Optimization of the parameters of microcirculatory structural adaptation model based on improved quantum-behaved particle swarm optimization algorithm_Journal of Biomedical Engineering

Authors：

PAN Qing ¹ , YAO Jialiang ¹ , WANG Ruofan ² , CAO Ping ³ , NING Gangmin ² ,  FANG Luping ¹

1. College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, P.R.China;
2. Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, P.R.China;
3. Zhijiang College, Zhejiang University of Technology, Shaoxing, Zhejiang 312030, P.R.China;

Corresponding author：

FANG Luping, Email: flp@zjut.edu.cn

Keywords：

microcirculation; structural adaptation; quantum-behaved particle swarm optimization

DOI：

10.7507/1001-5515.201607030

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The vessels in the microcirculation keep adjusting their structure to meet the functional requirements of the different tissues. A previously developed theoretical model can reproduce the process of vascular structural adaptation to help the study of the microcirculatory physiology. However, until now, such model lacks the appropriate methods for its parameter settings with subsequent limitation of further applications. This study proposed an improved quantum-behaved particle swarm optimization (QPSO) algorithm for setting the parameter values in this model. The optimization was performed on a real mesenteric microvascular network of rat. The results showed that the improved QPSO was superior to the standard particle swarm optimization, the standard QPSO and the previously reported Downhill algorithm. We conclude that the improved QPSO leads to a better agreement between mathematical simulation and animal experiment, rendering the model more reliable in future physiological studies.

Citation： PAN Qing, YAO Jialiang, WANG Ruofan, CAO Ping, NING Gangmin, FANG Luping. Optimization of the parameters of microcirculatory structural adaptation model based on improved quantum-behaved particle swarm optimization algorithm. Journal of Biomedical Engineering, 2017, 34(5): 784-789. doi: 10.7507/1001-5515.201607030 Copy

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2.	Levy B I, Ambrosio G, Pries A R, et al. Microcirculation in hypertension: a new target for treatment? Circulation, 2001, 104(6): 735-740.
3.	Struijker-Boudier H A J, Rosei A E, Bruneval P, et al. Evaluation of the microcirculation in hypertension and cardiovascular disease. Eur Heart J, 2007, 28(23): 2834-2840.
4.	Pries A R, Habazettl H, Ambrosio G, et al. A review of methods for assessment of coronary microvascular disease in both clinical and experimental settings. Cardiovasc Res, 2008, 80(2): 165-174.
5.	Ganesan P, He S, Xu H. Modelling of pulsatile blood flow in arterial trees of retinal vasculature. Med Eng Phys, 2011, 33(7): 810-823.
6.	Pan Q, Wang R F, Reglin B, et al. A one-dimensional mathematical model for studying the pulsatile flow in microvascular networks. J Biomech Eng-Trans ASME, 2014, 136(1): 011009.
7.	Carlson B E, Arciero J C, Secomb T W. Theoretical model of blood flow autoregulation: roles of myogenic, shear-dependent, and metabolic responses. Am J Physiol Heart Circ Physiol, 2008, 295(4): H1572-H1579.
8.	Cornelissen A J M, Dankelman J, Vanbavel E, et al. Balance between myogenic, flow-dependent, and metabolic flow control in coronary arterial tree: a model study. Am J Physiol Heart Circ Physiol, 2002, 282(6): H2224-H2237.
9.	Pries A R, Reglin B, Secomb T W. Structural adaptation of microvascular networks: functional roles of adaptive responses. Am J Physiol Heart Circ Physiol, 2001, 281(3): H1015-H1025.
10.	Pries A R, Höpfner M, le Noble F, et al. The shunt problem: control of functional shunting in normal and tumour vasculature. Nat Rev Cancer, 2010, 10(8): 587-593.
11.	Vorderwülbecke B J, Maroski J, Fiedorowicz K, et al. Regulation of endothelial connexin40 expression by shear stress. Am J Physiol Heart Circ Physiol, 2012, 302(1): H143-H152.
12.	Sun J, Fang W, Wu X, et al. Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection. Evol Comput, 2012, 20(3): 349-393.
13.	Pries A R, Secomb T W, Gessner T, et al. Resistance to blood flow in microvessels in vivo. Circ Res, 1994, 75(5): 904-915.
14.	Pan Q, Wang R, Reglin B, et al. Simulation of microcirculatory hemodynamics: estimation of boundary condition using particle swarm optimization. Bio-Med Mater Eng, 2014, 24(6): 2341-2347.
15.	Pries A R, Secomb T W. Microvascular blood viscosity in vivo and the endothelial surface layer. Am J Physiol Heart Circ Physiol, 2005, 289(6): H2657-H2664.
16.	Pries A R, Secomb T W, Gaehtgens P. Design principles of vascular beds. Circ Res, 1995, 77(5): 1017-1023.
17.	Meens M J, Pfenniger A, Kwak B R, et al. Regulation of cardiovascular connexins by mechanical forces and junctions. Cardiovasc Res, 2013, 99(2): 304-314.

1. Risau W. Mechanisms of angiogenesis. Nature, 1997, 386(6626): 671-674.
2. Levy B I, Ambrosio G, Pries A R, et al. Microcirculation in hypertension: a new target for treatment? Circulation, 2001, 104(6): 735-740.
3. Struijker-Boudier H A J, Rosei A E, Bruneval P, et al. Evaluation of the microcirculation in hypertension and cardiovascular disease. Eur Heart J, 2007, 28(23): 2834-2840.
4. Pries A R, Habazettl H, Ambrosio G, et al. A review of methods for assessment of coronary microvascular disease in both clinical and experimental settings. Cardiovasc Res, 2008, 80(2): 165-174.
5. Ganesan P, He S, Xu H. Modelling of pulsatile blood flow in arterial trees of retinal vasculature. Med Eng Phys, 2011, 33(7): 810-823.
6. Pan Q, Wang R F, Reglin B, et al. A one-dimensional mathematical model for studying the pulsatile flow in microvascular networks. J Biomech Eng-Trans ASME, 2014, 136(1): 011009.
7. Carlson B E, Arciero J C, Secomb T W. Theoretical model of blood flow autoregulation: roles of myogenic, shear-dependent, and metabolic responses. Am J Physiol Heart Circ Physiol, 2008, 295(4): H1572-H1579.
8. Cornelissen A J M, Dankelman J, Vanbavel E, et al. Balance between myogenic, flow-dependent, and metabolic flow control in coronary arterial tree: a model study. Am J Physiol Heart Circ Physiol, 2002, 282(6): H2224-H2237.
9. Pries A R, Reglin B, Secomb T W. Structural adaptation of microvascular networks: functional roles of adaptive responses. Am J Physiol Heart Circ Physiol, 2001, 281(3): H1015-H1025.
10. Pries A R, Höpfner M, le Noble F, et al. The shunt problem: control of functional shunting in normal and tumour vasculature. Nat Rev Cancer, 2010, 10(8): 587-593.
11. Vorderwülbecke B J, Maroski J, Fiedorowicz K, et al. Regulation of endothelial connexin40 expression by shear stress. Am J Physiol Heart Circ Physiol, 2012, 302(1): H143-H152.
12. Sun J, Fang W, Wu X, et al. Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection. Evol Comput, 2012, 20(3): 349-393.
13. Pries A R, Secomb T W, Gessner T, et al. Resistance to blood flow in microvessels in vivo. Circ Res, 1994, 75(5): 904-915.
14. Pan Q, Wang R, Reglin B, et al. Simulation of microcirculatory hemodynamics: estimation of boundary condition using particle swarm optimization. Bio-Med Mater Eng, 2014, 24(6): 2341-2347.
15. Pries A R, Secomb T W. Microvascular blood viscosity in vivo and the endothelial surface layer. Am J Physiol Heart Circ Physiol, 2005, 289(6): H2657-H2664.
16. Pries A R, Secomb T W, Gaehtgens P. Design principles of vascular beds. Circ Res, 1995, 77(5): 1017-1023.
17. Meens M J, Pfenniger A, Kwak B R, et al. Regulation of cardiovascular connexins by mechanical forces and junctions. Cardiovasc Res, 2013, 99(2): 304-314.

Journal of Biomedical Engineering

Optimization of the parameters of microcirculatory structural adaptation model based on improved quantum-behaved particle swarm optimization algorithm

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