دورية أكاديمية
أعرض في EDS

Cellular Blood Flow Modeling with HemoCell.

التفاصيل البيبلوغرافية
العنوان: Cellular Blood Flow Modeling with HemoCell.
المؤلفون: Zavodszky G; University of Amsterdam, Amsterdam, Netherlands. g.zavodszky@uva.nl., Spieker C; University of Amsterdam, Amsterdam, Netherlands., Czaja B; SURF Cooperation, Amsterdam, The Netherlands., van Rooij B; Philips Medical Systems, Best, The Netherlands.
المصدر: Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2024; Vol. 2716, pp. 351-368.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Humana Press Country of Publication: United States NLM ID: 9214969 Publication Model: Print Cited Medium: Internet ISSN: 1940-6029 (Electronic) Linking ISSN: 10643745 NLM ISO Abbreviation: Methods Mol Biol Subsets: MEDLINE
أسماء مطبوعة: Publication: Totowa, NJ : Humana Press
Original Publication: Clifton, N.J. : Humana Press,
مواضيع طبية MeSH: Erythrocytes* , Glass*, Humans ; Computer Simulation ; Kidney Tubules ; Rheology
مستخلص: 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.
(© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)
References: Boron WF, Boulpaep EL (eds) (2017) Medical physiology, 3rd edn. Elsevier, Philadelphia.
Caro CG (2012) The mechanics of the circulation, 2nd edn. Cambridge University Press, Cambridge.
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. (PMID: 10.1039/C8SM00061A29561062)
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. (PMID: 10.1073/pnas.1210236109/-/DCSupplemental232132293529085)
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. (PMID: 10.1103/PhysRevLett.98.07830117359066)
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. (PMID: 10.1126/science.168.3934.9775441028)
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. (PMID: 10.1016/S0006-3495(84)84225-364265401434900)
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. (PMID: 10.1038/srep04348)
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. (PMID: 10.1146/annurev-fluid-010816-060302)
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. (PMID: 10.1152/ajplegacy.1931.96.3.562)
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. (PMID: 1481902)
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. (PMID: 10.1016/j.bpj.2016.08.026277057715052466)
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. (PMID: 10.1146/annurev-fluid-010313-141349)
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. (PMID: 10.1038/srep04871247860004007071)
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. (PMID: 10.1039/C3SM51645H25353617)
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. (PMID: 10.1016/j.ces.2008.11.028)
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. (PMID: 10.1016/j.ijheatfluidflow.2013.06.008)
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. (PMID: 10.1103/PhysRev.94.511)
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. (PMID: 10.1146/annurev.fluid.30.1.329)
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.
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. (PMID: 10.1103/PhysRevA.40.7193)
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. (PMID: 10.1007/978-3-319-44649-3)
Peskin CS (2002) The immersed boundary method. Acta Numer 11:479–517. https://doi.org/10.1017/S0962492902000077. (PMID: 10.1017/S0962492902000077)
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. (PMID: 10.1016/S0006-3495(96)79556-587701941224916)
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. (PMID: 10.1529/biophysj.104.047332157497781305517)
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.
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. (PMID: 10.1371/journal.pcbi.1007716321634057093031)
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.
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. (PMID: 10.1016/j.procs.2017.05.084)
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. (PMID: 10.1088/0022-3719/5/15/006)
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. (PMID: 10.1016/j.compfluid.2018.04.025)
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. (PMID: 10.1016/S0049-3848(97)00014-59062952)
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. (PMID: 10.1016/j.jocs.2017.11.008)
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. (PMID: 10.1063/1.5085881)
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. (PMID: 10.1182/blood.202201672936026602)
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. (PMID: 10.1146/annurev-bioeng-071516-04453928441034)
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. (PMID: 10.3233/BIR-1506126600266)
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. (PMID: 10.1098/rsif.2019.0148315753446833312)
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. (PMID: 10.1098/rsfs.2019.012633335707)
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. (PMID: 10.1007/s10439-021-02870-4346680988671278)
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. (PMID: 10.1182/blood-2006-04-011551167726091895550)
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. (PMID: 10.1016/j.jvs.2014.12.05025704412)
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. (PMID: 10.1016/j.bpj.2010.01.032204417312862189)
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. (PMID: 10.1002/iub.58422131167)
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. (PMID: 17361027)
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.
Jenner P (2003) Oxidative stress in Parkinson’s disease. Ann Neurol 53(S3):S26–S38. https://doi.org/10.1002/ana.10483. (PMID: 10.1002/ana.1048312666096)
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. (PMID: 10.1016/0005-2736(85)90370-03995035)
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. (PMID: 10.3390/app8091616)
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. (PMID: 10.1080/10255842.2022.2034794)
فهرسة مساهمة: Keywords: Blood rheology; Cellular Blood Simulation; Computational Fluid Dynamics; High-Performance Computation; Immersed Boundary Method; Lattice Boltzmann method; Microfluidics
تواريخ الأحداث: Date Created: 20230913 Date Completed: 20230914 Latest Revision: 20230916
رمز التحديث: 20240628
DOI: 10.1007/978-1-0716-3449-3_16
PMID: 37702948
قاعدة البيانات: MEDLINE
الوصف
تدمد:1940-6029
DOI:10.1007/978-1-0716-3449-3_16