Thursday, April 25, 2024
4/25/2024
DESCRIPTION (provided by applicant): A Computational Biology and Biomedical Genomics Training Program at The Georgia Institute of Technology This T32 Proposal would establish a Ruth Kirschstein Institutional Training Program in Bioinformatics and Computational Biology at Georgia Tech. It builds on the solid foundation of over a decade of graduate training in Bioinformatics, as well as existing strengths in systems biology and computational modeling allied to genomics through one of the nation's leading Biomedical Engineering departments. Funding is requested to support two new Ph.D. students per year for five years, providing stability and supporting innovation during the crucial second and third years in the program when they are transitioning from course work to independent research. Five key features of the program are (i) the establishment of a computational genomics training program for the south-eastern United States, (ii) a broad base for recruitment across Biology, Bio-Engineering, and Health Systems statistical research, (iii) a highly interdisciplinary series of courses integrating bioinformatics, computational genomics, multivariate statistics, quantitative genetics, molecular evolution, and systems modeling, (iv) excellence in under- represented minority recruitment and graduation with over 15% of the student body African American or Hispanic, and (v) establishment of one of the first training programs specifically oriented to predictive health genomics, also linked to a long-term vision for Masters-level training of clinical genomicists. The trainees will be selected on a competitive basis from an expected pool of ten candidates per year spread across 23 mentor's research groups. Each year's cohort in the Computational Biology and Biomedical Genomics program will take an introductory two-semester ground course and core classes in computational theory and methods, then specialize in bioinformatics, systems biology, or predictive health. Students will graduate with a balance of knowledge across the fundamentals of computational genetics, molecular/cell biology and evolutionary theory, while performing research that in many cases is oriented toward translational research in bioengineering and health care innovation.
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