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应对强大的新技术的出现所导致的数据爆炸。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。这个国家科学基金会研究培训（NRT）奖给马里兰州大学，学院公园将建立一个创新的，跨学科的研究生教育模式，通过培养学生追求一系列的干职业在计算机，物理和生命科学的联系，解决这一挑战。学员将学习将联合收割机物理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，以更深入地了解生命系统的原理。该项目预计将培养大约60名博士生，其中包括35名受资助的学员，来自物理，计算机和生命科学。了解数据衍生的交互模式如何洞察复杂的生物现象是该计划的研究重点。通过跨学科培训，合作研究和推广活动的创新组合，NRT学员将成为将原始生物数据转化为有用信息的过程中的专家，从中可以推断出新的生物学见解。参与者将在网络分析的四个不同领域接受培训：生物网络的定量指标;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。该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，涵盖广泛的生物尺度：生物分子网络;神经网络;和生态/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向不同受众传达复杂科学思想所需的技能，以最大限度地发挥影响力。NSF研究培训计划（NRT）旨在鼓励为STEM研究生教育培训开发和实施大胆的、新的潜在变革模式。该培训轨道致力于在高优先级的跨学科研究领域的STEM研究生的有效培训，通过全面的培训模式，是创新的，以证据为基础，并与不断变化的劳动力和研究需求保持一致。

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.