BIOINFORMATIC and COMPUTATIONAL BIOLOGY TRAINING PROGRAM

生物信息学和计算生物学培训计划

• 批准号: 7254253
• 负责人: Thomas B Kepler
• 金额: $ 17.98万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7254253
• 关键词: Bioinformatics; Computational Biology; Training Programs

DESCRIPTION (provided by applicant): We have designed a training program for PhD students in Bioinformatics, Genome Technology and Computational Biology. Once it became apparent that modern biology would be increasingly dependent on quantitative methods, several approaches to training arose, largely as a matter of necessity rather than planning. Some of these, characterizes as teaching experts in one field the competencies of the other, were important when the field was changing to quickly to train a new cadre of researchers in a completely new field that had not yet been crystallized. This activity remains important, but is no longer sufficient. Mathematicians operating in the traditional context of academic mathematics departments will continue to learn biology and make contributions to its development. Laboratory biologists will similarly acquire competence in the statistical and computational techniques necessary to make use of genome technologies. But we are convinced of three key things: 1) that the greatest contributions in the field will be made by a new breed of researcher trained in computational biology per se, 2) that the time to begin such a training effort has arrived, with a substantial number of very bright undergraduates proactively pursuing double majors in the mathematical and biological sciences or similar interdisciplinary education, 3) perhaps more controversial than the other two, that mathematical talent is relatively rare, and that mathematical training requires substantial effort on the parts of both student and teacher. Thus, our program builds upon a solid foundation in statistics, and computer science, while providing direct integration into experimental biology laboratories, clinical research programs, and the culture of the life sciences.

描述（由申请人提供）：我们为生物信息学，基因组技术和计算生物学博士生设计了一个培训计划。 一旦现代生物学越来越依赖于定量方法，几种训练方法就出现了，主要是出于必要而不是计划。 其中一些，作为教学专家在一个领域的其他能力的特点，是重要的，当该领域正在迅速变化，以培养一个全新的领域，尚未结晶的研究人员的新干部。 这项活动仍然重要，但已经不够。 在传统的学术数学系中工作的数学家将继续学习生物学，并为生物学的发展做出贡献。实验室生物学家将同样获得利用基因组技术所必需的统计和计算技术的能力。 但我们确信三个关键点：1）在该领域的最大贡献将由一个新的研究人员在计算生物学本身的训练，2）时间开始这样的培训工作已经到来，与大量的非常聪明的本科生积极追求双专业的数学和生物科学或类似的跨学科教育，3）也许比其他两个更有争议的是，数学天赋相对罕见，数学训练需要学生和教师双方的大量努力。 因此，我们的计划建立在统计学和计算机科学的坚实基础上，同时提供直接整合到实验生物学实验室，临床研究计划和生命科学文化中。