MODELING ROLES OF BIOACTIVE LIPIDS IN GENE EXPRESSION SYSTEMS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 基因表达事件涉及信号转导通路的激活/抑制以及顺式调节元件对这种信号的反应。关于基因表达系统的信息通常是以不同形式的高通量实验数据收集的，例如，信号转导途径的活动状态通常体现为信号转导途径中信号分子的浓度变化；顺式调控元件的信息包含在基因启动子序列数据中；转录事件的信息(由反式和顺式调节元件的相互作用产生)反映在微阵列数据中。在这个项目中，我们将开发统计模型，以在概率图形模型框架内集成来自上述数据源的信息，在该框架中，系统内的组件被表示为变量，其交互作用被显式地建模为概率关系。我们的总体假设是，通过信息整合，所提出的模型将增强我们破译基因表达系统机制的能力。通过将提出的统计模型应用于复合数据，我们将检验特定的假设，如：信息整合增强识别受信号转导途径调控的基因组，并有助于阐明鞘磷脂调节基因表达的机制。所开发的模型将为未来研究糖尿病和癌症等疾病的机制奠定基础。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. A gene expression event involves the activation/repression of a signal transduction cascade and the cis-regulatory elements responding to such a signal. Information about the gene expression system is usually collected in heterogeneous forms of high throughput experimental data, e.g. activity state of a signal transduction pathway is usually embodied as the concentration changes of the signaling molecules within the pathway; information of cis-regulatory elements is contained in gene promoter sequence data; and information of transcription events (resulted from interaction of the trans- and cis-regulatory components) is reflected in microarray data. In this project, we will develop statistical models to integrate information from the above data sources within a probabilistic graphical model framework, in which the components within the systems are represented as variables and their interactions are explicitly modeled as probabilistic relationships. Our overall hypothesis is that, through information integration, the proposed models will enhance our capability to decipher the mechanisms of the gene expression system. By applying the proposed statistical models on the composite data, we will test specific hypotheses such as: information integration enhances identification the groups of genes regulated by signal transduction pathways, and it facilitates elucidating the mechanisms by which sphingolipids regulate gene expression. The developed models will lay a foundation for future study of mechanisms of diseases such as diabetes and cancer.