National Center: Multiscale Analysis of Genomic and Cellular Networks (MAGNet)

分子相互作用的网络，包括成千上万的基因、它们的产物和其他分子，构成了细胞过程的基础。从转录复合物的形成/激活，到信号通路的可用性，一直到宏观过程（如细胞粘附），对这些相互作用的广泛研究，要求在全基因组计算方法的设计中达到一个新的复杂水平。对于生物医学研究界来说，一个全面绘制和分析分子细胞相互作用的同质环境将是一个强大的资源。我们建议建立一个国家基因组和细胞网络多尺度分析中心（MAGNet）。该中心将提供一个综合计算框架，将细胞中的分子相互作用组织成可管理的上下文相关组件，并将开发可互操作的计算模型和工具，以利用这种细胞相互作用图来阐明重要的生物过程。中心的活动将涉及重要的，多学科的努力，生物和计算科学。具体的专业领域包括自然语言解析（NLP）、机器学习（ML）、软件系统和工程、数据库、计算结构生物学、遗传网络逆向工程、生物医学文献数据挖掘和生物医学本体等。该中心将1)构建一个证据整合框架，根据其统计相关性收集和融合各种不同的细胞相互作用线索；2)组装一套全面的基于物理和知识的方法来填充该框架；3)提供一套方法和过滤器，锚定在正式领域本体中，将特定的相互作用与生物体、组织、分子和细胞环境相关联。所有相关工具将通过通用的、可扩展的、可互操作的软件平台geWorkbench提供给生物医学研究界。我们将培训和鼓励研究人员使用和/或为geWorkbench开发新的模块。软件平台的一个重要元素将是开发特定组件，这些组件可以利用Core 9001研究人员开发的证据集成技术，将来自Core 9002算法和数据库的分子相互作用线索结合起来。开发将由生物医学界推动和测试，以确保工具的有用性和图形用户界面的可用性，以全新的方式解决生物医学问题，剖析负责细胞过程和功能的细胞相互作用网络。

A network of molecular interactions, involving many thousands of genes, their products, and other molecules, underlie cellular processes. Investigation of these interactions across a wide range of scales ranging from the formation/activation of transcriptional complexes, to the availability of a signaling pathway, all the way to macroscopic processes, such as cell adhesion, calls for a new level of sophistication in the design of genome- wide computational approaches. A homogeneous environment for the comprehensive mapping and analysis of molecular cellular interactions in would be a powerful resource for the biomedical research community. We propose the creation of a National Center for the Multiscale Analysis of Genomic and Cellular Networks (MAGNet). The Center will provide an integrative computational framework to organize molecular interactions in the cell into manageable context-dependent components and will develop interoperable computational models and tools that can leverage such a map of cellular interactions to elucidate important biological processes. Center activities will involve a significant, multidisciplinary effort of biological and computational sciences. Specific areas of expertise include natural language parsing (NLP), machine learning (ML), software systems and engineering, databases, computational structural biology, reverse engineering of genetic networks, biomedical literature datamining, and biomedical ontologies, among others. The Center will 1) construct an evidence integration framework to collect and fuse a variety of diverse cellular interaction clues based on their statistical relevance 2) assemble a comprehensive set of physics- and knowledge-based methodologies to fill this framework 3) provide a set of methodologies and filters, anchored in formal domain ontologies, to associated specific interactions to an organism, tissue, molecular, and cellular context. All relevant tools will be made accessible to the biomedical research community through a common, extensible, and interoperable software platform, geWorkbench. We will reach out to train and encourage researchers to use and/or develop new modules for, geWorkbench. An important element of the software platform will be the development of specific components that can exploit the evidence integration techniques developed by Core 9001 investigators to combine molecular interaction clues from Core 9002 algorithms and databases. Development will be both driven and tested by the biomedical community to ensure the usefulness of the tools and the usability of the graphical user interfaces to address biomedical problems in completely novel ways, to dissect the web of cellular interactions responsible for cellular processes and functions.