Time Series Analysis of Expression Profiles

在过去的几年里，已经设计出了强大的新方法，使研究人员能够同时研究整个基因组的表达动态。这些潜在的海量数据原则上能够解剖控制细胞中基因表达模式和节奏的复杂遗传网络。这项拟议工作的目标是开发全基因组表达谱的系统分析。我们的目标是从基因表达数据中推断出调控网络，并用实验结果验证这些程序。为此，该项目将综合各种努力，包括：大型网络的系统建模和可视化、模型系统实验以及数据库和生物信息学工具的开发。我们识别与复杂表型相关的基因类型的策略是对正常和功能失活系统的时间序列数据进行比较分析。功能失活是利用各种遗传和药理学方法在单个基因水平上实现的。这种方法成功的关键是开发动态的、可预测的基因表达模型。这项提议的目的是开发计算和信息学工具来分析和解释与这些功能修饰相关的表达谱。这些工具将在经过验证的“黄金标准”数据集上进行测试。这个模型系统将包括对SW1353软骨肉瘤细胞的信号转导通路进行分析。这一努力的一个组成部分是统计分析和实验设计之间的相互作用。这种努力的相互作用是通过项目中内置的协作来实现的。

Over the past few years powerful new methods have been devised that enable researchers to simultaneously study the dynamics of expression of an entire genome. This potentially vast quantity of data enables, in principle, the dissection of the complex genetic networks that control the patterns and rhythms of gene expression in the cell. The goal of the proposed work is to develop a systems analysis of whole genome expression profiles. Our aim is to infer regulatory networks from gene expression data and to validate these procedures with experimental results. To this end, the project will integrate a combination of efforts involving: systems modeling and visualization of large networks, experimentation on model systems, and development of database and bioinformatic tools. Our strategy for identifying genotypes associated with complex phenotype is to use a comparative analysis of time series data for normal and functional inactivated systems. The functional inactivation is achieved at the individual gene level using a variety of genetic and pharmacological methods. The key to the success of such an approach is the development of dynamic, predictive models of gene expression. The aim of this proposal is to develop the computational and informatic tools to analyze and interpret expression profiles associated with these functional modifications. These tools will be tested against a validated "gold-standard" data set. This model system will involve profiling signal transduction pathways in SW1353 chondrosarcoma cells. Integral to this effort is the interplay between statistical analysis and experimental design. Such interplay of efforts is made possible by the collaborations built into the project.