A Computational Framework to Understand Vascular Adaptation
| dc.contributor.advisor | Garbey, Marc | |
| dc.contributor.committeeMember | Berceli, Scott A. | |
| dc.contributor.committeeMember | Tsekos, Nikolaos V. | |
| dc.contributor.committeeMember | Gabriel, Edgar | |
| dc.contributor.committeeMember | Hilford, Victoria | |
| dc.creator | Rahman, Mahbubur 1982- | |
| dc.date.accessioned | 2017-08-14T17:23:32Z | |
| dc.date.available | 2017-08-14T17:23:32Z | |
| dc.date.created | May 2015 | |
| dc.date.issued | 2015-05 | |
| dc.date.submitted | May 2015 | |
| dc.date.updated | 2017-08-14T17:23:32Z | |
| dc.description.abstract | Researchers have been continuously applying a wide variety of approaches to understand vascular adaptation over the past two decades. However, the specific cause/effect or links between the hemodynamic factors, inflammatory biochemical mediators, cellular effectors and vascular occlusive phenotype remain unexplained still today. To explain these biological phenomena, we have introduced a multi-scale computational framework to systematically test many hypotheses associated with the vascular adaptation and finally applied this framework to explain some widely observed clinical and experimental cases. Our framework incorporates the cellular activities inside the vein graft influenced by the shear stress and tension, which are two of the most important environmental factors in the vascular adaptation. This is a hybrid agent based model (ABM) coupled with the partial differential equations (PDEs) associated with the calculation of the shear stress. Based on the computational framework, we have designed and developed a modular, adaptive, efficient and scalable simulation program so that we can explain some specific pattern formations associated with the vascular adaptation by pattern recognition algorithms of the framework in real time. Finally, we have coupled a genetic algorithm with the framework to verify the fact that a combination of interesting patterns associated with the vascular adaptation can be regenerated in a multivariate data analysis environment. As a result, this research will reduce the gap in understanding different cases observed in the vascular adaptation. | |
| dc.description.department | Computer Science, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Portions of this document appear in: Garbey, Marc, Mahbubur Rahman, and S. Berceli. "A multiscale computational framework to understand vascular adaptation." Journal of computational science 8 (2015): 32-47. https://doi.org/10.1016/j.jocs.2015.02.002 | |
| dc.identifier.uri | http://hdl.handle.net/10657/2016 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | Vascular adaptation | |
| dc.subject | Intimal hyperplasia | |
| dc.subject | Medial hyperplasia | |
| dc.subject | Phenotype | |
| dc.title | A Computational Framework to Understand Vascular Adaptation | |
| dc.type.dcmi | text | |
| dc.type.genre | Thesis | |
| thesis.degree.college | College of Natural Sciences and Mathematics | |
| thesis.degree.department | Computer Science, Department of | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |