The long term goal of this project is to develop state of the art computational models of thrombosis in order to explore how physical and chemical factors interact to determine its progression. An integral component of the developmental work will be the experimental validation of the models using well-established perfusion systems and confocal microscopy for determining the characteristics of thrombus deposition. Specifically, 3D computational models of i) platelet mural thrombosis in small diameter blood vessels (arterioles), and ii) combined platelet thrombosis and coagulation in small vessels (arterioles and venules) will be developed. The models will be designed to take full advantage of high performance parallel computing capabilities as well as innovative mathematical and computational methods and will permit exploration of the complex, dynamic, and multiscale interplay between flow, chemistry and vascular biology in thrombosis. Through a Consortium arrangement with Dr. Vincent Turitto and his thrombosis group at the Illinois Institute of Technology, experiments will be conducted to provide the necessary experimental data. Modification of the models will occur through close interaction between the computational and experimental groups, and with the assistance of an Advisory Group of thrombosis experts. The project thus brings together a unique cross-disciplinary team of mathematicians, computational scientists, biomedical engineers and life scientists in a well integrated computational and experimental effort to understand intravascular thrombosis and to develop reliable models for predicting the course of thrombotic events that can help in designing improved medical devices and therapies to prevent and treat thrombosis.
The project seeks to develop and validate computational models to simulate blood clotting inside of blood vessels. These models will be used to increase our understanding of the formation of intravascular blood clots including those that cause heart attack and stroke and improved understanding may lead to improved treatment.
Showing the most recent 10 out of 13 publications
