A predictive multiscale model for simulating flow-induced platelet activation: Correlating in silico results with in vitro results. (5th March 2021)

• Record Type: Journal Article
• Authors: Zhang, Peng; Sheriff, Jawaad; Einav, Shmuel; Slepian, Marvin J.; Deng, Yuefan; Bluestein, Danny
• Is Part Of: Journal of biomechanics. Volume 117(2021)
• Publication Date: 2021-03-05
• Subjects: Platelet activation -- Mechanical transduction -- Filopodia formation -- Multiscale simulations; Animal mechanics -- Periodicals; Biomechanics -- Periodicals; Biomechanics -- Periodicals; Mécanique animale -- Périodiques; Biomécanique -- Périodiques; Electronic journals; 571.4305
• DOI: 10.1016/j.jbiomech.2021.110275
• Languages: English
• ISSNs: 0021-9290

Abstract

Abstract: Flow-induced platelet activation prompts complex filopodial formation. Continuum methods fail to capture such molecular-scale mechanisms. A multiscale numerical model was developed to simulate this activation process, where a Dissipative Particle Dynamics (DPD) model of viscous blood flow is interfaced with a Coarse Grained Molecular Dynamics (CGMD) platelet model. Embedded in DPD blood flow, the macroscopic dynamic stresses are interactively transferred to the CGMD model, inducing intra-platelet associated events. The platelets activate by a biomechanical transductive linkage chain and dynamically change their shape in response. The models are fully coupled via a hybrid-potential interface and multiple time-stepping (MTS) schemes for handling the disparity between the spatiotemporal scales. Cumulative hemodynamic stresses that may lead to platelet activation are mapped on the surface membrane and simultaneously transmitted to the cytoplasm and cytoskeleton. Upon activation, the flowing platelets lose their quiescent discoid shape and evolve by forming filopodia. The model predictions were validated by a set of in vitro experiments, Platelets were exposed to various combinations of shear stresses and durations in our programmable hemodynamic shearing device (HSD). Their shape change was measured at multiple time points using scanning electron microscopy (SEM). The CGMD model parameters were fine-tuned by interrogating a parameter space established in these experiments. Segmentation of the SEM imaging streams was conducted by a deep machine learning system. This model can be further employed to simulate shear mediated platelet activation thrombosis initiation and to study the effects of modulating platelet properties to enhance their shear resistance via mechanotransduction pathways.