Design and Implementation of Effective Solutions for Archiving and Processing Systems Biology Data: Research Integrated with an Ongoing High School Education Program.

系统生物学研究所获得了一笔赠款，用于设计系统生物学数据整合的信息学解决方案，以加快网络推理和知识发现。具体目标包括：（1）定义用于以数字形式捕获实验和样本信息的灵活数据标准;（2）开发辅助数据管理、检索和统计分析的工具;以及（3）创建面向教育的数据库扩展，以促进高中培训计划。作为该项目的一部分，PI将开发一个基于系统生物学的探究式教育项目，将数学、自然科学和计算机科学引入高中课程。该项目的中心策略是由一个关键的观察所激发的，尽管复杂，但每种数据类型（例如，微阵列比率、串联质谱、蛋白质/DNA网络、序列、注释等）都可以在系统生物学中进行。通常在其“原生”上下文中定义良好，并且当这些数据组合在一起时，复杂性主要会增加。因此，本项目开发的工具和方法将利用独立的小型、专业化和强大的数据库和软件的优点，并通过使用精心设计的规则和技术（如Java RMI）松散地连接这些子系统，实现下游集成。这种简单方法的一个重要规定将允许研究人员通过一组不断发展的规则和应用这些规则的推理引擎，动态地将他们的数据与其他有价值的远程管理数据库相关联。此外，由于实验设计和样本处理在分析数据以得出有意义的见解时是必不可少的，因此数据集成将由灵活的实验元信息模式指导，该模式将在本项目中正式化。为了扩展这种数据模式的日常使用的实用性，在这个项目中开发的用户友好的软件工具将接口实验室科学家直接与自动化数据处理管道，以帮助捕获实验细节，并创建这个元信息模式。

The Institute of Systems Biology is awarded a grant to design an informatics solution for the integration of systems biology data to expedite network inference and knowledge discovery. Specific aims include (1) definition of a flexible data standard for capturing experiment and sample information in digital form (2) development of tools to assist data curation, retrieval, and statistical analysis; and (3) creation of education -oriented database extensions that facilitate high-school training programs. As part of the project, the PI's will develop an inquiry based education program in systems biology that brings mathematics, natural science and computer science to high-school curricula.The central strategy of the project is motivated by a key observation that, although complex, each data type (for example, microarray ratios, tandem mass spectra, protein/DNA networks, sequences, annotations etc.) is usually well-defined in its 'native' context, and that complexity arises mostly when these data are combined. Tools and approaches developed in this project will, therefore, capitalize on virtues of independent small, specialized and robust databases and software, and enable downstream integration by loosely connecting these sub-systems using carefully crafted rules and technologies such as Java RMI. An important provision of this simple approach will allow researchers to dynamically relate their data to other valuable remotely managed databases through an evolving set of rules, and an inference engine that applies these rules. Furthermore, because experiment design and sample processing are indispensable while analyzing data to draw meaningful insights, data integration will be guided by a flexible experiment meta-information schema, which will be formalized in this project. To extend practicality of this data schema for day-to-day use, user-friendly software tools developed in this project will interface laboratory scientists directly with automated data processing pipelines to help capture experiment details and create this meta-information schema.