Multiscale Modelling of Platelet Aggregation / Yichen Lu.

Format:

Book

Manuscript

Thesis/Dissertation

Author/Creator:

Lu, Yichen, author.

Contributor:

Diamond, Scott L., degree supervisor.

Sinno, Talid R., degree supervisor.

Brass, Lawrence F., degree committee member.

Crocker, John C., 1979- degree committee member.

University of Pennsylvania. Department of Chemical and Biomolecular Engineering, degree granting institution.

Language:

English

Subjects (All):

Penn dissertations--Chemical and biomolecular engineering.

Chemical and biomolecular engineering--Penn dissertations.

Local Subjects:

Penn dissertations--Chemical and biomolecular engineering.

Chemical and biomolecular engineering--Penn dissertations.

Physical Description:

xii, 148 leaves : illustrations ; 29 cm

Production:

[Philadelphia, Pennsylvania] : University of Pennsylvania, 2017.

Summary:

During clotting under flow, platelets bind and activate on collagen and release autocrinic factors such ADP and thromboxane, while tissue factor (TF) on the damaged wall leads to localized thrombin generation. Toward patient-specific simulation of thrombosis, a multiscale approach was developed to account for: platelet signaling (neural network trained by pairwise agonist scanning, PAS-NN), platelet positions (lattice kinetic Monte Carlo, LKMC), wall-generated thrombin and platelet-released ADP/thromboxane convection-diffusion (PDE), and flow over a growing clot (lattice Boltzmann). LKMC included shear-driven platelet aggregate restructuring. The PDEs for thrombin, ADP, and thromboxane were solved by finite element method using cell activation-driven adaptive triangular meshing. At all times, intracellular calcium was known for each platelet by PAS-NN in response to its unique exposure to local collagen, ADP, thromboxane, and thrombin. The model accurately predicted clot morphology and growth with time on collagen/TF surface as compared to microfluidic blood perfusion experiments. The model also predicted the complete occlusion of the blood channel under pressure relief settings. Prior to occlusion, intrathrombus concentrations reached 50 nM thrombin, ~1 μM thromboxane, and ~10 μM ADP, while the wall shear rate on the rough clot peaked at ~1000-2000 sec-1. Additionally, clotting on TF/collagen was accurately simulated for modulators of platelet cyclooxygenase-1, P2Y1, and IP-receptor. The model was then extended to a rectangular channel with symmetric Gaussian obstacles representative of a coronary artery with severe stenosis. The upgraded stenosis model was able to predict platelet deposition dynamics at the post-stenotic segment corresponding to development of artery thrombosis prior to severe myocardial infarction. The presence of stenosis conditions alters the hemodynamics of normal hemostasis, showing a different thrombus growth mechanism. The model was able to recreate the platelet aggregation process under the complex recirculating flow features and make reasonable prediction on the clot morphology with flow separation. The model also detected recirculating transport dynamics for diffusible species in response to vortex features, posing interesting questions on the interplay between biological signaling and prevailing hemodynamics. In future work, the model will be extended to clot growth with a patient cardio-vasculature under pulsatile flow conditions.

Notes:

Ph. D. University of Pennsylvania 2017.

Department: Chemical and Biomolecular Engineering.

Supervisor: Scott L. Diamond; Talid R. Sinno.

Includes bibliographical references.

OCLC:

1334945517