Модульное моделирование сердечно-сосудистой системы человека

Математическая биология и биоинформатика. 2012;7(2):703-736.

doi: 10.17537/2012.7.703.

1. Solodiannikov IuV. Elementy matematicheskogo modelirovaniia i identifikatsiia sistemy krovoobrashcheniia (Elements of mathematical modeling and the blood circulation system identification). Samara; 1994. P. 316 (in Russ.).
2. Karaaslan F, Denizhan Y, Kayserilioglu A, Ozcan Gulcur H. Long-term mathematical model involving renal sympathetic nerve activity, arterial pressure, and sodium excretion. Ann. Biomed. Eng. 2005;33(11):1607-1630.
3. Guyton AC, Coleman TG, Granger HJ. Circulation: Overall regulation. Ann. Rev. Physiol. 1972;34:13-46. doi: 10.1146/annurev.ph.34.030172.000305
4. Coleman TG, Hall JE. In: A mathematical model of renal hemodynamics and excretory function Structuring Biological Systems: A Computer Modelling Approach. Ed. Iyengar SS. Boca Raton, Florida: CRC Press; 1992. P. 89-124.
5. Uttamsingh RJ, Leaning MS, Bushman JA, Carson ER, Finkelstein L. Mathematical model of the human renal system. Med. Biol. Eng. Comput. 1985;23:525-535.
6. Lamponi D. One dimensional and multiscale model for blood flow circulation: Ph. D. Thesis No 3006. Lausanne: Ecole Polytechnique Federale De Lausanne; 2004. http://infoscience.epfl.ch/record/33478/files/EPFL_TH3006.pdf  (accessed 19 December 2012).
7. Holstein-Rathlou NH, Marsh DJ. A dynamic model of renal blood flow autoregulation. Bull. Math. Biol. 1994;56(3):411-429.
8. Judas L, Bentzen SM, Stewart FA. Progression rate of radiation damage to the mouse kidney: a quantitative analysis of experimental data using a simple mathematical model of the nephron. Int. J. Radiat. Biol. 1997;72(4):461-473.
9. Proshin AP, Solodyannikov YV. Mathematical modeling of blood circulation system and its practical application. Automat. Rem. Contr. 2006;67(2):329-341.
10. Snoep JL, Bruggeman F, Olivier BG, Westerhoff HV. Towards building the silicon cell: a modular approach. Biosystems. 2006;83(2-3):207-16. doi: 10.1016/j.biosystems.2005.07.006
11. Randhawa R, Shaffer CA, Tyson JJ. Model Composition for Macromolecular Regulatory Networks. IEEE/ACM Trans. Comput. Biol. Bioinform. 2010;7(2):278-287.
12. Hernández AI, Le Rolle V, Defontaine A, Carrault G. A Multiformalism and Multiresolution Modelling Environment: Application to the cardiovascular system and its regulation. Philos Transact A Math Phys Eng Sci. 2009;367(1908):4923-4940. doi: 10.1098/rsta.2009.0163
13. Fenner JW, Brook B, Clapworthy G, Coveney PV, Feipel V, Gregersen H, Hose DR, Kohl P, Lawford P, McCormack KM, Pinney D, Thomas SR, Van Sint Jan S, Waters S, Viceconti M. The EuroPhysiome, STEP and a roadmap for the virtual physiological human. Phil. Trans. R. Soc. 2008;366(1878):2979-2999.
14. Perez L, Dragicevic S. An agent-based approach for modeling dynamics of contagious disease spread. Int. J. Health Geogr. 2009;8(1):50. doi: 10.1186/1476-072X-8-50
15. Chany NT, LeBaronz B, Loyy AW, Poggiozz T. Agent-Based Models of Financial Markets: A Comparison with Experimental Markets. Working Paper. 1999.
16. Glenn Elert. Volume of Blood in a Human. The Physics Factbook™. http://hypertextbook.com/facts/1998/LanNaLee.shtml (accessed 01 March 2012).
17. Meditsinskaia entsiklopediia (Medical Enc.ru). http://www.medical-enc.ru/m/15/funktsionalnye-metody-issledovania-pochek-3.shtml  (accessed 01 March 2012) (in Russ.).
18. Navar LG, Hamm LL. The kidney in blood pressure regulation. In: Atlas of Diseases of the Kidney. Hypertension and the Kidney. Ed. Wilcox CS. Philadelphia: Current Medicine; 1999. P. 1.1–1.22.
19. Bendel P, Buonocore E, Bockisch A, Besozzi MC. Blood flow in the carotid arteries: quantification by using phase-sensitive MR imaging. Am. J. Roentgenol. 1989;152(6):1307–1310. doi: 10.2214/ajr.152.6.1307