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Cellular Blood Flow Modeling with HemoCell

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High Performance Computing for Drug Discovery and Biomedicine

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2716))

Abstract

Many of the intriguing properties of blood originate from its cellular nature. Bulk effects, such as viscosity, depend on the local shear rates and on the size of the vessels. While empirical descriptions of bulk rheology are available for decades, their validity is limited to the experimental conditions they were observed under. These are typically artificial scenarios (e.g., perfectly straight glass tube or in pure shear with no gradients). Such conditions make experimental measurements simpler; however, they do not exist in real systems (i.e., in a real human circulatory system). Therefore, as we strive to increase our understanding on the cardiovascular system and improve the accuracy of our computational predictions, we need to incorporate a more comprehensive description of the cellular nature of blood. This, however, presents several computational challenges that can only be addressed by high performance computing. In this chapter, we describe HemoCell (https://www.hemocell.eu), an open-source high-performance cellular blood flow simulation, which implements validated mechanical models for red blood cells and is capable of reproducing the emergent transport characteristics of such a complex cellular system. We discuss the accuracy and the range of validity, and demonstrate applications on a series of human diseases.

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References

  1. Boron WF, Boulpaep EL (eds) (2017) Medical physiology, 3rd edn. Elsevier, Philadelphia

    Google Scholar 

  2. Caro CG (2012) The mechanics of the circulation, 2nd edn. Cambridge University Press, Cambridge

    Google Scholar 

  3. Varchanis S, Dimakopoulos Y, Wagner C, Tsamopoulos J (2018) How viscoelastic is human blood plasma? Soft Matter 14(21):4238–4251. https://doi.org/10.1039/C8SM00061A

    Article  CAS  PubMed  Google Scholar 

  4. Dupire J, Socol M, Viallat A (2012) Full dynamics of a red blood cell in shear flow. Proc Natl Acad Sci U S A 109(51):20808. https://doi.org/10.1073/pnas.1210236109/-/DCSupplemental; www.pnas.org/cgi/doi/10.1073/pnas.1210236109

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Skotheim JM, Secomb TW (2007) Red blood cells and other nonspherical capsules in shear flow: oscillatory dynamics and the Tank-Treading-to-Tumbling Transition. Phys Rev Lett 98(7):078301. https://doi.org/10.1103/PhysRevLett.98.078301

    Article  CAS  PubMed  Google Scholar 

  6. Chien S (1970) Shear dependence of effective cell volume as a determinant of blood viscosity. Science 168(3934):977–979. https://doi.org/10.1126/science.168.3934.977

    Article  CAS  PubMed  Google Scholar 

  7. Samsel RW, Perelson AS (1984) Kinetics of rouleau formation. II. Reversible reactions. Biophys J 45(4):805–824. https://doi.org/10.1016/S0006-3495(84)84225-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Brust M et al (2014) The plasma protein fibrinogen stabilizes clusters of red blood cells in microcapillary flows. Sci Rep 4:1–6. https://doi.org/10.1038/srep04348

    Article  CAS  Google Scholar 

  9. Secomb TW (2017) Blood flow in the microcirculation. Annu Rev Fluid Mech 49(August):443–461. https://doi.org/10.1146/annurev-fluid-010816-060302

    Article  Google Scholar 

  10. Fåhræus R, Lindqvist T (1931) The viscosity of the blood in narrow capillary tubes. Am J Physiol-Leg Content 96(3):562–568. https://doi.org/10.1152/ajplegacy.1931.96.3.562

    Article  Google Scholar 

  11. Pries AR, Neuhaus D, Gaehtgens P (1992) Blood viscosity in tube flow: dependence on diameter and hematocrit. Am J Physiol 263(6 Pt 2):H1770–H1778

    CAS  PubMed  Google Scholar 

  12. Carboni EJ et al (2016) Direct tracking of particles and quantification of margination in blood flow. Biophys J 111(7):1487–1495. https://doi.org/10.1016/j.bpj.2016.08.026

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Freund JB (2014) Numerical simulation of flowing blood cells. Annu Rev Fluid Mech 46(1):67–95. https://doi.org/10.1146/annurev-fluid-010313-141349

    Article  Google Scholar 

  14. Müller K, Fedosov DA, Gompper G (2014) Margination of micro- and nano-particles in blood flow and its effect on drug delivery. Sci Rep 4:4871. https://doi.org/10.1038/srep04871

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Krüger T, Gross M, Raabe D, Varnik F (2013) Crossover from tumbling to tank-treading-like motion in dense simulated suspensions of red blood cells. Soft Matter 9(37):9008–9015. https://doi.org/10.1039/C3SM51645H

    Article  PubMed  Google Scholar 

  16. Hosseini SM, Feng JJ (2009) A particle-based model for the transport of erythrocytes in capillaries. Chem Eng Sci 64(22):4488–4497. https://doi.org/10.1016/j.ces.2008.11.028

    Article  CAS  Google Scholar 

  17. Závodszky G, Paál G (2013) Validation of a lattice Boltzmann method implementation for a 3D transient fluid flow in an intracranial aneurysm geometry. Int J Heat Fluid Flow 44:276–283. https://doi.org/10.1016/j.ijheatfluidflow.2013.06.008

    Article  Google Scholar 

  18. Bhatnagar PL, Gross EP, Krook M (1954) A model for collision processes in gases. I. Small amplitude processes in charged and neutral one-component systems. Phys Rev 94(3):511–525. https://doi.org/10.1103/PhysRev.94.511

    Article  CAS  Google Scholar 

  19. Chen S, Doolen GD (1998) Lattice Boltzmann method for fluid flows. Annu Rev Fluid Mech 30(1):329–364. https://doi.org/10.1146/annurev.fluid.30.1.329

    Article  Google Scholar 

  20. Qian Y, D’Humières D, Lallemand P (1992) Lattice BGK models for Navier-Stokes equation. EPL Europhys Lett 479 [Online]. Available: http://iopscience.iop.org/0295-5075/17/6/001. Accessed 12 Jul 2014

  21. Rosenau P (1989) Extending hydrodynamics via the regularization of the Chapman-Enskog expansion. Phys Rev A 40(12):7193–7196. https://doi.org/10.1103/PhysRevA.40.7193

    Article  CAS  Google Scholar 

  22. Krüger T, Kusumaatmaja H, Kuzmin A, Shardt O, Silva G, Viggen EM (2017) The Lattice Boltzmann method: principles and practice in graduate texts in physics. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-44649-3

    Book  Google Scholar 

  23. Peskin CS (2002) The immersed boundary method. Acta Numer 11:479–517. https://doi.org/10.1017/S0962492902000077

    Article  Google Scholar 

  24. Hansen JC, Skalak R, Chien S, Hoger A (1996) An elastic network model based on the structure of the red blood cell membrane skeleton. Biophys J 70(1):146–166. https://doi.org/10.1016/S0006-3495(96)79556-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Li J, Dao M, Lim CT, Suresh S (2005) Spectrin-level modeling of the cytoskeleton and optical tweezers stretching of the erythrocyte. Biophys J 88(5):3707–3719. https://doi.org/10.1529/biophysj.104.047332

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Závodszky G, van Rooij B, Azizi V, Hoekstra A (2017) Cellular level in-silico modeling of blood rheology with an improved material model for red blood cells. Front Physiol 8. https://doi.org/10.3389/fphys.2017.00563

  27. Czaja B, Gutierrez M, Závodszky G, de Kanter D, Hoekstra A, Eniola-Adefeso O (2020) The influence of red blood cell deformability on hematocrit profiles and platelet margination. PLOS Comput Biol 16(3):e1007716. https://doi.org/10.1371/journal.pcbi.1007716

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. de Vries K, Nikishova A, Czaja B, Závodszky G, Hoekstra AG (2020) Inverse uncertainty quantification of a cell model using a Gaussian process metamodel. Int J Uncertain Quantif 10(4). https://doi.org/10.1615/Int.J.UncertaintyQuantification.2020033186

  29. Zavodszky G, van Rooij B, Azizi V, Alowayyed S, Hoekstra A (2017) Hemocell: a high-performance microscopic cellular library. Procedia Comput Sci 108:159–165. https://doi.org/10.1016/j.procs.2017.05.084

    Article  Google Scholar 

  30. Lees AW, Edwards SF (1972) The computer study of transport processes under extreme conditions. J Phys C Solid State Phys 5(15):1921. https://doi.org/10.1088/0022-3719/5/15/006

    Article  Google Scholar 

  31. Azizi Tarksalooyeh VW, Závodszky G, van Rooij BJM, Hoekstra AG (2018) Inflow and outflow boundary conditions for 2D suspension simulations with the immersed boundary lattice Boltzmann method. Comput Fluids 172:312–317. https://doi.org/10.1016/j.compfluid.2018.04.025

    Article  Google Scholar 

  32. Varon D et al (1997) A new method for quantitative analysis of whole blood platelet interaction with extracellular matrix under flow conditions. Thromb Res 85(4):283–294. https://doi.org/10.1016/S0049-3848(97)00014-5

    Article  CAS  PubMed  Google Scholar 

  33. Alowayyed S, Závodszky G, Azizi V, Hoekstra AG (2018) Load balancing of parallel cell-based blood flow simulations. J Comput Sci 24:1–7. https://doi.org/10.1016/j.jocs.2017.11.008

    Article  Google Scholar 

  34. Závodszky G, van Rooij B, Czaja B, Azizi V, de Kanter D, Hoekstra AG (2019) Red blood cell and platelet diffusivity and margination in the presence of cross-stream gradients in blood flows. Phys Fluids 31(3):031903. https://doi.org/10.1063/1.5085881

    Article  CAS  Google Scholar 

  35. Kimmerlin Q et al (2022) Loss of α4A- and β1-tubulins leads to severe platelet spherocytosis and strongly impairs hemostasis in mice. Blood 140(21):2290–2299. https://doi.org/10.1182/blood.2022016729

    Article  CAS  PubMed  Google Scholar 

  36. Casa LDC, Ku DN (2017) Thrombus formation at high shear rates. Annu Rev Biomed Eng 19(1):415–433. https://doi.org/10.1146/annurev-bioeng-071516-044539

    Article  CAS  PubMed  Google Scholar 

  37. Gogia S, Neelamegham S (2015) Role of fluid shear stress in regulating VWF structure, function and related blood disorders. Biorheology 52(5–6):319–335. https://doi.org/10.3233/BIR-15061

    Article  CAS  PubMed  Google Scholar 

  38. van Rooij BJM, Závodszky G, Azizi Tarksalooyeh VW, Hoekstra AG (2019) Identifying the start of a platelet aggregate by the shear rate and the cell-depleted layer. J R Soc Interface 16(159):20190148. https://doi.org/10.1098/rsif.2019.0148

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. van Rooij BJM, Závodszky G, Hoekstra AG, Ku DN (2021) Haemodynamic flow conditions at the initiation of high-shear platelet aggregation: a combined in vitro and cellular in silico study. Interface Focus 11(1):20190126. https://doi.org/10.1098/rsfs.2019.0126

    Article  PubMed  Google Scholar 

  40. Spieker CJ et al (2021) The effects of micro-vessel curvature induced Elongational flows on platelet adhesion. Ann Biomed Eng 49(12):3609–3620. https://doi.org/10.1007/s10439-021-02870-4

    Article  PubMed  PubMed Central  Google Scholar 

  41. Ruggeri ZM, Orje JN, Habermann R, Federici AB, Reininger AJ (2006) Activation-independent platelet adhesion and aggregation under elevated shear stress. Blood 108(6):1903–1910. https://doi.org/10.1182/blood-2006-04-011551

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Casa LDC, Deaton DH, Ku DN (2015) Role of high shear rate in thrombosis. J Vasc Surg 61(4):1068–1080. https://doi.org/10.1016/j.jvs.2014.12.050

    Article  PubMed  Google Scholar 

  43. Sing CE, Alexander-Katz A (2010) Elongational flow induces the unfolding of Von Willebrand factor at physiological flow rates. Biophys J 98(9):L35–L37. https://doi.org/10.1016/j.bpj.2010.01.032

    Article  PubMed  PubMed Central  Google Scholar 

  44. Chirico EN, Pialoux V (2012) Role of oxidative stress in the pathogenesis of sickle cell disease. IUBMB Life 64(1):72–80. https://doi.org/10.1002/iub.584

    Article  CAS  PubMed  Google Scholar 

  45. Shin S, Ku Y-H, Ho J-X, Kim Y-K, Suh J-S, Singh M (2007) Progressive impairment of erythrocyte deformability as indicator of microangiopathy in type 2 diabetes mellitus. Clin Hemorheol Microcirc 36(3):253–261

    CAS  PubMed  Google Scholar 

  46. Tan JSY, Závodszky G, Sloot PMA (2018) Understanding malaria induced red blood cell deformation using data-driven Lattice Boltzmann simulations. In: Computational science – ICCS 2018, Y Shi, H Fu, Y Tian, VV Krzhizhanovskaya, MH Lees, J Dongarra, PMA Sloot (eds.), in Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 392–403. https://doi.org/10.1007/978-3-319-93698-7_30

  47. Jenner P (2003) Oxidative stress in Parkinson’s disease. Ann Neurol 53(S3):S26–S38. https://doi.org/10.1002/ana.10483

    Article  CAS  PubMed  Google Scholar 

  48. Rice-Evans C, Baysal E, Pashby DP, Hochstein P (1985) t-butyl hydroperoxide-induced perturbations of human erythrocytes as a model for oxidant stress. Biochim Biophys Acta BBA 815(3):426–432. https://doi.org/10.1016/0005-2736(85)90370-0

    Article  CAS  PubMed  Google Scholar 

  49. De Haan M, Zavodszky G, Azizi V, Hoekstra AG (2018) Numerical investigation of the effects of red blood cell cytoplasmic viscosity contrasts on single cell and bulk transport behaviour. Appl Sci 8(9):9. https://doi.org/10.3390/app8091616

    Article  CAS  Google Scholar 

  50. Czaja B et al (2022) The effect of stiffened diabetic red blood cells on wall shear stress in a reconstructed 3D microaneurysm. Comput Methods Biomech Biomed Engin 25:1–19. https://doi.org/10.1080/10255842.2022.2034794

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Amsterdam, Amsterdam, Netherlands

    Gabor Zavodszky & Christian Spieker

  2. SURF Cooperation, Amsterdam, The Netherlands

    Benjamin Czaja

  3. Philips Medical Systems, Best, The Netherlands

    Britt van Rooij

Authors
  1. Gabor Zavodszky
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  2. Christian Spieker
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  3. Benjamin Czaja
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  4. Britt van Rooij
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Corresponding author

Correspondence to Gabor Zavodszky .

Editor information

Editors and Affiliations

  1. In Silico Research and Development, Evotec UK Ltd, Abingdon, UK

    Alexander Heifetz

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© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Zavodszky, G., Spieker, C., Czaja, B., van Rooij, B. (2024). Cellular Blood Flow Modeling with HemoCell. In: Heifetz, A. (eds) High Performance Computing for Drug Discovery and Biomedicine. Methods in Molecular Biology, vol 2716. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3449-3_16

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  • DOI: https://doi.org/10.1007/978-1-0716-3449-3_16

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3448-6

  • Online ISBN: 978-1-0716-3449-3

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