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OMICS数据的高复杂性，大多数现有的计算方法分别分别分析了从不同来源收集的数据。此外，他们通常假定可用的先前生物学知识（例如以生物网络的分子相互作用）是准确的。由于公共数据库中现有的策划分子相互作用和功能仍然没有令人满意的覆盖范围或一致性，因此开发有效的分析方法可以通过整合来自多个生物网络的各种证据来实现生物学上有意义的解决方案。拟议的研究的目的是开发基于网络的数学框架和一组新的计算算法，以进行多个网络的集成分析。拟议的研究在网络生物学方面具有强大的变革潜力。如果成功，它最终可能会导致计算工具，以更准确，可靠地识别新型生物标志物和功能途径。除此之外，通过与生物学家和医师进行的持续合作，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来促进工程和生物学之间的思想的交叉侵入。技术描述：拟议的研究调查了多个生物网络的综合分析，这些分析通常是嘈杂的，可以强有力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，可以通过同时分析模块化空间中的多个网络来解决有关计算复杂性和生物学意义的多个关键问题。对多个生物网络的综合分析的优势是两倍：首先，在不同生物体中，进行关键功能的细胞功能途径很可能是保守的。多个网络分析将改善功能模块识别的性能。其次，来自对已确定模块的分析的新证据可以有效地将先前应计的知识转移到更自信的策划和分子关系的注释和基本细胞机制。拟议的研究和教育活动是：1）设计一种新的数学模型，用于多种生物网络分析，以确定网络模块，以更好地了解细胞的功能组织和复杂的细胞机制； 2）设计有效，有效的优化算法，包括数学编程和随机优化算法，以解决不同复杂性水平的优化问题； 3）通过构建生物学上现实的基准数据集来评估所提出的方法的性能； 4）通过与生物医学研究人员的合作将方法应用于系统生物学研究； 5）将研究结果纳入计算网络生物学跨学科领域的各种学术背景的学生的教育和培训。