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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.

项目概要（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计划需要熟练掌握生物数据（实验数据生成，生物信息学和统计推断）与生命系统相关的理论（分析建模，计算模拟和进化论）。这种二分法是具有挑战性的，以解决在博士培训：生物学学生很少接受培训，以发展或批评新的定量方法，定量分析生物数据的学生很少深入研究生物数据生成迫切需要遏制各自为政的努力，以应对这些挑战，相反，开发一个集中的社区和培训计划，专注于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的模型，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形”博士预科生-在一个学科中具有优势-成为“π形”生物数据科学家有两个核心优势：（1）生成和分析生物数据，（2）开发理论模型，和关于生物过程的可检验的假设。布朗大学的这个集中社区将是由28名敬业的跨学科教师导师维护，他们将指导四名NIH支持的博士预科生每年沿着4名布朗大学支持的受训人员（产生40个生物数据 5年以上的科学家）在各种教学，研究和职业发展活动一年。这些活动将包括为期一年的研究生研讨会、跨学科研究轮换、项目务虚会为教师和学员，并举行了一系列圆桌讨论，重点是专业发展，跨学科研究人员。由此产生的社区将促进技能的发展，跨学科的生物医学研究，包括沟通科学的能力，以广泛和领域，特定的受众，浏览跨学科合作和赠款申请，面试学术和以行业为基础的研究事业，并进行可复制和开放的科学。教师研究课程涵盖多种生物有机体，系统和问题，包括进化遗传学，功能基因组学，生物网络，衰老分子生物学，发育稳健性，生物医学信息学、免疫调节和生物物理学。此外，导师们每年都有一个超过1200万美元的直接成本的研究资金基础，提供了一个强大的基础，以支持这一创新培训计划。这项培训计划将使调查人员能够从生活中提取新的见解从复杂的生物数据集。