NRT-DESE: Network Biology: From Data to Information to Insights

当今生命科学研究人员面临的一个紧迫问题是如何应对由强大的新技术的出现而产生的数据爆炸。如果没有这种转变所需的跨学科工具，更多的数据不会产生更好的信息。这项授予马里兰大学的国家科学基金会研究培训(NRT)奖将为研究生教育建立一种创新的、跨学科的模式，通过让学生在计算机、物理和生命科学的结合部从事一系列STEM职业来应对这一挑战。学员将学习将物理学风格的定量建模与计算机科学的数据处理、分析和可视化方法相结合，以更深入地了解支配生命系统的原理。该项目预计将培训大约六十(60)名博士生，其中包括三十五(35)名资助的学员，他们来自物理、计算机和生命科学。理解数据衍生的交互模式如何能够洞察复杂的生物现象是该项目的研究重点。通过跨学科培训、合作研究和推广活动的创新组合，NRT受训人员将成为将原始生物数据转化为有用信息的专家，从中可以推断出新的生物学见解。学员将在四个不同的网络分析领域接受培训：生物网络的量化指标；生物网络的机械模型；生物应用的网络统计和机器学习；以及大型、复杂的生物数据集的可视化技术。这一培训将为三个应用领域中的一个或多个领域的研究提供基础，涵盖广泛的生物学范围：生物分子网络；神经元网络；以及生态/行为网络。研究经验、跨学科课程、点对点教程和与合作伙伴的实习将为研究生提供所需的技能，以便向不同的受众交流复杂的科学思想，以最大限度地发挥影响。推广活动将把该计划的好处扩大到本科生、初中生和广大公众。NSF研究培训(NRT)计划旨在鼓励为STEM研究生教育培训开发和实施大胆的、具有潜在变革性的新模式。培训路径致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求保持一致的综合培训模式，在高度优先的跨学科研究领域对STEM研究生进行有效培训。

An urgent issue facing today's researchers in the life sciences is coping with the data explosion resulting from the advent of powerful new technologies. More data does not yield better information without the interdisciplinary tools required for such a transformation. This National Science Foundation Research Traineeship (NRT) award to the University of Maryland, College Park will build an innovative, cross-disciplinary model for graduate education that addresses this challenge by preparing students to pursue a range of STEM careers at the nexus of the computer, physical, and life sciences. Trainees will learn to combine physics-style quantitative modeling with data processing, analysis, and visualization methods from computer science to gain deeper insights into the principles governing living systems. The project anticipates training approximately sixty (60) PhD students, including thirty-five (35) funded trainees, from the physical, computer, and life sciences.Understanding how data-derived interaction patterns can give insights into complex biological phenomena is the research focus of this program. Through an innovative combination of cross-disciplinary training, collaborative research, and outreach activities, NRT trainees will become experts in the process of transforming raw biological data into useful information from which new biological insights can be inferred. Participants will receive training in four different areas of network analysis: quantitative metrics for biological networks; mechanistic models of biological networks; network statistics and machine learning for biological applications; and visualization techniques for large, complex, biological datasets. This training will provide the foundation for research in one or more of three application areas, covering a wide range of biological scales: biomolecular networks; neuronal networks; and ecological/behavioral networks. Research experiences, interdisciplinary coursework, peer-to-peer tutorials, and internships with partners will provide graduate students with the skills needed to communicate complex scientific ideas to diverse audiences in order to maximize impact. Outreach activities will extend the benefits of the program to undergraduates, middle/high school students, and to the public at large.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.