CIF:Medium:Collaborative Research:Integrating and Mining Bio-Data from Multi Sources in Biological Networks

CIF：中：协作研究：生物网络中多源生物数据的集成和挖掘

• 批准号: 0905291
• 负责人: Xiaohua Hu
• 金额: $ 35.33万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905291&HistoricalAwards=false
• 关键词: CIF; Medium; Collaborative; Research; Integrating

Networks are a natural, powerful, and versatile tool representing the structure of complex systems and have been widely used in many disciplines, ranging from sociology to physics to biology. Massive amounts of biological data from multiple sources await interpretation. This calls for formal information integration, modeling and mining methods. The functioning of complex biological systems demands the intricate coordination of various cellular processes and their participating components. As biological networks grow in size and complexity, the model of a biological network must become more rigorous to keep track of all the components and their interactions, and in general this presents the need for new methods and technologies in information acquisition, transmission and processing. This research develops a set of novel methods and algorithms of integrating, analyzing and mining biological networks that include multiple sources of biomolecular information such as gene and protein expressions, interactions, and regulations, the formation and dissolution of protein complexes, and interactions between proteins and small molecules. Quantitative and qualitative bio-data from multiple sources are iteratively integrated, mined and organized to generate scalable hypothetical biomolecular network structures. The dynamics of these computational hypotheses are tested and refined through dynamic simulation and laboratory experiments. The investigators will develop an integrated modeling and mining method for the biomolecular networks governing cellular processes, with natural connections to both the biological knowledge base and the design and analysis of biological experiments. We will also plan to investigate novel graph-based theoretical models and algorithms for biological data represented as networks or graphs.

网络是一种自然的、强大的、多功能的工具，代表了复杂系统的结构，并已广泛应用于许多学科，从社会学到物理学到生物学。来自多个来源的大量生物学数据有待解释。这就需要正式的信息集成、建模和挖掘方法。复杂的生物系统的功能需要各种细胞过程及其参与组件的复杂协调。随着生物网络的规模和复杂性的增长，生物网络的模型必须变得更加严格，以跟踪所有组件及其相互作用，一般来说，这需要在信息获取，传输和处理方面采用新的方法和技术。该研究开发了一套整合，分析和挖掘生物网络的新方法和算法，其中包括多种生物分子信息来源，如基因和蛋白质表达，相互作用和调控，蛋白质复合物的形成和溶解，以及蛋白质和小分子之间的相互作用。来自多个来源的定量和定性生物数据被迭代地整合、挖掘和组织，以生成可扩展的假设生物分子网络结构。这些计算假设的动力学通过动态模拟和实验室实验进行测试和完善。研究人员将为控制细胞过程的生物分子网络开发一种综合建模和挖掘方法，与生物学知识库以及生物学实验的设计和分析有着天然的联系。我们还将计划研究新的基于图的理论模型和算法，用于表示为网络或图的生物数据。