Tri-Institutional Training Program in Computational Biology and Medicine

描述（由申请人提供）：计算生物学和医学的三机构培训计划（CBM）利用位于纽约州伊萨卡的康奈尔大学、纽约市的威尔康奈尔医学院和斯隆-凯特林研究所（纪念斯隆-凯特林癌症中心的研究机构）的优秀教育和研究资源，培训计算生物学家，让他们用跨学科的方法来解决生物学和医学的复杂问题。CBM培训环境旨在解决培训该领域科学家的独特挑战，其特点是：定量和生物科学课程；研究轮转，使充分知情的论文选题；培训生研究进展系列研讨会，以加强项目凝聚力和培养相关学科的流畅性；在不同的基础到转化计算和实验实验室进行论文研究；定量和生物指导，确保培训的平衡性和广度；并鼓励参与混合计算/实验项目，以促进理论与实验之间的联系。所有项目组成部分的一个基本主题是让学员接触到计算生物学的不同方法和领域，这反映了跨学科方法对于解决当今生物学和医学中的许多问题是无价的。该项目已进入第10个年头，在培训方面取得了越来越多的成功。有了这个竞争性的更新提案，我们要求将T32插槽从6个增加到12个，我们认为这是合理的，因为我们有大量高素质的培训资助合格申请人，以及大量前沿论文研究机会和一流的教师

DESCRIPTION (provided by applicant): The Tri-Institutional Training Program in Computational Biology and Medicine (CBM) takes advantage of the outstanding educational and research resources of Cornell University in Ithaca, NY, the Weill Cornell Medical College in New York City, and the Sloan-Kettering Institute (the research arm of the Memorial Sloan-Kettering Cancer Center) to train computational biologists in the interdisciplinary approaches they need to solve the complex problems that characterize biology and medicine. The CBM training environment is designed to address the unique challenges of training scientists in this area and is characterized by: coursework in both quantitative and biological sciences; research rotations to enable a well-informed thesis topic selection; trainee Research-in-Progress seminar series to enhance program cohesion and foster fluency in relevant disciplines; thesis research in one of a diverse array of basic to translational computational and experimental laboratories; both quantitative and biological mentorship to ensure training balance and breadth; and encouragement to engage in hybrid computational/experimental projects to foster connections between theory and experiment. A theme underlying all program components is trainee exposure to diverse approaches and areas of computational biology, a reflection of the fact that interdisciplinary approaches are invaluable for solving many problems in biology and medicine today. The program, which is well established in its 10th year, has an expanding record of training success. With this competitive renewal proposal, we are requesting an increase from 6 to 12 T32 slots, which we believe is justified by the deep pool of highly qualified training-grant eligible applicants, large array of cutting-edge thesis research opportunities with leading faculty scientists, and the training enrichment that is inherent to an increased critical mass of students. The requested T32 funding would greatly aid the CBM program in continuing to achieve its mission of training the next generation of scientists to use computational and analytical methods to solve complex problems in biology. It is our belief that the development of such a cadre of computational biologists will foster discovery in frontiers of basic biological and biomedical sciences.