Tri-Institutional Training Program in Computational Biology and Medicine

描述（申请人提供）：计算生物学和医学（CBM）的三机构培训计划利用了纽约州伊萨卡市康奈尔大学，纽约市威尔康奈尔医学院，以及斯隆-凯特琳研究所（纪念斯隆-凯特琳癌症中心的研究部门）培养跨学科方法的计算生物学家，他们需要解决生物学和医学的复杂问题。CBM培训环境旨在解决在这一领域培训科学家的独特挑战，其特点是：定量和生物科学课程;研究轮换，使一个知情的论文选题;实习生研究进展研讨会系列，以提高程序的凝聚力和促进相关学科的流畅性;论文研究在一个基础的各种各样的转化计算和实验实验室之一;定量和生物导师，以确保培训的平衡和广度;并鼓励从事混合计算/实验项目，以促进理论和实验之间的联系。所有计划组成部分的主题是学员接触不同的方法和计算生物学领域，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非常宝贵的事实。该计划，这是建立在其第10个年头，有一个不断扩大的培训成功的记录。有了这个有竞争力的更新建议，我们要求从6个增加到12个T32插槽，我们认为这是合理的高素质的培训补助金合格的申请人的深池，大量的尖端论文研究机会与领先的教师 科学家，和培训丰富，这是固有的增加临界质量的学生。 要求的T32资金将大大有助于CBM计划继续实现其使命，即培训下一代科学家使用计算和分析方法解决生物学中的复杂问题。我们相信，这样一批计算生物学家的发展将促进基础生物和生物医学科学前沿的发现。

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.