(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复杂代谢和调控网络的视觉和计算描述，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设。这些网络将结合来自路径数据库、文本挖掘程序、机器学习系统和其他来源的联合收割机图形模型，以及多类实验数据。该图形可视化分析平台的独特之处在于：1）评估随时间和其他条件而变化的动态链接的结构效果。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，以评估其影响力和解释力。3)以用户或自动选择的详细程度交互式显示大型代谢和调节网络。4)创建专门设计用于改进生物网络显示、比较和分析的可视化图形显示工具。 作为这个过程的一部分，变量图结构，增量图布局，有效的可视化和标签的分析中的重要问题将与跨学科的重点解决。该软件将是开源的，免费提供给学术机构。它将使用三个相互关联但仅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以指导特定的植物对胁迫的防御反应。 该软件将为研究界的开放源码软件工具箱作出贡献，提供更有效的方法来可视化和操纵图形。通过在设计和开发中积极整合生物学家，我们将确保非计算机专家用户的实用性和可用性。 教育和外联活动将促进研究、K-12和本科教育，并将成果传播给广大受众，同时培养新一代科学家，他们将充分利用计算机的力量来推动所有科学的发展。

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.