EAGER: Identifying Blockmodel Functional Modules across Multiple Networks

EAGER：识别跨多个网络的 Blockmodel 功能模块

• 批准号: 1447235
• 负责人: Xiaoning Qian
• 金额: $ 20万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-15 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1447235&HistoricalAwards=false
• 关键词: EAGER; Identifying; Blockmodel; Functional; Modules

Broader Significance and Importance: Due to the high complexity of analyzing high-throughput omics data, most of the existing computational methods separately analyze the data collected from different sources. Furthermore, they typically assume that the available prior biology knowledge, such as molecular interactions manifested as biological networks, is accurate. As the existing curated molecular interactions and functionalities across public databases still have unsatisfactory coverage or consistency, it is critical to develop effective analysis methods that can achieve biologically meaningful solutions by integrating diverse evidence from multiple biological networks. The objective of the proposed research is to develop a network-based mathematical framework and a set of new computational algorithms for the integrative analysis of multiple networks. The proposed research has strong transformative potentials in network biology. If successful, it can eventually lead to computational tools for more accurate and reliable identification of novel biomarkers and functional pathways. Beyond that, through the ongoing collaborations with biologists and physicians, it will open up new applications of network analysis methods to improve our understanding of complex human diseases. The interdisciplinary nature of this proposal promises to foster cross-fertilization of ideas between engineering and biology through research and education.Technical Description: The proposed research investigates integrative analysis of multiple biological networks, which are often noisy, to robustly identify biologically significant functional modules. The proposed mathematical framework provides a platform to address both critical issues in multiple network analysis regarding the computational complexity and biological significance by simultaneously analyzing multiple networks in a modular space. The advantage of integrative analysis of multiple biological networks is two-fold: First, cellular functional pathways that carry out critical functionalities are likely to be conserved across different organisms. Multiple network analysis will improve the performance of functional module identification. Second, new evidence from the analysis of identified modules may effectively transfer previously accrued knowledge to more confident curation and annotation of molecular relationships and the underlying cellular mechanisms. The proposed research and education activities are to: 1) design a new mathematical model for multiple biological network analysis to identify network modules for better understanding functional organization of cells and the complex cellular mechanisms; 2) devise effective and efficient optimization algorithms, including mathematical programming and stochastic optimization algorithms, to solve the optimization problems at different levels of complexity; 3) evaluate the performance of the proposed methods by constructing biologically realistic benchmark datasets; 4) apply the methods to systems biology research through collaboration with biomedical researchers; and 5) integrate research findings into the education and training of students with various academic backgrounds in the interdisciplinary field of computational network biology.

更广泛的意义和重要性：由于分析高通量组学数据的高度复杂性，现有的大多数计算方法分别分析来自不同来源的数据。此外，他们通常假设现有的生物学知识，如表现为生物网络的分子相互作用，是准确的。由于现有的跨公共数据库的分子相互作用和功能的覆盖范围和一致性仍然不令人满意，因此开发有效的分析方法，通过整合来自多个生物网络的不同证据来获得具有生物学意义的解决方案至关重要。提出的研究目标是开发一个基于网络的数学框架和一套新的计算算法，用于多个网络的综合分析。该研究在网络生物学领域具有强大的变革潜力。如果成功，它最终可以导致计算工具更准确和可靠地识别新的生物标志物和功能途径。除此之外，通过与生物学家和医生的持续合作，它将开辟网络分析方法的新应用，以提高我们对复杂人类疾病的理解。该提案的跨学科性质有望通过研究和教育促进工程和生物学之间的思想交流。技术描述：提出的研究调查了多个生物网络的综合分析，这些网络通常是嘈杂的，以稳健地识别生物学上重要的功能模块。提出的数学框架提供了一个平台，通过同时分析模块化空间中的多个网络来解决多网络分析中关于计算复杂性和生物学意义的关键问题。对多种生物网络进行整合分析的优势有两个方面：首先，执行关键功能的细胞功能通路可能在不同的生物体中是保守的。多网络分析将提高功能模块识别的性能。其次，来自已识别模块分析的新证据可以有效地将先前积累的知识转化为更有信心的分子关系和潜在细胞机制的管理和注释。提出的研究和教育活动是：1)设计一个新的数学模型，用于多种生物网络分析，以识别网络模块，以便更好地理解细胞的功能组织和复杂的细胞机制；2)设计有效、高效的优化算法，包括数学规划和随机优化算法，解决不同复杂程度的优化问题；3)通过构建生物学上真实的基准数据集来评估所提出方法的性能；4)通过与生物医学研究人员的合作，将这些方法应用于系统生物学研究；5)将研究成果整合到计算网络生物学跨学科领域不同学术背景学生的教育和培训中。