DATA-DRIVEN AND KNOWLEDGE-DRIVEN COMPUTATIONAL MODELS OF ANGIOGENESIS IN APPLICATION TO PERIPHERAL ARTERIAL DISEASE

Bioinformatics;peripheral arterial disease;biomarker;genomics;proteomics;drug repositioning;compartmental model;Biomedical Engineering

Chu, Liang-HuiBader, Joel S. ;

Johns Hopkins University

关键词: Bioinformatics;    peripheral arterial disease;    biomarker;    genomics;    proteomics;    drug repositioning;    compartmental model;    Biomedical Engineering;   

Others  :  https://jscholarship.library.jhu.edu/bitstream/handle/1774.2/37887/CHU-DISSERTATION-2015.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: JOHNS HOPKINS DSpace Repository

【 摘 要 】

Angiogenesis, the formation of new blood vessels from pre-existing vessels, is involved in both physiological conditions (e.g. development, wound healing and exercise) and diseases (e.g. cancer, age-related macular degeneration, and ischemic diseases such as coronary artery disease and peripheral arterial disease). Peripheral arterial disease (PAD) affects approximately 8 to 12 million people in United States, especially those over the age of 50 and its prevalence is now comparable to that of coronary artery disease. To date, all clinical trials that includes stimulation of VEGF (vascular endothelial growth factor) and FGF (fibroblast growth factor) have failed. There is an unmet need to find novel genes and drug targets and predict potential therapeutics in PAD. We use the data-driven bioinformatic approach to identify angiogenesis-associated genes and predict new targets and repositioned drugs in PAD. We also formulate a mechanistic three- compartment model that includes the anti-angiogenic isoform VEGF165b. The thesis can serve as a framework for computational and experimental validations of novel drug targets and drugs in PAD.