MIT/Whitehead/Broad Computational Genetics Training Program

描述（由申请人提供）：我们建议建立一个新的跨学科的研究培训计划，在计算遗传学之间的合作努力，麻省理工学院，怀特黑德研究所，麻省理工学院和哈佛的广泛研究所。该计划的目标是培养麻省理工学院的学生成为有效的跨学科科学家，与生物学家合作开发新的算法，工具和方法，用于分析基因组和遗传数据，并以原则性预测模型的形式表达这种分析。该计划的教师将包括五名麻省理工学院EECS和数学教师，四名怀特黑德教师，以及四名麻省理工学院和哈佛的布罗德研究所成员。该计划的主要研究学科包括：1）开发新的方法和算法，用于分析来自基因组学和遗传学的实验和研究数据; 2）基于过去数据的研究原则设计方法; 3）构建解释复杂表型和生物现象的计算模型; 4）以及解释与人类健康和疾病相关的基因组，遗传和临床数据的方法的发展。 建议在该方案中支持四名博士前学员，每人为期两年（共8个名额）。我们已经在这一领域开展了七年多的培训计划，迄今为止，我们的学生已经为该领域做出了重大贡献。在我们最近的毕业生是教师在斯坦福大学，伯克利，华盛顿，普林斯顿大学，杜克大学，和CMU。我们的申请人库是异常强大，在2008年有592名申请人在计算机科学的相关子领域。我们建议的研究培训计划的学员将从麻省理工学院计算机科学研究生课程中获得非常严格的技术和定量基础，结合正式的跨学科课程工作以及计算机科学和生物学教师之间的共同导师安排。目前在我们的博士前学生强大的技术技能提供了一个很好的基础，创造突破性的新方法和算法在计算遗传学。 公共卫生宣传--我们将培养能够发现遗传信息与人类疾病风险之间联系的科学家。这些研究可以为某些疾病提供适当的治疗方法，并为开发新的治疗方法提供线索。随着全基因组关联研究的数据越来越多，我们预计遗传信息将成为预防医学的重要组成部分。

DESCRIPTION (provided by applicant): We propose to establish a new interdisciplinary research training program in Computational Genetics as a collaborative effort between MIT, the Whitehead Institute, and the Broad Institute of MIT and Harvard. The goal of this program is to train MIT students to be effective interdisciplinary scientists, working as team members with biologists to develop new algorithms, tools, and approaches for analyzing genomic and genetic data and expressing this analysis in the form of principled predictive models. The program faculty will consist of five MIT EECS and Mathematics faculty, four Whitehead faculty members, and four members of the Broad Institute of MIT and Harvard. The major research disciplines of this program include: 1) the development of new approaches and algorithms for the analysis of data from genomics and genetics based experiments and studies; 2) approaches for the principled design of studies based upon past data; 3) the construction of computational models that explain complex phenotypes and biological phenomenon; 4) and the development of approaches for interpreting genomic, genetic, and clinical data relevant to human health and disease. It is proposed that four pre-doctoral trainees be supported in this program, each for a period of two years (a total of 8 slots). We have been running a training program in this area for over seven years, and our students to date have made substantial contributions to the field. Among our recent graduates are faculty at Stanford, Berkeley, Univ. of Washington, Princeton, Duke, and CMU. Our pool of applicants is unusually strong, with 592 applicants in 2008 in relevant sub-areas of Computer Science. Trainees in our proposed research training program will have a very rigorous technical and quantitative foundation from the MIT graduate program in Computer Science, combined formal interdisciplinary course work and a co mentorship arrangement between a Computer Science and a Biology faculty member. The strong technical skills present in our pre doctoral students have provided an excellent foundation for the creation of ground breaking new approaches and algorithms in Computational Genetics. PUBLIC HEALTH REVELANCE - We will train scientists who can discover links between genetic information and risks for human disease. These studies can suggest appropriate therapies for certain diseases and give clues towards the development of new therapeutics. As more data form Genome Wide Association Studies becomes available, we expect that genetic information will become an important component of preventative medicine.