Marc Garbey's interdisciplinary collaboration with University of Florida on the multiscale modeling of endovascular diseases has been sponsored by a Research Project Grant Program (RO1) from the National Institutes of Health. Using state-of-the-art techniques in mathematics, engineering, and computer science to integrate fundamental biologic and physical data, a predictive model of vascular adaptation following acute intervention is going under extensive development. This work is sponsored by a four year NIH grant (RO1, 4 year budget excluding overhead $ 1000 K) entitled Multiscale Network Modeling of Hemodynamic Driven Vascular Adaptation.
Some of the preliminary work by the team of that project that comprises Pr. Scott Berceli (PI Department of Surgery, University of Florida), Pr. Roger Tran-Son-Tay (MAE – University of Florida) and Marc Garbey from CS@UH, has been published in recent issues of the Annals of Biomedical Engineering and the journal of the international Society for Vascular Surgery in 2008.
Arterial occlusive pathologies, manifested predominantly through myocardial, cerebrovascular, and lower extremity ischemia, continue to be the leading cause of mortality and morbidity in the United States. Even though significant advances in surgical techniques and endovascular therapies have been achieved over the last decade, long-term success in arterial revascularizations has been limited. Although bypass grafts and transluminal angioplasties can provide immediate and dramatic improvements in perfusion, the half-life of these interventions is relatively short, and continues to be measured in months. Vascular adaptation following local injury occurs through a combination of intimal hyperplasia and wall (inward/outward) remodeling. Over the past two decades, researchers have applied a wide variety of approaches to investigate neointimal hyperplasia and vascular remodeling in an effort to identify novel therapeutic strategies. However, despite incremental progress over these decades, specific cause/effect links between hemodynamic factors, inflammatory biochemical mediators, cellular effectors, and vascular occlusive phenotype remain lacking.
Prior strategies have focused largely on linear models to separately describe the physical or biologic components of vascular disease progression. In order to significantly advance our understanding of the function of such complex phenomena, it is necessary to integrate different types of data and use quantitative models to predict behavior and outcomes. Our multidisciplinary team approach uses both experimental data and computational models to understand these dynamic phenomena, and most importantly to predict outcomes to specific perturbations. Such information is vital for translation to effective clinical strategies to enhance revascularization durability. Through use of these cutting edge technologies, we combine the physical and biologic microenvironments via multi-scale mathematical modeling to predict dynamic stability or progression towards an occlusive vascular phenotype following acute perturbation.