Dynamical characteristics of microvascular networks with a myogenic response gradient
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1.
Neganova A. Dynamical characteristics of microvascular networks with a myogenic response gradient. MAIO [Internet]. 2017 Jul. 7 [cited 2024 Dec. 22];1(4):43-61. Available from: https://www.maio-journal.com/index.php/MAIO/article/view/45

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Keywords

mathematical modeling; myogenic response; myogenic response gradient; oscillations; vascular network

Abstract

Purpose: Myogenic response is the ability of smooth muscle cells lining the vascu-lar wall to react to changing intravascular pressure: increasing pressure normallyinduces contraction whereas decreasing pressure leads to dilatation. Experimentalstudies show that the intensity of the myogenic response is dierent in arteriolarvessels of dierent radii: smaller arterioles react relatively more intensely, but overa more narrow range of pressures, than larger arterioles. In a network of vessels,this gives rise to a myogenic response gradient. The physiological significance of this gradient is, nonetheless, debated. Our purpose is to investigate the dynamical characteristics of microvascular networks with a myogenic response gradient by means of mathematical modeling.Methods: We present a mathematical vascular network model which includes a de-tailed description of vessel wall mechanics and the myogenic response gradient. Wefocus on the influence of this gradient on short-term network dynamics. We performa series of numerical simulations in both symmetrical and asymmetrical vasculartrees in which the individual vessel is given a realistic morphology, i.e., relative wallthickness is smaller in larger vessels.Results: Our main findings show that the presence of a myogenic response gradient:1. adjusts flow and pressure in the capillary bed to an adequate level and dampensoscillations transmitted from upstream feeding vessels;2. provides the network as a whole with a basal level of tone necessary for theoperation of vasomotor mechanisms other than the myogenic response; and3. provides the system with the overall ability to autoregulate network flowsmoothly.Conclusion: The mathematical model shows that networks with a myogenic response gradient present advantages regarding the physiological function of regulating flow in a bifurcating network compared to networks without myogenic response and passive networks
https://doi.org/10.35119/maio.v1i4.45
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References

W. Bayliss, “On the local reactions of the arterial wall to changes of internal pressure,” J Physiol, 1902.

G. Osol, J. Brekke, K. McElroy-Yaggy, and N. Gokina, “Myogenic tone, reactivity, and forced dilatation: a three-phase model of in vitro arterial myogenic behavior,” Am J Physiol Heart Circ Physiol, 2002.

P. Clifford, “Local control of blood flow,” Adv Physiol Educ, 2011.

M. Davis and M. Hill, “Signaling mechanisms underlying the vascular myogenic response,” Physiol Revl, 1999.

M. Davis, “Myogenic response gradient in an arteriolar network,” Am. J. Physiol, 1993.

P. Jeppesen, J. Sanye-Hajari, and T. Bek, “Increased blood pressure induces a diameter response of retinal arterioles that increases with decreasing arteriolar diameter,” Invest Ophthalmol Vis Sci, 2007.

R. Schubert and M. Mulvany, “The myogenic response: established facts and attractive hypotheses,” Clin Sci (Lond), 1999.

J. Yang, J. C. Jr., R. Bryan, and C. Robertson, “The myogenic response in isolated rat cerebrovascular arteries: smooth muscle cell model,” Med Eng Phys, 2003.

J. Yang, J. C. Jr., R. Bryan, and C. Robertson, “The myogenic response in isolated rat cerebrovascular arteries: vessel model,” Med Eng Phys, 2003.

B. Carlson and T. Secomb, “A theoretical model for the myogenic response based on the length-tension characteristics of vascular smooth muscle,” Microcirculation, 2005.

J. Jacobsen, M. Mulvany, and N. Holstein-Rathlou, “A mechanism for arteriolar remodeling based on maintenance of smooth muscle cell activation,” Am J Physiol Regul Integr Comp Physiol, 2008.

A. Cornelissen, J. Dankelman, E. VanBavel, H. Stassen, and J. Spaan, “Myogenic reactivity and resistance distribution in the coronary arterial tree: a model study,” Am J Physiol Heart Circ Physiol, 2000.

W. Hacking, E. VanBavel, and J. Spaan, “Shear stress is not sufficient to control growth of vascular networks: a model study,” Am J Physiol, 1996.

J. B. Jacobsen, F. Gustafsson, and N.-H. Holstein-Rathlou, “A model of physical factors in the structural adaptation of microvascular networks in normotension and hypertension,” Physiol Meas, 2003.

G. Guidoboni, A. Harris, S. Cassani, J. Arciero, B. Siesky, A. Amireskandari, L. Tobe, P. Egan, I. Januleviciene, and J. Park, “Intraocular pressure, blood pressure, and retinal blood flow autoregulation: a mathematical model to clarify their relationship

and clinical relevance,” Invest Ophthalmol Vis Sci, 2014.

D. Postnov, D. Postnov, D. Marsh, N. Holstein-Rathlou, and O. Sosnovtseva, “Dynamics of nephron-vascular network,” Bull Math Biol, 2012.

J. B. Jacobsen, M. Hornbech, and N.-H. Holstein-Rathlou, “Significance of microvascular remodelling for the vascular flow reserve in hypertension,” Interface Focus, 2011.

A. Pries, T. Secomb, and P. Gaehtgens, “Structural adaptation and stability of microvascular networks: theory and simulations,” Am J Physiol, 1998.

A. Pries, A. Cornelissen, A. Sloot, M. Hinkeldey, M. Dreher, M. Hopfner, M. Dewhirst, and T. Secomb, “Structural adaptation and heterogeneity of normal and tumor microvascular networks,” PLoS Comput Biol, 2009.

B. Fry, T. Roy, and T. Secomb, “Capillary recruitment in a theoretical model for blood flow regulation in heterogeneous microvessel networks,” Physiol Rep, 2013.

E. VanBavel and B. Tuna, “Integrative modeling of small artery structure and function uncovers critical parameters for diameter regulation,” PLoS One, 2014.

R. Feldberg, M. Colding-Jørgensen, and N. Holstein-Rathlou, “Analysis of interaction between tgf and the myogenic response in renal blood flow autoregulation,” Am J Physiol, 1995.

L. Kuo, F. Arko, W. Chilian, and M. Davis, “Coronary venular responses to flow and pressure,” Circ Res, 1993.

G. Dornyei, E. Monos, G. Kaley, and A. Koller, “Myogenic responses of isolated rat skeletal muscle venules: modulation by norepinephrine and endothelium,” Am J Physiol, 1996.

C. Hall, C. Reynell, B. Gesslein, N. Hamilton, A. Mishra, B. Sutherland, F. O’Farrell, A. Buchan, M. Lauritzen, and D. Attwell, “Capillary pericytes regulate cerebral blood flow in health and disease,” Nature, 2014.

S. Schroder, M. Brab, G. W. Schmid-Schonbein, M. Reim, and H. Schmid-Schonbein, “Microvascular network topology of the human retinal vessels,” Fortschr Ophthalmol, 1990.

D. D. Postnov, D. J. Marsh, D. E. Postnov, T. H. Braunstein, N.-H. Holstein-Rathlou, E. A. Martens, and O. Sosnovtseva, “Modeling of kidney hemodynamics: Probabilitybased topology of an arterial network,” PLoS Comput. Biol, 2016 (accepted).

L. Sosula, “Capillary radius and wall thickness in normal and diabetic rat retinae,” Microvasc Res, 1974.

R. W. Gore, “Pressures in cat mesenteric arterioles and capillaries during changes in systemic arterial blood pressure,” Circ Res, 1974.

O. Frank, “The basic shape of the arterial pulse. first treatise: mathematical analysis. 1899.,” J Mol Cell Cardiol, 1990.

D. Marsh, O. Sosnovtseva, E. Mosekilde, and N.-H. Holstein-Rathlou, “Vascular coupling induces synchronization, quasiperiodicity, and chaos in a nephron tree,” Chaos, 2007.

Numerical recipes in C (2nd ed.): the art of scientific computing, ch. Power Spectrum Estimation Using the FFT. Cambridge University Press New York, 1992.

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