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

• 批准号: 10197955
• 负责人: Sohini Ramachandran
• 金额: $ 29.26万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 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/10197955
• 关键词: Biological; Data Science; Training Programs; Universities; pre-doctoral

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，建立了成功且独立的生物医学研究计划 需要流利的生物学数据（实验数据生成，生物信息学和统计推断） 和与生活系统有关的理论（分析建模，计算模拟和进化理论）。 在博士培训中，这种二分法具有挑战性：生物学学生很少受过培训以发展或 批评新的定量方法，定量学生分析生物学数据很少获得深度 生物数据生成。迫切需要遏制零散的努力来应对这些挑战，并 相反，开发一个集中的社区和培训计划，旨在培养生物数据科学家： 科学家的研究利用观察到生物数据中的模式来生成新模型和 生物过程和系统的假设。 布朗大学生物数据科学的这项综合培训计划的目的是转向 “ i形”的学生 - 在一门学科中具有力量 - 陷入“ PI形”的生物数据科学家 具有两个核心优势：（1）生成和分析生物学数据，（2）开发理论模型 以及有关生物过程的可检验假设。布朗大学这个集中社区将是 由28个订婚，跨学科的教师主持人维护，他们将精神上四个NIH支持 每年学员以及每年4名棕色大学支持的学员（导致40个生物学数据 科学家超过5年）在各种教学，研究和职业发展活动中有一年的时间。这些 活动将包括一个新的一年的研究生，跨学科研究轮换，计划撤退 对于教师和学员，以及一系列圆桌讨论，重点是专业发展 跨学科研究人员。由此产生的社区将促进针对必不可少的技能的发展 跨学科的生物医学研究，包括将科学传达到广泛和领域的能力 - 特定的受众，导航跨学科合作和授予申请，学术和面试 基于行业的研究职业，并从事可再现和开放科学。教师主持人的研究 程序涵盖了多种生物生物，系统和问题，遍及进化遗传学， 功能基因组学，生物网络，衰老的分子生物学，发育鲁棒性，生物医学 信息信息，免疫学调节和生物物理学。此外，受体的年度合计 研究资金基础超过1200万美元的直接成本，为加强这一创新性的基础提供了坚实的基础 培训计划。该培训计划将产生能够提取新见解的调查人员 复杂生物数据集的系统。