Dr. Majid Jaberi-Douraki
Experimental approaches are conventionally used to investigate the highly nonlinear epigenetic alterations that lead to abnormal cell growth and tumors. But with the emergence of computational and mathematical biology, it is now practical to employ quantitative in silico methods, in parallel with experimental techniques, to increase understanding of tumor growths and maximize the effectiveness of potential anti-cancer drugs.
Type 1 diabetes (T1D) in both humans and nonobese diabetic mice is a prototypic organ-specific autoimmune disease that our team is targeting. Studies have shown that patients with diabetes have significantly higher cancer incidence, and diabetes may be a risk factor or symptom of pancreatic cancer. Based on these studies, pancreatic cancer is more likely to occur in patients who have prolonged enduring diabetes (such as T1D) than in patients who do not have diabetes. The complexity of the processes underlying this disease and the difficulty of examining them experimentally make the use of quantitative approaches very compelling. My work involves modeling and developing useful computer and mathematical tools such as GUI and mathematical models to analyze and predict the distribution of new, effective anti-cancer drugs and nanomaterial behavior across multiple animal species and humans.