MIT/Whitehead/Broad Computational Genetics Training Program

参见说明）： 我们建议建立一个新的跨学科的研究培训计划，在计算遗传学作为一个合作 麻省理工学院，怀特黑德研究所，麻省理工学院和哈佛的布罗德研究所之间的努力。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，作为团队成员与生物学家一起开发新的 用于分析基因组和遗传数据的算法、工具和方法，并以 原则性预测模型。该计划的教师将包括五个麻省理工学院EECS和数学教师，四个 怀特黑德的教职员工，以及麻省理工学院和哈佛大学布罗德研究所的四名成员。的主要研究 该计划的学科包括：1）开发用于分析数据的新方法和算法， 基于基因组学和遗传学的实验和研究; 2）基于基因组学和遗传学的研究原则设计方法 过去的数据; 3）解释复杂表型和生物现象的计算模型的构建; 4） 以及开发用于解释与人类健康相关的基因组、遗传和临床数据的方法， 疾病 建议在该计划中支持四名博士前学员，每人为期两年（共8人 插槽）。我们已经在这一领域开展了七年多的培训计划，迄今为止，我们的学生已经取得了 对这一领域的重大贡献。在我们最近的毕业生是教师在斯坦福大学，伯克利分校， 华盛顿，普林斯顿，杜克和卡内基梅隆大学。我们的申请人库异常强大，2008年有592名申请人， 计算机科学的相关子领域。在我们拟议的研究培训计划的学员将有一个非常严格的 麻省理工学院计算机科学研究生课程的技术和定量基础，结合形式 跨学科课程工作和计算机科学和生物学教师之间的共同导师安排 会员是否在我们的博士生预科学生目前强大的技术技能提供了一个很好的基础， 在计算遗传学领域开创性的新方法和算法。 相关性（参见说明）： 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，并为新疗法的开发提供线索。作为 更多来自全基因组关联研究的数据可用，我们预计遗传信息将成为 预防医学的重要组成部分。

See instructions): We propose to establish a new interdisciplinaryresearch trainingprogram 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 interdisciplinaryscientists, 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 explaincomplex 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 runninga training program in this area for over seven years, and our students to date have made substantial contributionsto 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 quantitativefoundation 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. RELEVANCE (See instructions): We will train scientists who can discover links between genetic informationand 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.