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的博士生设计了一项培训计划。 一旦很明显，现代生物学将越来越依赖定量方法，就出现了几种训练方法，主要是必要的，而不是计划。 其中一些是作为一个领域的教学专家的特征，而当该领域变化以迅速培训一个尚未结晶的全新领域的新研究人员时，该领域的能力很重要。 该活动仍然很重要，但不再足够。 在学术数学部门的传统背景下运作的数学家将继续学习生物学并为其发展做出贡献。实验室生物学家将同样获得使用基因组技术所需的统计和计算技术的能力。 但是，我们深信三件事：1）在现场最大的贡献将由一个在计算生物学上接受培训的新型研究人员本质上，2）开始，开始这样的训练工作的时间已经到来，大量非常聪明的本科生在数学和类似的跨学科培训方面，比起其他多重的人，比这两次跨学科的人都更加多地追求这两次培训，这可能是更多的数学习惯，这是3）。很少见，这种数学培训需要在学生和老师的各个部分上进行大量努力。 因此，我们的计划基于统计和计算机科学的坚实基础，同时将直接集成到实验生物学实验室，临床研究计划和生命科学文化中。