply and the development of genetic abnormalities. The lack of a clear distinction in understanding mechanisms of these processes creates problems of diagnosis and treatment of pathological HH. In our opinion, a computer modeling of main hemodynamic effects of different forms of HH is able to shed light on mechanisms of HH and help in clarifying of this distinction.

The purpose of this article is to describe a mathematical model (MM) able to simulate basic hemodynamic effects of the adaptive and pathological forms of HH.

Results. MM describes the pumping function of the heart and vascular hemodynamics in a large and small circulations. MM also takes into account the mechanism of baroreflector regulation of the heart and blood vessels. The heart model quantitatively connects the mean values of flow, pressure and blood volume in each ventricle with its end-diastolic elasticity (C) and input blood pressure. The computer simulation showed that changes of central hemodynamics under left ventricle’s pathological hypertrophy can be satisfactorily reproduced via proper decrease of left ventricular parameter C. The adaptive HH is modeled by combining two procedures: i) growth of C of both ventricles of the heart; ii) adequate lowering of the tone of the cardiac sympathetic nerve.

Conclusions. Despite the MM satisfactorily simulates main effects of HH, its several links are still unclear thus the future advanced MM should include humoral, central neural regulator contours, as well as mechanisms providing energy balance both in cardiac myocites and at organism-scale.

Keywords: mathematical model, human cardiovascular system, computer simulation, adaptive hypertrophy, pathology.

Download full text (ru)!


  1. Debold E.P., Schmitt J.P., Patlak J.B. et. al. Hypertrophic and dilated cardiomyopathy mutations differentially affect the molecular force generation of mouse alpha-cardiac myosin in the laser trap assay. Am J Physiol Heart Circ Physiol, 2007, 293, pp.284–291.
  2. Lakdawala N.K., Givertz M.M. Dilated cardiomyopathy with conduction disease and arrhythmia. Circulation, 2010, 122, pp.527–534.
  3. Berry J.F., Naseemen R.H., Rothermel B.A. of cardiac hypertrophy and transition to heart failure. Drug Discovery Today: Disease Models, 2007, №4, pp. 197–206.
  4. Dellefave L., McNally E.M. The genetics of dilated cardiomyopathy. Curr. Opin. Cardiol., 2010, 25, pp.198–204.
  5. Moybenko A.A., Dosenko V.E., Parkhomenko A.N. Endogeneous mechanisms of cardiac protection as a basis for therapy of cardiac diseases. Kiev: Naukova Dumka, 2008, 520 p.
  6. Rhian T.M. New insights into mechanisms of hypertension // Current Opinion in Nephrology & Hypertension, 2012, 21, Iss. 2, pp. 119–121.
  7. Grygoryan R.D. The Energy basis of reversible adaptation. N.Y.: Nova Science, 2012, 254 p.
  8. Grygoryan R.D. The energy concept of arterial pressure. Reports of the National Academy of Sciences of Ukraine, 2011, №7, pp. 148–155.
  9. Grygoryan R.D. An individual physiological norm: the concept and problems. Reports of the National Academy of Sciences of Ukraine, 2013, №8, pp. 156–162.
  10. Grygoryan R.D. The “floating” arterial pressure paradigm. Düsseldorf, Germany. Palmarium Academic Publishing, 2016, 417 p.

Received 12.02.2016