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出现带来的数据爆炸。如果没有这种转变所需的跨学科工具，更多的数据并不能产生更好的信息。马里兰大学帕克分校获得国家科学基金会研究实习 (NRT) 奖，将为研究生教育建立一个创新的跨学科模型，通过让学生做好准备在计算机领域从事一系列 STEM 职业来应对这一挑战、物理和生命科学。学员将学习将物理式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管理原理。该项目预计培训大约六十 (60) 名博士生，其中包括三十五 (35) 名受资助的受训人员，他们来自物理、计算机和生命科学领域。了解数据衍生的交互模式如何深入了解复杂的生物现象是研究重点这个程序的。通过跨学科培训、合作研究和推广活动的创新结合，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，并从中推断出新的生物学见解。参与者将接受网络分析四个不同领域的培训：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研究奠定基础，涵盖广泛的生物尺度：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、同行辅导以及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。外展活动将使该计划的好处惠及本科生、中学生/高中生以及广大公众。 NSF 研究实习 (NRT) 计划旨在鼓励开发和实施大胆的、具有潜在变革性的 STEM 新模型研究生教育培训。培训课程致力于通过创新、循证且符合不断变化的劳动力和研究需求的综合培训模式，对高度优先的跨学科研究领域的 STEM 研究生进行有效培训。

项目成果

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

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.

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

Phase transitions and assortativity in models of gene regulatory networks evolved under different selection processes

不同选择过程下进化的基因调控网络模型中的相变和相配性

• DOI: 10.1098/rsif.2020.0790
• 发表时间: 2021
• 期刊: Journal of The Royal Society Interface
• 影响因子: 3.9
• 作者: Alexander, Brandon;Pushkar, Alexandra;Girvan, Michelle
• 通讯作者: Girvan, Michelle