Predoctoral Training Program in Biological Data Science at Brown University

布朗大学生物数据科学博士前培训项目

基本信息

批准号：
10447019
负责人：
Sohini Ramachandran
金额：
$ 31.09万
依托单位：
BROWN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10447019
关键词：
Biological Data Science Training Programs Universities pre-doctoral

项目摘要

PROJECT SUMMARY (1 page/30 lines). In this era of Big Data, building a successful and independently funded biomedical research program requires fluency in both biological data (experimental data generation, bioinformatics, and statistical inference) and theory relevant to living systems (analytical modeling, computational simulation, and evolutionanry theory). This dichotomy is challenging to address in doctoral training: biology students are rarely trained to develop or critique new quantitative methods, and quantitative students analyzing biological data rarely gain depth in biological data generation. There is an urgent need to curb fragmented efforts to address these challenges, and to instead develop a centralized community and training program focused on fostering Biological Data Scientists: scientists whose research leverages observed patterns in biological data to generate new models and hypotheses for biological processes and systems. The objective of this Predoctoral Training Program in Biological Data Science at Brown University is to turn “I-shaped” predoctoral students — with strength in one discipline — into “pi-shaped” Biological Data Scientists with two core strengths: (1) generating and analyzing biological data, and (2) developing theoretical models for and testable hypotheses regarding biological processes. This centralized community at Brown University will be maintained by 28 engaged, crossdisciplinary faculty preceptors who will mentor four NIH-supported predoctoral trainees each year along with 4 Brown University-supported trainees each year (resulting in 40 Biological Data Scientists over 5 years) in a variety of didactic, research, and career development activities for one year. These activities will include a new year-long graduate seminar, crossdisciplinary research rotations, a program retreat for faculty and trainees, and a series of roundtable discussions focusing on professional development for interdisciplinary researchers. The resulting community will promote the development of skills essential for interdisciplinary biomedical research, including the ability to communicate science to both broad and field- specific audiences, navigate interdisciplinary collaboration and grant applications, interview for academic and industry-based research careers, and conduct reproducible and open science. The faculty preceptors' research programs cover multiple biological organisms, systems, and problems, ranging across evolutionary genetics, functional genomics, biological networks, molecular biology of aging, developmental robustness, biomedical informatics, regulation of immunity, and biological physics. Further, the preceptors have a combined annual research funding base of over $12 million in direct costs, offering a strong foundation to bolster this innovative training program. This training program will yield investigators equipped to extract new insights into living systems from complex biological datasets.

项目摘要（1 页/30 行）。在这个大数据时代，建立一个成功的、独立资助的生物医学研究项目要求精通生物数据（实验数据生成、生物信息学和统计推断）以及与生命系统相关的理论（分析建模、计算模拟和进化理论）。这种二分法在博士培训中很难解决：生物学学生很少接受过开发或批评新的定量方法，分析生物数据的定量学生很少能深入了解迫切需要遏制分散的努力来应对这些挑战，并且相反，开发一个集中的社区和培训计划，重点培养生物数据科学家：科学家的研究利用生物数据中观察到的模式来生成新模型生物过程和系统的假设。布朗大学生物数据科学博士前培训计划的目标是将 “I型”博士前学生——在某一学科上有优势——成为“pi型”生物数据科学家具有两个核心优势：（1）生成和分析生物数据，以及（2）开发理论模型以及有关生物过程的可检验假设。布朗大学的这个集中社区将是由 28 名跨学科教师导师维护，他们将指导 4 名 NIH 支持的博士前教师每年 4 名受训人员以及每年 4 名布朗大学支持的受训人员（产生 40 个生物数据）超过 5 年的科学家）从事一年的各种教学、研究和职业发展活动。活动将包括为期一年的研究生研讨会、跨学科研究轮换、项目务虚会针对教师和学员的圆桌讨论，以及一系列重点关注专业发展的圆桌讨论由此产生的跨学科研究人员社区将促进必要技能的发展。跨学科生物医学研究，包括向广泛和现场传播科学的能力特定受众，导航跨学科合作和资助申请，学术和学术界的面试以行业为基础的研究事业，并进行可重复和开放的科学研究。项目涵盖多种生物有机体、系统和问题，涵盖进化遗传学、功能基因组学、生物网络、衰老分子生物学、发育稳健性、生物医学此外，导师们还有一个年度综合课程。直接成本超过 1200 万美元的研究资助基础，为支持这一创新提供了坚实的基础该培训计划将使研究人员能够获得对生活的新见解。来自复杂生物数据集的系统。