Angle of Inclination of Tank-Treading Red Cells: Dependence on Shear Rate and Suspending Medium

Fischer, Thomas M.; Korzeniewski, Rafal

doi:10.1016/j.bpj.2015.01.028

Cited by 17 publications

(13 citation statements)

References 44 publications

(65 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typically, an RBC exposed to a shear flow may elongate and align itself at a constant angle with respect to the flow. We have investigated the angle inclination and in Reference we confirmed that our model corresponds to the experimental measurements of the inclination angle reported in Reference .…”

Section: Validating Dynamic Behavior—rotation Frequency In Shear Flowsupporting

confidence: 87%

“…In References and , the blood samples were obtained from healthy blood bank volunteers. Chilling, centrifuge, and buffered salt solutions were used to obtain hematocrit of approximately 50%.…”

Section: Validating Dynamic Behavior—rotation Frequency In Shear Flowmentioning

confidence: 99%

“…For example, in References , the authors focus on the individual RBC passage through narrow channels, where the deformation index or the passage time is recorded and may be compared to the simulated values. Other experiments analyze the speed of cell rotation and its inclination angle in shear flow. Static experiments involve stretching of RBC using optical tweezers…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spring‐network model of red blood cell: From membrane mechanics to validation

Jančigová

Kovalčíková

Bohiniková

et al. 2020

Numerical Methods in Fluids

View full text Add to dashboard Cite

Summary Red blood cell membrane is highly elastic and proper modeling of this elasticity is essential for biomedical applications that involve computational experiments with blood flow. Inseparable and often some of the most difficult parts of modeling process are verification and validation. In this work, we present a revised model, which uses a spring network to represent the cell membrane immersed in a fluid and has been successfully used in blood flow simulations. We demonstrate the validation steps by first deriving the theoretical relations between the bulk properties of elastic membranes—shear modulus and area compressibility modulus—and parameters of the model that enter the nonlinear stretching and local area conservation computational moduli. We verify the theoretically derived relations using computer simulations of deformable triangular mesh. We calibrate the model by performing a computational version of the optical tweezers experiment. And finally, we validate the modeled cell behavior by investigating the cell rotation frequency when it is subjected to shear flow and cell deformation in narrow channels. The supplementary material contains an extensive dataset that can be used for setting different elastic properties for each cell in simulations of dense suspensions, while still conforming to the biological data. This work contains a complete model development process: From modelling of basic mechanical concepts (the spring network) and advanced biomechanical concepts (such as elasticity of the membrane), through calibration process towards the final stage of model validation.

show abstract

Section: Validating Dynamic Behavior—rotation Frequency In Shear Flowsupporting

confidence: 87%

Section: Validating Dynamic Behavior—rotation Frequency In Shear Flowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spring‐network model of red blood cell: From membrane mechanics to validation

Jančigová

Kovalčíková

Bohiniková

et al. 2020

Numerical Methods in Fluids

View full text Add to dashboard Cite

show abstract

“…Under certain flow conditions, a red blood cell in shear flow may tumble or exhibit a tank-treading motion of the membrane, depending on the shear rate [ 36 ]. We have investigated the inclination angle in [ 39 ] and confirmed that our model corresponds to the experimental measurements of the inclination angle reported in [ 40 ]. Further, in [ 34 ] we performed computational experiments of tank-treading frequency also reproducing the experimental results and thus validating the model.…”

Section: Design and Implementationsupporting

confidence: 83%

PyOIF: Computational tool for modelling of multi-cell flows in complex geometries

et al. 2020

View full text Add to dashboard Cite

A user ready, well documented software package PyOIF contains an implementation of a robust validated computational model for cell flow modelling. The software is capable of simulating processes involving biological cells immersed in a fluid. The examples of such processes are flows in microfluidic channels with numerous applications such as cell sorting, rare cell isolation or flow fractionation. Besides the typical usage of such computational model in the design process of microfluidic devices, PyOIF has been used in the computer-aided discovery involving mechanical properties of cell membranes. With this software, single cell, many cell, as well as dense cell suspensions can be simulated. Many cell simulations include cell-cell interactions and analyse their effect on the cells. PyOIF can be used to test the influence of mechanical properties of the membrane in flows and in membrane-membrane interactions. Dense suspensions may be used to study the effect of cell volume fraction on macroscopic phenomena such as cell-free layer, apparent suspension viscosity or cell degradation. The PyOIF module is based on the official ESPResSo distribution with few modifications and is available under the terms of the GNU General Public Licence. PyOIF is based on Python objects representing the cells and on the C++ computational core for fluid and interaction dynamics. The source code is freely available at GitHub repository, runs natively under Linux and MacOS and can be used in Windows Subsystem for Linux. The communication among PyOIF users and developers is maintained using active mailing lists. This work provides a basic background to the underlying computational models and to the implementation of interactions within this framework. We provide the prospective PyOIF users with a practical example of simulation script with reference to our publicly available User Guide.

show abstract

“…The RBCs in shear flow have been observed to exhibit two primary types of dynamics: tank treading (TT) motion and tumbling (TB) motion, depending on cell geometry (S/V ratio), cell elasticity (capillary number Ca µ = η oγ R 0 /µ where R 0 is the radius of a sphere with the same volume as an RBC), and fluid properties such as viscosity ratio (λ = η i /η o where η i and η o are the cytoplasm and suspending fluid viscosities) (25,(67)(68)(69)(70). At low shear rateγ, the resistance to shear causes RBCs to tumble.…”

Section: Dynamic Behavior Of Individual T2dm Rbc In Shear Flowmentioning

confidence: 99%

Modeling of biomechanics and biorheology of red blood cells in type-2 diabetes mellitus

Chang

Karniadakis

2017

Preprint

View full text Add to dashboard Cite

Erythrocytes in patients with type-2 diabetes mellitus (T2DM) are associated with reduced cell deformability and elevated blood viscosity, which contribute to impaired blood flow and other pathophysiological aspects of diabetesrelated vascular complications. In this study, by using a two-component red blood cell (RBC) model and systematic parameter variation, we perform detailed computational simulations to probe the alteration of the biomechanical, rheological and dynamic behavior of T2DM RBCs in response to morphological change and membrane stiffening. First, we examine the elastic response of T2DM RBCs subject to static tensile forcing and their viscoelastic relaxation response upon release of the stretching force. Second, we investigate the membrane fluctuations of T2DM RBCs and explore the effect of cell shape on the fluctuation amplitudes. Third, we subject the T2DM RBCs to shear flow and probe the effects of cell shape and effective membrane viscosity on their tank-treading movement. In addition, we model the cell dynamic behavior in a microfluidic channel with constriction and quantify the biorheological properties of individual T2DM RBCs. Finally, we simulate T2DM RBC suspensions under shear and compare the predicted viscosity with experimental measurements. Taken together these simulation results and their comparison with currently available experimental data are helpful in identifying a specific parametric model -the first of its kind -that best describes the main hallmarks of T2DM RBCs, which can be used in future simulation studies of hematologic complications of T2DM patients.

show abstract

Angle of Inclination of Tank-Treading Red Cells: Dependence on Shear Rate and Suspending Medium

Cited by 17 publications

References 44 publications

Spring‐network model of red blood cell: From membrane mechanics to validation

Spring‐network model of red blood cell: From membrane mechanics to validation

PyOIF: Computational tool for modelling of multi-cell flows in complex geometries

Modeling of biomechanics and biorheology of red blood cells in type-2 diabetes mellitus

Contact Info

Product

Resources

About