Alireza Yazdani

Comparison of erythrocyte dynamics in shear flow under different stress-free configurations

An open question that has persisted for decades is whether the cytoskeleton of a red blood cell is stress-free or under a stress. This question is important in the context of theoretical modeling of cellular motion under a flowing condition where it is necessary to make an assumption about the stress-free state. Here, we present a 3D numerical study to compare the cell dynamics in a simple shear flow under two different stress-free states, a biconcave discocyte representing the resting shape of the cell, and a nearly spherical oblate shape. We find that whether the stress-free states make a significant difference or not depends on the viscosity of the suspending medium. If the viscosity is close to that of blood plasma, the two stress-free states do not show any significant difference in cell dynamics. However, when the suspending medium viscosity is well above that of the physiological range, as in many in vitro studies, the shear rate separating the tank-treading and tumbling dynamics is observed to be higher for the biconcave stress-free state than the spheroidal state. The former shows a strong shape oscillation with repeated departures from the biconcave shape, while the latter shows a nearly stable biconcave shape. It is found that the cell membrane in the biconcave stress-free state is under a compressive stress and a weaker bending force density, leading to a periodic compression of the cell. The shape oscillation then leads to a higher energy barrier against membrane tank-tread leading to an early transition to tumbling. However, if the cells are released with a large off-shear plane angle, the oscillations can be suppressed due to an azimuthal motion of the membrane along the vorticity direction leading to a redistribution of the membrane points and lowering of the energy barrier, which again results in a nearly similar behavior of the cells under the two different stress-free states. A variety of off-shear plane dynamics is observed, namely, rolling, kayaking, precession, and a new dynamics termed “hovering.” For the physiological viscosity range, the shear-plane tumbling appears to be relatively less common, while the rolling is observed to be more stable.

Transport dissipative particle dynamics model for mesoscopic advection-diffusion-reaction problems

We present a transport dissipative particle dynamics (tDPD) model for simulating mesoscopic problems involving advection-diffusion-reaction (ADR) processes, along with a methodology for implementation of the correct Dirichlet and Neumann boundary conditions in tDPD simulations. tDPD is an extension of the classic dissipative particle dynamics (DPD) framework with extra variables for describing the evolution of concentration fields. The transport of concentration is modeled by a Fickian flux and a random flux between tDPD particles, and the advection is implicitly considered by the movements of these Lagrangian particles. An analytical formula is proposed to relate the tDPD parameters to the effective diffusion coefficient. To validate the present tDPD model and the boundary conditions, we perform three tDPD simulations of one-dimensional diffusion with different boundary conditions, and the results show excellent agreement with the theoretical solutions. We also performed two-dimensional simulations of ADR systems and the tDPD simulations agree well with the results obtained by the spectral element method. Finally, we present an application of the tDPD model to the dynamic process of blood coagulation involving 25 reacting species in order to demonstrate the potential of tDPD in simulating biological dynamics at the mesoscale. We find that the tDPD solution of this comprehensive 25-species coagulation model is only twice as computationally expensive as the conventional DPD simulation of the hydrodynamics only, which is a significant advantage over available continuum solvers.

A General Shear-Dependent Model for Thrombus Formation.

Modeling the transport, activation, and adhesion of platelets is crucial in predicting thrombus formation and growth following a thrombotic event in normal or pathological conditions. We propose a shear-dependent platelet adhesive model based on the Morse potential that is calibrated by existing in vivo and in vitro experimental data and can be used over a wide range of flow shear rates (100<γ˙<28,000s-1). We introduce an Eulerian-Lagrangian model where hemodynamics is solved on a fixed Eulerian grid, while platelets are tracked using a Lagrangian framework. A force coupling method is introduced for bidirectional coupling of platelet motion with blood flow. Further, we couple the calibrated platelet aggregation model with a tissue-factor/contact pathway coagulation cascade, representing the relevant biology of thrombin generation and the subsequent fibrin deposition. The range of shear rates covered by the proposed model encompass venous and arterial thrombosis, ranging from low-shear-rate conditions in abdominal aortic aneurysms and thoracic aortic dissections to thrombosis in stenotic arteries following plaque rupture, where local shear rates are extremely high.

Flow in complex domains simulated by Dissipative Particle Dynamics driven by geometry-specific body-forces

We demonstrate how the quality of simulations by Dissipative Particle Dynamics (DPD) of flows in complex geometries is greatly enhanced when driven by body forces suitably tailored to the geometry. In practice, the body force fields are most conveniently chosen to be the pressure gradient of the corresponding Navier-Stokes (N-S) flow. In the first of three examples, the driving-force required to yield a stagnation-point flow is derived from the pressure field of the potential flow for a lattice of counter-rotating line vortices. Such a lattice contains periodic squares bounded by streamlines with four vortices within them. Hence, the DPD simulation can be performed with periodic boundary conditions to demonstrate the value of a non-uniform driving-force without the need to model real boundaries. The second example is an irregular geometry consisting of a 2D rectangular cavity on one side of an otherwise uniform channel. The Navier-Stokes pressure field for the same geometry is obtained numerically, and its interpolated gradient is then employed as the driving-force for the DPD simulation. Finally, we present a third example, where the proposed method is applied to a complex 3D geometry of an asymmetric constriction. It is shown that in each case the DPD simulations closely reproduce the Navier-Stokes solutions. Convergence rates are found to be much superior to alternative methods; in addition, the range of convergence with respect to Reynolds number and Mach number is greatly extended.

Analysis of membrane tank-tread of nonspherical capsules and red blood cells.

AbstractWe present an analysis of membrane motion of deformable capsules and red blood cells suspended in a linear shear flow and undergoing swinging and tumbling motions using three-dimensional numerical simulations. This study is motivated by the theory of the shape-preserving cells which predicts that the direction of the membrane rotation depends on the cell orientation and reverses at every 45° inclination angle of the cell major axis with respect to the external flow direction. By considering large deformation of capsules and red blood cells, here we investigate how the shape oscillation affects the time dependence and the direction reversal of the membrane rotation. We find that the membrane tank-tread is highly time-dependent in nature and synchronized with the time-dependent deformation. The maximum and minimum of the tank-tread velocity occur at and near the minimum and maximum deformation, respectively. For the swinging capsules and red blood cells, the direction of the membrane rotation is always along the direction of the external fluid rotation; however, a direction reversal occurs during the tumbling motion in which case the membrane rotates in the direction of the external fluid rotation when the major axis is mostly in the extensional quadrant of the shear flow, and in the opposite direction when it is mostly in the compressional quadrant. Unlike the theory which predicts the direction reversal at every 45° inclination angle irrespective of the control parameters, namely, the capillary number, viscosity ratio, and asphericity, we find that the angle at which the direction reversal occurs depends on these parameters. In particular, if the tumbling motion occurs by decreasing the capillary number, the membrane rotation is in the direction of the external flow rotation in the entire extensional quadrant, but in the opposite direction in the compressional quadrant, irrespective of the specific values of the capillary number. If the tumbling motion occurs by increasing the viscosity ratio and asphericity, the angle at which the direction reversal occurs depends on the specific values of these two parameters. The spatial variation of the tank-tread velocity also is analyzed and attributed to the straining motion of the external flow.

A discrete mesoscopic particle model of the mechanics of a multi-constituent arterial wall.

Blood vessels have unique properties that allow them to function together within a complex, self-regulating network. The contractile capacity of the wall combined with complex mechanical properties of the extracellular matrix enables vessels to adapt to changes in haemodynamic loading. Homogenized phenomenological and multi-constituent, structurally motivated continuum models have successfully captured these mechanical properties, but truly describing intricate microstructural details of the arterial wall may require a discrete framework. Such an approach would facilitate modelling interactions between or the separation of layers of the wall and would offer the advantage of seamless integration with discrete models of complex blood flow. We present a discrete particle model of a multi-constituent, nonlinearly elastic, anisotropic arterial wall, which we develop using the dissipative particle dynamics method. Mimicking basic features of the microstructure of the arterial wall, the model comprises an elastin matrix having isotropic nonlinear elastic properties plus anisotropic fibre reinforcement that represents the stiffer collagen fibres of the wall. These collagen fibres are distributed evenly and are oriented in four directions, symmetric to the vessel axis. Experimental results from biaxial mechanical tests of an artery are used for model validation, and a delamination test is simulated to demonstrate the new capabilities of the model.

Dynamic and rheological properties of soft biological cell suspensions

Quantifying dynamic and rheological properties of suspensions of soft biological particles such as vesicles, capsules, and red blood cells (RBCs) is fundamentally important in computational biology and biomedical engineering. In this review, recent studies on dynamic and rheological behavior of soft biological cell suspensions by computer simulations are presented, considering both unbounded and confined shear flow. Furthermore, the hemodynamic and hemorheological characteristics of RBCs in diseases such as malaria and sickle cell anemia are highlighted.

Data-driven Modeling of Hemodynamics and its Role on Thrombus Size and Shape in Aortic Dissections

Aortic dissection is a pathology that manifests due to microstructural defects in the aortic wall. Blood enters the damaged wall through an intimal tear, thereby creating a so-called false lumen and exposing the blood to thrombogenic intramural constituents such as collagen. The natural history of this acute vascular injury thus depends, in part, on thrombus formation, maturation, and possible healing within the false lumen. A key question is: Why do some false lumens thrombose completely while others thrombose partially or little at all? An ability to predict the location and extent of thrombus in subjects with dissection could contribute significantly to clinical decision-making, including interventional design. We develop, for the first time, a data-driven particle-continuum model for thrombus formation in a murine model of aortic dissection. In the proposed model, we simulate a final-value problem in lieu of the original initial-value problem with significantly fewer particles that may grow in size upon activation, thus representing the local concentration of blood-borne species. Numerical results confirm that geometry and local hemodynamics play significant roles in the acute progression of thrombus. Despite geometrical differences between murine and human dissections, mouse models can provide considerable insight and have gained popularity owing to their reproducibility. Our results for three classes of geometrically different false lumens show that thrombus forms and extends to a greater extent in regions with lower bulk shear rates. Dense thrombi are less likely to form in high-shear zones and in the presence of strong vortices. The present data-driven study suggests that the proposed model is robust and can be employed to assess thrombus formation in human aortic dissections.

Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics

Medical research focuses on identifying the causes and deciphering the mechanisms related to a disease, aiming to eventually develop accurate diagnostic tools and effective treatments. With the breakthrough technological advances of the last decades, the “educated” guess that had been previously used for raising scientific hypotheses is rapidly being replaced by the knowledge provided through untargeted high-throughput methods that are able to generate enormous data sets in a short amount of time and in a cost-effective manner. Fortunately, major advances have been also observed in computational mathematics which enables the accurate analysis of the “big” data sets deriving from high-throughput approaches. Here, we summarize the most important “-omics” procedures and describe the current challenges related to their use. Additionally, we describe the novel methods of data-mining and machine learning analysis, and particularly, how they can be used in a hierarchical manner to produce robust results for medicine from “big” data.

Quantifying Platelet Margination in Diabetic Blood Flow

Patients with type 2 diabetes mellitus (T2DM) develop thrombotic abnormalities strongly associated with cardiovascular diseases. In addition to the changes of numerous coagulation factors such as elevated levels of thrombin and fibrinogen, the abnormal rheological effects of red blood cells (RBCs) and platelets flowing in blood are crucial in platelet adhesion and thrombus formation in T2DM. An important process contributing to the latter is the platelet margination. We employ the dissipative particle dynamics method to seamlessly model cells, plasma, and vessel walls. We perform a systematic study on RBC and platelet transport in cylindrical vessels by considering different cell shapes, sizes, and RBC deformabilities in healthy and T2DM blood, as well as variable flowrates and hematocrit. In particular, we use cellular-level RBC and platelet models with parameters derived from patient-specific data and present a sensitivity study. We find T2DM RBCs, which are less deformable compared to normal RBCs, lower the transport of platelets toward the vessel walls, whereas platelets with higher mean volume (often observed in T2DM) lead to enhanced margination. Furthermore, increasing the flowrate or hematocrit enhances platelet margination. We also investigated the effect of platelet shape and observed a nonmonotonic variation with the highest near-wall concentration corresponding to platelets with a moderate aspect ratio of 0.38. We examine the role of white blood cells (WBCs), whose count is increased notably in T2DM patients. We find that WBC rolling or WBC adhesion tends to decrease platelet margination due to hydrodynamic effects. To the best of our knowledge, such simulations of blood including all blood cells have not been performed before, and our quantitative findings can help separate the effects of hydrodynamic interactions from adhesive interactions and potentially shed light on the associated pathological processes in T2DM such as increased inflammatory response, platelet activation and adhesion, and ultimately thrombus formation.