Bayesian Methods in Signal Transduction Network Analysis

描述(由申请人提供)：我们的长期目标是通过计算建模和整合不同类型的信息来研究细胞用来完成信号转导过程的机制。大规模基因组和蛋白质组技术的最新进展产生了大量的数据，并引起了我们对系统水平上信号转导的全面理解的关注。考虑到这些高通量技术产生的不完整和有噪声的数据，需要能够结合多个数据源来分析全基因组信号转导网络的新的计算方法，以充分利用快速积累的数据。在这项研究中，我们将开发稳健的贝叶斯方法来整合不同数据类型的信号网络推理，主要是在萌芽酵母酿酒酵母中。我们提出的方法将被应用于识别蛋白质伴侣复合体，并研究伴侣在介导信号通路中的作用。我们的方法的结果将受到进一步的实验验证。该应用程序的中心假设是，通过开发和应用贝叶斯方法在不同的大规模数据源上，我们可以成功地在全基因组范围内推断信号网络。我们计划验证这一假设，并通过追求以下具体目标来实现这一应用的总体目标：1)开发基于大规模蛋白质相互作用数据的贝叶斯方法来识别蛋白质复合体；2)通过整合来自不同来源的信息，包括微阵列基因表达数据、蛋白质相互作用数据和蛋白质磷酸化数据，发现蛋白质之间的关联并推断信号转导网络；3)开发用户友好的计算机软件来实现所提出的方法。该软件将被开发、测试并免费分发给科学界。这项拟议的工作将是通过数据集成从不同的大规模数据集中提取信息以在全基因组范围内推断信号转导网络的第一项主要工作。我们提出的方法可以成为一种强有力的手段，使推断信号转导网络的过程更快、更容易，并产生指导实验设计的假设，导致更多信息丰富的实验。该研究将有助于设计有效的信令网推理方法，实现对异质数据源的集成。这些方法和用户友好软件的开发将提供有用的工具，以更好地了解细胞如何对环境变化做出反应，更重要的是，这些反应的失败如何导致各种疾病。

DESCRIPTION (provided by applicant): Our long-term goal is to investigate the mechanisms cells use to accomplish signal transduction processes through computational modeling and integration of different types of information. Recent advances in large- scale genomic and proteomic techniques have generated enormous amounts of data and have drawn our attention towards a comprehensive understanding of signal transduction at the systems level. Given the incomplete and noisy data generated from these high-throughput techniques, novel computational approaches capable of incorporating multiple data sources for analyzing the genome-wide signal transduction networks are needed to fully take advantage of the rapid accumulation of data. In this study, we will develop robust Bayesian methods to integrate diverse data types for signaling network inference, primarily in the budding yeast Saccharomyces cerevisiae. Our proposed methodology will be applied for identifying protein chaperone complexes and investigating the roles of chaperones in mediating signaling pathways. The results of our method will be subject to further experimental validation. The central hypothesis of the application is that, by developing and applying Bayesian methods on heterogeneous large-scale data sources, we can successfully infer signaling networks in a genome-wide scale. We plan to test the hypothesis and accomplish the overall objective of this application by pursuing the following specific aims: 1) Develop Bayesian methods to identify protein complexes based on large-scale protein interaction data; 2) Discover the associations among proteins and infer signal transduction networks by integrating information from diverse sources, including microarray gene expression data, protein interaction data and protein phosphorylation data; 3) Develop user-friendly computer software to implement the proposed methods. The software will be developed, tested and distributed to the scientific community free of charge. The proposed work will represent the first major effort that extracts information from diverse large-scale datasets through data integration for inferring signal transduction networks at a genome-wide scale. Our proposed approach can be a powerful means to make the process of inferring signal transduction networks faster and easier, and produce hypotheses that guide the experimental design, leading to more informative experiments. The research will contribute to designing efficient signaling network inference methods through integrating heterogeneous data sources. The development of these methods and user-friendly software will provide useful tools to better understand how cells respond to environment changes, and more importantly, how failure of these responses leads to a variety of diseases.