Stochastic processes in biological and biochemical kinetics / Ian J. Laurenzi.

Format:

Book

Manuscript

Microformat

Thesis/Dissertation

Author/Creator:

Laurenzi, Ian J.

Contributor:

Diamond, Scott L., advisor.

University of Pennsylvania.

Language:

English

Subjects (All):

Penn dissertations--Chemical engineering.

Chemical engineering--Penn dissertations.

Local Subjects:

Penn dissertations--Chemical engineering.

Chemical engineering--Penn dissertations.

Physical Description:

ix, 182 pages : illustrations ; 29 cm

Production:

2002.

Summary:

The aggregation of platelets with neutrophils is the primary and critical event in both normal blood clotting and pathological thrombosis. Cellular aggregation is a two part process. First, cells must collide with each other, usually as a result of their relative positions in the blood stream. Subsequently, they adhere to each other using chemical tethers formed from macromolecules attached to their surfaces. Complicating the analysis of these steps, collision and tether formation, are their probabilistic natures. The goal of this thesis dissertation is to quantitatively and probabilistically characterize the physical and chemical interactions between blood cells, and develop exact methods of predicting the time-evolution of blood cell aggregation in flow. To this end, the stochastic approach to chemical kinetics was employed to quantify the rates and probabilities of receptor-mediated adhesion between surfaces. Subsequently, a stochastic approach to aggregation kinetics was employed to produce a novel and exact simulation algorithm to predict the time-evolutions of random aggregations of particles composed of multiple components. We then employ these methods to quantify the aggregation of fully-activated platelets and neutrophils in linear shear flow. Finally, we develop a genetic algorithm for the deconvolution of aggregation models from experimental aggregometry experiments. Our methods and analysis motivate new experimental approaches to studies of biological aggregation processes, and demonstrate that computational approaches can predict the temporal hematological performance of human blood from a probabilistic perspective.

Notes:

Supervisor: Scott L. Diamond.

Thesis (Ph.D. in Chemical Engineering) -- University of Pennsylvania, 2002.

Includes bibliographical references.

Local Notes:

University Microfilms order no.: 3073024.

OCLC:

244972866