Graduate Training in Computational and Systems Biology

描述（由申请人提供）：目标是继续发展麻省理工学院计算与系统生物学（CSB）的跨学科博士前培训计划，该计划将学生培养成为生物学，计算和工程界面的生物医学研究的领导者。CSB是麻省理工学院为对计算和系统生物学感兴趣的学生提供的主要培训项目，也是唯一一个强调该领域跨学科培训和研究的项目。项目师资集中在这三所学校

DESCRIPTION (provided by applicant): The objective is to continue development of MIT's interdisciplinary predoctoral training program in Computational and Systems Biology (CSB), which trains students to become leaders in biomedical research at the interface of biology, computation and engineering. CSB is the primary training program at MIT for students interested in computational and systems biology and is the only program that emphasizes interdisciplinary training and research in the field. Program faculty are concentrated in the three founding departments - Biological Engineering (BE), Biology, and Electrical Engineering & Computer Science (EECS) - with additional involvement of faculty from other departments. Training faculty research interests span a wide range of CSB- related areas including computational molecular biology/regulatory genomics and evolution, molecular and cellular biophysics, systems biology of cancer and other diseases, proteomics, microbial ecology, bioimaging, protein design/engineering, synthetic biology, microfluidics/Bio-MEMS, and toxicogenomics. This proposal seeks to expand the pool of training faculty significantly, including 9 faculty newly hired in the past 5 years who have active research programs in the field. Students apply directly to the CSB Ph.D. program from their undergraduate or Master's institution and receive multi- and inter-disciplinary training in the field of computational and systems biology. The proposal seeks 12 slots to support 6 students for the first two years of the Ph.D., enabling extended research rotations and participation in special program activities. Unique aspects of the program include: (a) unusually diverse collection of research areas across science and engineering, with highly collaborative interdisciplinary faculty; (b) a unique core of recently developed, interdisciplinary classroom subjects that combine biology, engineering, statistics and computation; (c) intensive advising and multi- disciplinary thesis committees to optimize the training experience for students from diverse academic backgrounds; (d) an annual retreat with participation of students and faculty focusing on research, leadership, and challenges to interdisciplinary research.