Computational Applications in Diabetes and Endocrinology

点击翻译按钮获取中文摘要

DESCRIPTION (provided by applicant): We propose to develop a cross-disciplinary NIDDK educational program in Computational Applications in Diabetes and Endocrinology that integrates quantitative and computational strategies with diabetes and endocrine research in undergraduate, graduate, and postgraduate curricula at the University of Virginia (UVA). Development and implementation of an undergraduate summer research experience will be assisted by three consultants - undergraduate math and biology faculty at Sweet Briar College (SBC; a small, technologically-oriented undergraduate women's college near UVA) with whom the program directors have a productive, long-standing relationship re: educational development activities. The goal is a unique, innovative, curriculum-driven program that creates educational opportunities to attract students and fellows to careers in biomedical diabetes/endocrine research, with particular emphasis on quantitative applications. In this way, we hope to target historically under-represented disciplines in biomedical research in diabetes and endocrinology (math, stats, engr, comp sci, etc.). A broad array of independently-funded projects in diabetes/endocrine research at UVA [spanning a range of hierarchical levels of organization: (i) sub-cellular/cellular; (ii) hormonal-patterning; (iii) endocrine-network; (iv) human, macroscopic; and (v) biobehavioral] will serve as content basis for the educational modules developed in this program. Students will derive unique benefit from attempts to replicate challenges presented by contemporary research, thereby providing skills needed to transition from undergraduate classroom to graduate education to professional career and beyond. The instructional format will be modular, based on individual diabetes/endocrine research projects/questions. Tangible deliverables we propose to develop: (i) undergraduate research experience modules with problems sets, laboratory and project activities, and instructor lesson plans; (ii) advanced, graduate-level textbook, "Quantifying Diabetes: Data Analysis and Modeling Methods for Diabetes Research and Clinical Practice;" (iii) corresponding interactive CDs; and (iv) interactive web site providing links between textbook/CD content and research projects, and updated tutorial materials. Eventually, we plan to offer formal degree subspecializations in Biomathematics (BS/MS) and Computational Diabetes and Endocrinology (MS/PhD)