MODELING ROLES OF BIOACTIVE LIPIDS IN GENE EXPRESSION SYSTEMS

该子项目是利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得主要资金， 因此可以在其他CRISP条目中表示。列出的机构是 中心，不一定是研究者的机构。 基因表达事件涉及信号转导级联的激活/抑制以及响应于这种信号的顺式调节元件。 基因表达系统的信息通常以异质形式的高通量实验数据收集，如信号转导通路的活性状态通常体现为通路内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;并且转录事件的信息（由反式和顺式调节组分的相互作用产生）反映在微阵列数据中。 在这个项目中，我们将开发统计模型，将上述数据源中的信息整合到概率图形模型框架中，其中系统中的组件表示为变量，它们的相互作用明确建模为概率关系。我们的总体假设是，通过信息整合，所提出的模型将提高我们的能力，破译基因表达系统的机制。通过将所提出的统计模型应用于复合数据，我们将测试特定的假设，如：信息整合增强了对信号转导途径调控的基因组的识别，并有助于阐明鞘脂调节基因表达的机制。 该模型的建立为进一步研究糖尿病、癌症等疾病的发病机制奠定了基础。

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.