Date of Award

2011

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Graduate Group

Chemical and Biomolecular Engineering

First Advisor

Scott L. Diamond

Second Advisor

Talid Sinno

Abstract

Platelet aggregation is an essential process in forming a stable clot to prevent blood loss. The response of platelets to a complex signal of pro-clotting agonists determines the stability and size of the resulting clot. An underdeveloped clot represents a bleeding risk, while an overdeveloped clot can cause vessel occlusion, which can lead to heart attack or stroke. A multiscale model was developed to study the integration of platelet signaling within the complex phenomena driven by flow. The model is built upon a lattice kinetic Monte Carlo algorithm (LKMC) to track platelet motion and binding. First, a new method for including flow-driven particle motion in LKMC was derived from a timescale analysis of particle motion. Simple methods for simulating flow-driven motion were found to exhibit concentration dependent velocities violating the assumptions in the model. The nature of the error was analyzed mathematically and resolved by considering the chain length distribution on the lattice. The accuracy of the method was found to scale linearly with the lattice spacing. Second, the LKMC method was extended to study particle aggregation in complex flows. The LKMC results for simple flows were compared directly to a continuum population balance equation (PBE) approach. A contact time model was introduced to capture nonideal collisions in the LKMC model and a connection to the continuum collision efficiency was derived. The particle size distribution for a baffled geometry with regions of standing vortices and squeezing flows was determined using the LKMC method for varying baffle heights. Finally, the LKMC method was incorporated within a multiscale model to simulate platelet aggregation including platelet signaling (neural network model), blood flow (lattice Boltzmann method), and the release of soluble platelet agonists (finite element method). The neural network model for platelet signaling was trained on patient-specific, experimental measurements of intracellular calcium enabling patient-specific predictions of platelet function in flow. The model accurately predicted the order of potency for three antiplatelet therapies, donor-specific aggregate size, and donor-specific response to antiplatelet therapy as compared to microfluidic experiments of platelet aggregation.

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