We propose a new model for the emergence of blood capillary networks. We assimilate the tissue and extra cellular matrix as a porous medium, using Darcy's law for describing both blood and interstitial fluid flows. Oxygen obeys a convection-diffusion-reaction equation describing advection by the blood, diffusion and consumption by the tissue. Discrete agents named capillary elements and modelling groups of endothelial cells are created or deleted according to different rules involving the oxygen concentration gradient, the blood velocity, the sheer stress or the capillary element density. Once created, a capillary element locally enhances the hydraulic conductivity matrix, contributing to a local increase of the blood velocity and oxygen flow. No connectivity between the capillary elements is imposed. The coupling between blood, oxygen flow and capillary elements provides a positive feedback mechanism which triggers the emergence of a network of channels of high hydraulic conductivity which we identify as new blood capillaries. We provide two different, biologically relevant geometrical settings and numerically analyze the influence of each of the capillary creation mechanism in detail. All mechanisms seem to concur towards a harmonious network but the most important ones are those involving oxygen gradient and sheer stress. A detailed discussion of this model with respect to the literature and its potential future developments concludes the paper.
Citation: Pedro Aceves-Sanchez, Benjamin Aymard, Diane Peurichard, Pol Kennel, Anne Lorsignol, Franck Plouraboué, Louis Casteilla, Pierre Degond. A new model for the emergence of blood capillary networks[J]. Networks and Heterogeneous Media, 2021, 16(1): 91-138. doi: 10.3934/nhm.2021001
We propose a new model for the emergence of blood capillary networks. We assimilate the tissue and extra cellular matrix as a porous medium, using Darcy's law for describing both blood and interstitial fluid flows. Oxygen obeys a convection-diffusion-reaction equation describing advection by the blood, diffusion and consumption by the tissue. Discrete agents named capillary elements and modelling groups of endothelial cells are created or deleted according to different rules involving the oxygen concentration gradient, the blood velocity, the sheer stress or the capillary element density. Once created, a capillary element locally enhances the hydraulic conductivity matrix, contributing to a local increase of the blood velocity and oxygen flow. No connectivity between the capillary elements is imposed. The coupling between blood, oxygen flow and capillary elements provides a positive feedback mechanism which triggers the emergence of a network of channels of high hydraulic conductivity which we identify as new blood capillaries. We provide two different, biologically relevant geometrical settings and numerically analyze the influence of each of the capillary creation mechanism in detail. All mechanisms seem to concur towards a harmonious network but the most important ones are those involving oxygen gradient and sheer stress. A detailed discussion of this model with respect to the literature and its potential future developments concludes the paper.
[1] | G. Albi, M. Burger, J. Haskovec, P. Markowich and M. Schlottbom, Continuum modeling of biological network formation, in Active Particles, Springer, 1 (2017), 1-48. doi: 10.1007/978-3-319-49996-3_1 |
[2] | C. Amitrano, A. Coniglio and F. Di Liberto, Growth probability distribution in kinetic aggregation processes, Phys. Rev. Lett., 57 (1986), 1016. doi: 10.1103/PhysRevLett.57.1016 |
[3] | A mathematical model of tumour-induced capillary growth. J. Theoret. Biol. (1985) 114: 53-73. |
[4] | Stability and spectral convergence of fourier method for nonlinear problems: On the shortcomings of the $2/3$ de-aliasing method. Numer. Math. (2015) 129: 749-782. |
[5] | A. L. Bauer, T. L. Jackson and Y. Jiang, Topography of extracellular matrix mediates vascular morphogenesis and migration speeds in angiogenesis, PLoS Computational Biology, 5 (2009), e1000445, 18pp. doi: 10.1371/journal.pcbi.1000445 |
[6] | Trail formation based on directed pheromone deposition. J. Math. Biol. (2013) 66: 1267-1301. |
[7] | S. C. Brenner and R. L. Scott, The Mathematical Theory of Finite Element Methods, vol. 15, Springer Science & Business Media, 2008. doi: 10.1007/978-0-387-75934-0 |
[8] | T. Büscher, A. L. Diez, G. Gompper and J. Elgeti, Instability and fingering of interfaces in growing tissue, New J. Phys., 22 (2020), 083005, 11pp. doi: 10.1088/1367-2630/ab9e88 |
[9] | Mathematical models for tumour angiogenesis: Numerical simulations and nonlinear wave solutions. Bull. Math. Biol. (1995) 57: 461-486. |
[10] | Angiogenesis in cancer and other diseases. Nature (2000) 407: 249-257. |
[11] | On the equations of landscape formation. Interfaces Free Bound. (2014) 16: 105-136. |
[12] | Landscape evolution models: A review of their fundamental equations. Geomorphology (2014) 219: 68-86. |
[13] | Kinetics of oxygen uptake by cells potentially used in a tissue engineered trachea. Biomaterials (2014) 35: 6829-6837. |
[14] | G. Dahlquist and Å. Björck, Numerical Methods in Scientific Computing, Volume i, Society for Industrial and Applied Mathematics, 2008. doi: 10.1137/1.9780898717785 |
[15] | A cell-based model of extracellular-matrix-guided endothelial cell migration during angiogenesis. Bull. Math. Biol. (2013) 75: 1377-1399. |
[16] | The weighted particle method for convection-diffusion equations, i, the case of an isotropic viscosity. Mathematics of computation (1989) 53: 485-507. |
[17] | A deterministic approximation of diffusion equations using particles. SIAM J. Sci. Stat. Comput. (1990) 11: 293-310. |
[18] | Y. Efendiev and T. Y. Hou, Multiscale finite element methods: Theory and applications, vol. 4, Springer Science & Business Media, 2009. doi: 10.1007/978-0-387-09496-0 |
[19] | Angiogenesis in gliomas: Biology and molecular pathophysiology. Brain pathology (2005) 15: 297-310. |
[20] | Angiogenesis in cancer, vascular, rheumatoid and other disease. Nature Medicine (1995) 1: 27-30. |
[21] | (2017) Basic Transport Phenomena in Biomedical Engineering.CRC press. |
[22] | Fluid shear stress threshold regulates angiogenic sprouting. Proc. Natl. Acad. Sci. USA (2014) 111: 7968-7973. |
[23] | B. Garipcan, S. Maenz, T. Pham, U. Settmacher, K. D. Jandt, J. Zanow and J. Bossert, Image analysis of endothelial microstructure and endothelial cell dimensions of human arteries-a preliminary study, Advanced Engineering Materials, 13 (2011), B54-B57. doi: 10.1002/adem.201080076 |
[24] | Tumor growth and neovascularization: An experimental model using the rabbit cornea. Journal of the National Cancer Institute (1974) 52: 413-427. |
[25] | M. S. Gockenbach, Understanding and Implementing The Finite Element Method, Vol. 97, SIAM, 2006. doi: 10.1137/1.9780898717846 |
[26] | A computational study of the effect of capillary network anastomoses and tortuosity on oxygen transport. J. Theoret. Biol. (2000) 206: 181-194. |
[27] | Spatio-temporal point process statistics: A review. Spat. Stat. (2016) 18: 505-544. |
[28] | J. A. Grogan, A. J. Connor, J. M. Pitt-Francis, P. K. Maini and H. M. Byrne, The importance of geometry in the corneal micropocket angiogenesis assay, PLoS Computational Biology, 14 (2018), e1006049. doi: 10.1371/journal.pcbi.1006049 |
[29] | J. Haskovec, L. M. Kreusser and P. Markowich, Rigorous continuum limit for the discrete network formation problem, Comm. Partial Differential Equations, 44 (2019), 1159-1185. doi: 10.1080/03605302.2019.1612909 |
[30] | Mathematical analysis of a pde system for biological network formation. Comm. Partial Differential Equations (2015) 40: 918-956. |
[31] | Notes on a pde system for biological network formation. Nonlinear Anal. (2016) 138: 127-155. |
[32] | Laplacian growth as one-dimensional turbulence. Phys. D (1998) 116: 244-252. |
[33] | Geometrical cluster growth models and kinetic gelation. Physics Reports (1986) 136: 153-224. |
[34] | M 5 mesoscopic and macroscopic models for mesenchymal motion. J. Math. Biol. (2006) 53: 585-616. |
[35] | D. Hu and D. Cai, Adaptation and optimization of biological transport networks, Phys. Rev. Lett., 111 (2013), 138701. doi: 10.1103/PhysRevLett.111.138701 |
[36] | Effects of shear stress on wound-healing angiogenesis in the rabbit ear chamber. Journal of Surgical Research (1997) 72: 29-35. |
[37] | H. Kang, K. J. Bayless and R. Kaunas, Fluid shear stress modulates endothelial cell invasion into three-dimensional collagen matrices, American Journal of Physiology-Heart and Circulatory Physiology, 295 (2008), H2087-H2097. doi: 10.1152/ajpheart.00281.2008 |
[38] | Synergistic regulation of angiogenic sprouting by biochemical factors and wall shear stress. Cellular And Molecular Bioengineering (2011) 4: 547-559. |
[39] | Hypoxia and the hypoxia-inducible-factor pathway in glioma growth and angiogenesis. Neuro-Oncology (2005) 7: 134-153. |
[40] | Direct implicit large time-step particle simulation of plasmas. J. Comput. Phys. (1983) 51: 107-138. |
[41] | Multiscale modelling and nonlinear simulation of vascular tumour growth. Journal of Mathematical Biology (2009) 58: 765-798. |
[42] | Particle approximation of a linear convection-diffusion problem with neumann boundary conditions. SIAM Journal on Numerical Analysis (1995) 32: 1098-1125. |
[43] | Mersenne twister: A 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Transactions on Modeling and Computer Simulation (TOMACS) (1998) 8: 3-30. |
[44] | Mathematical modelling of dynamic adaptive tumour-induced angiogenesis: Clinical implications and therapeutic targeting strategies. J. Theoret. Biol. (2006) 241: 564-589. |
[45] | A model for vein formation in higher plants. Proc. R. Soc. Lond. B (1980) 207: 79-109. |
[46] | The polar transport of auxin and vein patterns in plants. Phil. Trans. R. Soc. Lond. B (1981) 295: 461-471. |
[47] | Smoothed particle hydrodynamics. Annual Review of Astronomy and Astrophysics (1992) 30: 543-574. |
[48] | M. Müller, D. Charypar and M. Gross, Particle-based fluid simulation for interactive applications, in Proceedings of The 2003 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Eurographics Association, (2003), 154-159. |
[49] | W. L. Murfee, Implications of fluid shear stress in capillary sprouting during adult microvascular network remodeling, Mechanobiology of the Endothelium, (2015), 166. |
[50] | The physiological principle of minimum work: I. the vascular system and the cost of blood volume. Proc. Natl. Acad. Sci. USA (1926) 12: 207-214. |
[51] | Tumor-induced neovascularization in the mouse eye. Journal of the National Cancer Institute (1982) 69: 699-708. |
[52] | Viscous fingering: An optimal bound on the growth rate of the mixing zone. SIAM Journal on Applied Mathematics (1997) 57: 982-990. |
[53] | Angiogenesis and vascular remodelling in normal and cancerous tissues. J. Math. Biol. (2009) 58: 689-721. |
[54] | Modelling cell migration strategies in the extracellular matrix. J. Math. Biol. (2009) 58: 511-543. |
[55] | First steps of tumor-related angiogenesis, laboratory investigation. A Journal of Technical Methods and Pathology (1991) 65: 334-346. |
[56] | J. Y. Park, J. B. White, N. Walker, C.-H. Kuo, W. Cha, M. E. Meyerhoff and S. Takayama, Responses of endothelial cells to extremely slow flows, Biomicrofluidics, 5 (2011), 022211. doi: 10.1063/1.3576932 |
[57] | Tumor-related angiogenesis. Critical Reviews in Oncology Hematology (1989) 9: 197-242. |
[58] | Multiscale homogenization for fluid and drug transport in vascularized malignant tissues. Math. Models Methods Appl. Sci. (2015) 25: 79-108. |
[59] | H. Perfahl, H. M. Byrne, T. Chen, V. Estrella, T. Alarcón, A. Lapin, R. A. Gatenby, R. J. Gillies, M. C. Lloyd, P. K. Maini, et al., Multiscale modelling of vascular tumour growth in 3d: The roles of domain size and boundary conditions, PloS One, 6 (2011), e14790. doi: 10.1371/journal.pone.0014790 |
[60] | Simple mechanical cues could explain adipose tissue morphology. J. Theoret. Biol. (2017) 429: 61-81. |
[61] | Angiogenesis: A team effort coordinated by notch. Developmental cell (2009) 16: 196-208. |
[62] | Stochastic model for dielectric breakdown. J. Stat. Phys. (1984) 36: 909-916. |
[63] | S. Pillay, H. M. Byrne and P. K. Maini, Modeling angiogenesis: A discrete to continuum description, Phys. Rev. E, 95 (2017), 012410, 12pp. doi: 10.1103/physreve.95.012410 |
[64] | A. Pries, T. Secomb and P. Gaehtgens, Structural adaptation and stability of microvascular networks: Theory and simulations, American Journal of Physiology-Heart and Circulatory Physiology, 275 (1998), H349-H360. doi: 10.1152/ajpheart.1998.275.2.H349 |
[65] | Resistance to blood flow in microvessels in vivo. Circulation Research (1994) 75: 904-915. |
[66] | P.-A. Raviart, An analysis of particle methods, in Numerical Methods in Fluid Dynamics, Springer, 1127 (1985), 243-324. doi: 10.1007/BFb0074532 |
[67] | Mechanisms of angiogenesis. Nature (1997) 386: 671-674. |
[68] | Reviewing models of auxin canalization in the context of leaf vein pattern formation in arabidopsis. The Plant Journal (2005) 44: 854-865. |
[69] | The penetration of a fluid into a porous medium or hele-shaw cell containing a more viscous liquid. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences (1958) 245: 312-329. |
[70] | Tissue metabolism driven arterial tree generation. Medical Image Analysis (2012) 16: 1397-1414. |
[71] | A review of mathematical models for the formation of vascular networks. J. Theoret. Biol. (2013) 333: 174-209. |
[72] | T. W. Secomb, J. P. Alberding, R. Hsu, M. W. Dewhirst and A. R. Pries, Angiogenesis: An adaptive dynamic biological patterning problem, PLoS Computational Biology, 9 (2013), e1002983, 12pp. doi: 10.1371/journal.pcbi.1002983 |
[73] | The role of mechanical stresses in microvascular remodeling. Microcirculation (1996) 3: 143-165. |
[74] | Interstitial flow and its effects in soft tissues. Annu. Rev. Biomed. Eng. (2007) 9: 229-256. |
[75] | Oxygen consumption rate of tissue measured by a micropolarographic method. The Journal of general physiology (1966) 50: 317-335. |
[76] | L. Tang, A. L. van de Ven, D. Guo, V. Andasari, V. Cristini, K. C. Li and X. Zhou, Computational modeling of 3d tumor growth and angiogenesis for chemotherapy evaluation, PloS One, 9 (2014), e83962. doi: 10.1371/journal.pone.0083962 |
[77] | R. D. Travasso, E. C. Poiré, M. Castro, J. C. Rodrguez-Manzaneque, and A. Hernández-Machado, Tumor angiogenesis and vascular patterning: A mathematical model, PloS One, 6 (2011), e19989. doi: 10.1371/journal.pone.0019989 |
[78] | On particle weighted methods and smooth particle hydrodynamics. Mathematical Models and Methods in Applied Sciences (1999) 9: 161-209. |
[79] | Emergent vascular network inhomogeneities and resulting blood flow patterns in a growing tumor. Journal of Theoretical Biology (2008) 250: 257-280. |
[80] | Vascular remodelling of an arterio-venous blood vessel network during solid tumour growth. Journal of Theoretical Biology (2009) 259: 405-422. |
[81] | Dynamic measurement of human capillary blood pressure. Clinical Science (1988) 74: 507-512. |
[82] | Coupled modeling of blood perfusion in intravascular, interstitial spaces in tumor microvasculature. Journal of Biomechanics (2008) 41: 996-1004. |
[83] | Erythrocyte-derived sphingosine 1-phosphate is essential for vascular development. The Journal of Clinical Investigation (2014) 124: 4823-4828. |