Networks from Toxicogenomics Gene Expression Data

毒理基因组学基因表达数据网络

• 批准号: 6935463
• 负责人: TATIANA A NIKOLSKAYA
• 金额: $ 9.86万
• 依托单位: GENEGO, INC.
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-05-10 至 2005-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6935463
• 关键词: biological signal transduction; cooperative study; data management; early diagnosis; gene environment interaction; gene expression; genetic polymorphism; high throughput technology; information retrieval; liver disorder diagnosis; liver toxic disorder; mathematics; metabolomics; microarray technology; molecular biology information system; protein protein interaction; proteomics; technology /technique development; toxicology information system; biological signal transduction; cooperative study; data management; early diagnosis; gene environment interaction; gene expression; genetic polymorphism; high throughput technology; information retrieval; liver disorder diagnosis; liver toxic disorder; mathematics; metabolomics; microarray technology; molecular biology information system; protein protein interaction; proteomics; technology /technique development; toxicology information system

DESCRIPTION (provided by applicant): Recently available for ADME/Tox research, new "genomics" technologies allowed to exponentially increase the throughput of pre-clinical toxicity testing. However, the functional analysis of new data lags behind, with mainly mechanistic clustering statistical procedures. We propose a novel approach of identification and analysis of functional networks and modules implicated in toxicity in human and experimental models. In Phase I we will use our system's biology data mining suit MetaCore to analyze publicly available toxicogenomics microarray expression data and reconstruct signaling, regulatory and metabolic networks specifically implicated in different types of hepatotoxicity in rat. The networks will be adjusted to reflect the difference in protein-protein interactions between human and rat and analyzed using multiple network comparison algorithms within our suit MetaCore. The resulted "signature networks" characteristic for specific toxicities, will be tested in genome-wide microarray experiments in collaboration with the University of Wisconsin (EDGE ? 2 database). The project will result in a unique toolkit for rational analysis of gene expression, proteomics and metabolomics data widely needed in toxicogenomics research, and in series of "signature networks" applicable for early detection and sub-categorization of hepatotoxicity. The technology allows integration and analysis of different types of experimental data on the same backbone of functional networks. The network analysis toolkit (algorithms, database, visualization tools) will be integrated in our ADME/Tox suit MetaDrug and offered to phramaceutical companies and academic institutions including NIEHS and FDA.

描述（由申请人提供）：最近可用于ADME/TOX研究，允许新的“基因组学”技术指数增加临床前毒性测试的吞吐量。但是，对新数据落后的功能分析主要是机械集群统计程序。我们提出了一种与人类和实验模型中毒性有关的功能网络和模块的鉴定和分析的新方法。在第一阶段，我们将使用系统的生物学数据挖掘套件来分析公开可用的毒物学微阵列表达数据并重建信号传导，调节性和代谢网络，这些网络特异性涉及大鼠不同类型的肝毒性。将调整网络以反映人与大鼠之间蛋白质 - 蛋白质相互作用的差异，并使用我们西装元素中的多个网络比较算法进行分析。由威斯康星大学（Edge？2数据库）合作，将在全基因组微阵列实验中测试所得的特定毒性特征的“签名网络”特征。该项目将导致一个独特的工具包，以合理地分析毒物学研究中广泛需要的基因表达，蛋白质组学和代谢组学数据，以及适用于适用于肝毒性早期检测和亚分类的一系列“签名网络”。该技术允许在功能网络相同的骨干网上集成和分析不同类型的实验数据。网络分析工具包（算法，数据库，可视化工具）将集成在我们的ADME/TOX西装元数据中，并提供给Phramaceutical公司和包括NIEHS和FDA在内的学术机构。