(SEI+II (BIO)) Interactive Visualization and Analysis of Large-Scale Graphs for Biological Network Modeling

这项研究的最终目标是整合对复杂代谢和调节网络的可视化和计算描述，以帮助生物学家评估这些动态网络在不同条件下如何发挥作用的假说。这些网络将把路径数据库、文本挖掘程序、机器学习系统和其他来源的图形模型与多类实验数据结合在一起。提出的图形可视化和分析平台的独特功能是：1)评估随时间和其他条件变化的动态连杆的结构效应。2)能够立即将当前的研究假设与现有的已发表结果相结合，以评估其影响和解释能力。3)以用户选择的或自动选择的细节级别交互显示大型代谢和调节网络。4)创建专为改进生物网络显示、比较和分析而设计的可视化图形显示工具。作为这一过程的一部分，将以跨学科的重点解决可变图表结构分析、增量图表布局以及有效的可视化和标记方面的重大问题。该软件将是开源的，学术机构可以免费使用。它将使用三个相互关联但只有部分了解的信号转导网络进行评估和验证：乙烯、茉莉酸和水杨酸。这些途径复杂地相互作用，指导特定的植物对逆境的防御反应。该软件将通过提供更有效的可视化和操作图表的方法，为研究社区的开源软件工具箱做出贡献。通过积极地让生物学家参与设计和开发，我们将确保非计算机专家用户的实用适用性和可用性。教育和外联活动将促进研究、K-12和本科教育，并向广大受众传播成果，同时培养最大限度地利用计算机能力推动所有科学发展的新一代科学家。

The ultimate goal of this research is to integrate visual and computational descriptions of complex metabolic and regulatory networks to aid biologists in evaluating hypotheses for how these dynamic networks function under different conditions. These networks will combine graph models from pathway databases, text-mining programs, machine learning systems, and other sources, together with multiple classes of experimental data. The unique features of the proposed graph visualization and analysis platform are: 1) Evaluation of the structural effects of dynamic links that change depending on time and other conditions. 2) The ability to immediately integrate current research hypotheses with available published results to evaluate their impact and explanatory power. 3) Interactive display of large metabolic and regulatory networks in either user- or automatically selected levels of detail. 4) Creation of visual graph display tools specifically designed for improved biological network display, comparison, and analysis. As part of this process, significant problems in the analysis of variable graph structures, incremental graph layout, and effective visualization and labeling will be addressed with an interdisciplinary focus. The software will be open source and freely available to academic institutions. It will be evaluated and validated using three interrelated but only partially understood signal transduction networks: ethylene, jasmonate and salicylic acid. These pathways interact complexly to direct specific plant defense responses to stress. The software will contribute to the research community's open source software toolbox by providing more effective ways to visualize and manipulate graphs. By actively integrating biologists in the design and development we will ensure practical applicability and usability for non-computer-expert users. Education and outreach activities will promote research, K-12 and undergraduate education, and dissemination of results to a broad audience, while developing a new generation of scientists that employ the powers of computers to their fullest to advance all sciences.