Cellular blood flow modelling with HemoCell

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 , 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, the range of validity, and demonstrate applications on a series of human diseases.

[1]  M. Freund,et al.  Loss of α4A- and β1-tubulin leads to severe platelet spherocytosis and strongly impairs hemostasis in mice. , 2022, Blood.

[2]  A. Hoekstra,et al.  The effect of stiffened diabetic red blood cells on wall shear stress in a reconstructed 3D microaneurysm , 2022, Computer methods in biomechanics and biomedical engineering.

[3]  A. Hoekstra,et al.  The Effects of Micro-vessel Curvature Induced Elongational Flows on Platelet Adhesion , 2021, Annals of Biomedical Engineering.

[4]  A. G. Hoekstra,et al.  Haemodynamic flow conditions at the initiation of high-shear platelet aggregation: a combined in vitro and cellular in silico study , 2020, Interface Focus.

[5]  Gábor Závodszky,et al.  The influence of red blood cell deformability on hematocrit profiles and platelet margination , 2020, PLoS Comput. Biol..

[6]  A. Hoekstra,et al.  Identifying the start of a platelet aggregate by the shear rate and the cell-depleted layer , 2019, Journal of the Royal Society Interface.

[7]  A. Hoekstra,et al.  Red blood cell and platelet diffusivity and margination in the presence of cross-stream gradients in blood flows , 2019, Physics of Fluids.

[8]  Gábor Závodszky,et al.  Numerical Investigation of the Effects of Red Blood Cell Cytoplasmic Viscosity Contrasts on Single Cell and Bulk Transport Behaviour , 2018, Applied Sciences.

[9]  Gábor Závodszky,et al.  Inflow and outflow boundary conditions for 2D suspension simulations with the immersed boundary lattice Boltzmann method , 2018, Computers & Fluids.

[10]  Joey Sing Yee Tan,et al.  Understanding Malaria Induced Red Blood Cell Deformation Using Data-Driven Lattice Boltzmann Simulations , 2018, ICCS.

[11]  J. Tsamopoulos,et al.  How viscoelastic is human blood plasma? , 2018, Soft matter.

[12]  Gábor Závodszky,et al.  Cellular Level In-silico Modeling of Blood Rheology with An Improved Material Model for Red Blood Cells , 2017, Front. Physiol..

[13]  D. Ku,et al.  Thrombus Formation at High Shear Rates. , 2017, Annual review of biomedical engineering.

[14]  Erlend Magnus Viggen,et al.  The Lattice Boltzmann Method: Principles and Practice , 2016 .

[15]  Anson W K Ma,et al.  Direct Tracking of Particles and Quantification of Margination in Blood Flow. , 2016, Biophysical journal.

[16]  S. Neelamegham,et al.  Role of fluid shear stress in regulating VWF structure, function and related blood disorders , 2015, Biorheology.

[17]  D. Ku,et al.  Role of high shear rate in thrombosis. , 2015, Journal of vascular surgery.

[18]  Gerhard Gompper,et al.  Margination of micro- and nano-particles in blood flow and its effect on drug delivery , 2014, Scientific Reports.

[19]  Thomas Podgorski,et al.  The plasma protein fibrinogen stabilizes clusters of red blood cells in microcapillary flows , 2014, Scientific Reports.

[20]  Jonathan B. Freund,et al.  Numerical Simulation of Flowing Blood Cells , 2014 .

[21]  Gábor Závodszky,et al.  Validation of a lattice Boltzmann method implementation for a 3D transient fluid flow in an intracranial aneurysm geometry , 2013 .

[22]  Dierk Raabe,et al.  Crossover from tumbling to tank-treading-like motion in dense simulated suspensions of red blood cells. , 2013, Soft matter.

[23]  M. Socol,et al.  Full dynamics of a red blood cell in shear flow , 2012, Proceedings of the National Academy of Sciences.

[24]  V. Pialoux,et al.  Role of oxidative stress in the pathogenesis of sickle cell disease , 2012, IUBMB life.

[25]  Alfredo Alexander-Katz,et al.  Elongational flow induces the unfolding of von Willebrand factor at physiological flow rates. , 2010, Biophysical journal.

[26]  James J. Feng,et al.  A particle-based model for the transport of erythrocytes in capillaries , 2009 .

[27]  S. Przedborski,et al.  Oxidative Stress in Parkinson's Disease , 2008, Annals of the New York Academy of Sciences.

[28]  T. Secomb,et al.  Red blood cells and other nonspherical capsules in shear flow: oscillatory dynamics and the tank-treading-to-tumbling transition. , 2006, Physical review letters.

[29]  A. Federici,et al.  Activation-independent platelet adhesion and aggregation under elevated shear stress. , 2005, Blood.

[30]  S. Suresh,et al.  Spectrin-level modeling of the cytoskeleton and optical tweezers stretching of the erythrocyte. , 2005, Biophysical journal.

[31]  C. Peskin.  The immersed boundary method , 2002, Acta Numerica.

[32]  Shiyi Chen,et al.  LATTICE BOLTZMANN METHOD FOR FLUID FLOWS , 2001 .

[33]  N. Savion,et al.  A new method for quantitative analysis of whole blood platelet interaction with extracellular matrix under flow conditions. , 1997, Thrombosis research.

[34]  A. Pries,et al.  Blood viscosity in tube flow: dependence on diameter and hematocrit. , 1992, The American journal of physiology.

[35]  Y. Qian,et al.  Lattice BGK Models for Navier-Stokes Equation , 1992 .

[36]  Rosenau,et al.  Extending hydrodynamics via the regularization of the Chapman-Enskog expansion. , 1989, Physical review. A, General physics.

[37]  C. Rice-Evans,et al.  t-butyl hydroperoxide-induced perturbations of human erythrocytes as a model for oxidant stress. , 1985, Biochimica et biophysica acta.

[38]  A. Perelson,et al.  Kinetics of rouleau formation. II. Reversible reactions. , 1984, Biophysical journal.

[39]  S. Edwards,et al.  The computer study of transport processes under extreme conditions , 1972 .

[40]  Shu Chien,et al.  Shear Dependence of Effective Cell Volume as a Determinant of Blood Viscosity , 1970, Science.

[41]  P. Bhatnagar,et al.  A Model for Collision Processes in Gases. I. Small Amplitude Processes in Charged and Neutral One-Component Systems , 1954 .

[42]  Robin Fåhræus,et al.  THE VISCOSITY OF THE BLOOD IN NARROW CAPILLARY TUBES , 1931 .

[43]  Gábor Závodszky,et al.  INVERSE UNCERTAINTY QUANTIFICATION OF A CELL MODEL USING A GAUSSIAN PROCESS METAMODEL , 2020, International Journal for Uncertainty Quantification.

[44]  Gábor Závodszky,et al.  Load balancing of parallel cell-based blood flow simulations , 2018, J. Comput. Sci..

[45]  Gábor Závodszky,et al.  Hemocell: a high-performance microscopic cellular library , 2017, ICCS.

[46]  Sehyun Shin,et al.  Progressive impairment of erythrocyte deformability as indicator of microangiopathy in type 2 diabetes mellitus. , 2007, Clinical hemorheology and microcirculation.

[47]  J.,et al.  The Mechanics of the Circulation , 2005 .

[48]  S Chien,et al.  An elastic network model based on the structure of the red blood cell membrane skeleton. , 1996, Biophysical journal.

[49]  G M Collins,et al.  Blood flow in the microcirculation. , 1966, Pacific medicine and surgery.