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

NRT-DESE：网络生物学：从数据到信息到见解

• 批准号: 1632976
• 负责人: Michelle Girvan
• 金额: $ 295.99万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1632976&HistoricalAwards=false
• 关键词: NRT; DESE; Network; Biology; Data

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.

当今研究人员在生命科学领域面临的紧急问题是应对强大的新技术出现导致的数据爆炸。如果没有这种转换所需的跨学科工具，更多的数据不会产生更好的信息。这项国家科学基金会研究实习生（NRT）授予马里兰大学，大学公园将建立一种创新的，跨学科的研究生教育模型，通过使学生准备在计算机，物理和生命科学的Nexus中从事一系列STEM职业，以应对这一挑战。受训者将学会将物理风格的定量建模与来自计算机科学的数据处理，分析和可视化方法相结合，以更深入地了解有关生活系统的原理。该项目预计，从物理，计算机和生命科学中培训了大约60（60）个博士学位学生，包括35（35）个资助的学员。理解数据衍生的互动模式如何使对复杂的生物学现象的见解是该计划的研究重点。通过跨学科培训，协作研究和外展活动的创新组合，NRT受训者将成为将原始生物学数据转换为有用的有用信息的专家，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；用于生物应用的网络统计和机器学习；和大型，复杂，生物数据集的可视化技术。该培训将为在三个应用领域的一个或多个研究中的研究提供基础，涵盖了广泛的生物量表：生物分子网络；神经元网络；和生态/行为网络。研究经验，跨学科课程，点对点教程以及与合作伙伴的实习，将为研究生提供与各种观众交流复杂的科学思想所需的技能，以最大程度地发挥影响力。外展活动将把计划的好处扩展到本科生，中学生和广大公众。NSF研究训练（NRT）计划旨在鼓励开发和实施用于STEM研究生教育培训的大胆，新的潜在转型模型。通过全面的跨学科研究领域的STEM研究生，培训轨道致力于有效地培训STEM研究生，通过全面的培训模型，该模型具有创新，基于循证的，并且与不断变化的员工队伍和研究需求保持一致。

项目成果

A Neurocomputational Model of Posttraumatic Stress Disorder

创伤后应激障碍的神经计算模型

• DOI: 10.1109/ner49283.2021.9441345
• 发表时间: 2021
• 期刊: International IEEE/EMBS Conference on Neural Engineering 2021
• 作者: Davis, Gregory P.;Katz, Garrett E.;Soranzo, Daniel;Allen, Nathaniel;Reinhard, Matthew J.;Gentili, Rodolphe J.;Costanzo, Michelle E.;Reggia, James A.
• 通讯作者: Reggia, James A.

Discovering Protein Conformational Flexibility through Artificial-Intelligence-Aided Molecular Dynamics

通过人工智能辅助分子动力学发现蛋白质构象灵活性

• DOI: 10.1021/acs.jpcb.0c03985
• 发表时间: 2020
• 期刊: The Journal of Physical Chemistry B
• 作者: Smith, Zachary;Ravindra, Pavan;Wang, Yihang;Cooley, Rory;Tiwary, Pratyush
• 通讯作者: Tiwary, Pratyush

Determination of disease phenotypes and pathogenic variants from exome sequence data in the CAGI 4 gene panel challenge.

• DOI: 10.1002/humu.23249
• 发表时间: 2017-09
• 期刊: Human mutation
• 影响因子: 3.9
• 作者: Kundu K;Pal LR;Yin Y;Moult J
• 通讯作者: Moult J

Modeling Working Memory to Identify Computational Correlates of Consciousness

建模工作记忆以识别意识的计算相关性

• DOI: 10.1515/opphil-2019-0022
• 发表时间: 2019
• 期刊: Open Philosophy
• 影响因子: 0.5
• 作者: Reggia, James A.;Katz, Garrett E.;Davis, Gregory P.
• 通讯作者: Davis, Gregory P.

Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems

• DOI: 10.1063/5.0005541
• 发表时间: 2020-02
• 期刊: Chaos
• 影响因子: 2.9
• 作者: Alexander Wikner;Jaideep Pathak;B. Hunt;M. Girvan;T. Arcomano;I. Szunyogh;A. Pomerance;E. Ott