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理解酵母中的信号传导:来自网络分析的见解。

Understanding signaling in yeast: Insights from network analysis.

作者信息

Arga K Yalçin, Onsan Z Ilsen, Kirdar Betül, Ulgen Kutlu O, Nielsen Jens

机构信息

Department of Chemical Engineering, Boğaziçi University, 34342 Istanbul, Turkey.

出版信息

Biotechnol Bioeng. 2007 Aug 1;97(5):1246-58. doi: 10.1002/bit.21317.

DOI:10.1002/bit.21317
PMID:17252576
Abstract

Reconstruction of protein interaction networks that represent groups of proteins contributing to the same cellular function is a key step towards quantitative studies of signal transduction pathways. Here we present a novel approach to reconstruct a highly correlated protein interaction network and to identify previously unknown components of a signaling pathway through integration of protein-protein interaction data, gene expression data, and Gene Ontology annotations. A novel algorithm is designed to reconstruct a highly correlated protein interaction network which is composed of the candidate proteins for signal transduction mechanisms in yeast Saccharomyces cerevisiae. The high efficiency of the reconstruction process is proved by a Receiver Operating Characteristic curve analysis. Identification and scoring of the possible linear pathways enables reconstruction of specific sub-networks for glucose-induction signaling and high osmolarity MAPK signaling in S. cerevisiae. All of the known components of these pathways are identified together with several new "candidate" proteins, indicating the successful reconstructions of two model pathways involved in S. cerevisiae. The integrated approach is hence shown useful for (i) prediction of new signaling pathways, (ii) identification of unknown members of documented pathways, and (iii) identification of network modules consisting of a group of related components that often incorporate the same functional mechanism.

摘要

重建代表参与相同细胞功能的蛋白质组的蛋白质相互作用网络,是信号转导通路定量研究的关键一步。在此,我们提出一种新方法,通过整合蛋白质-蛋白质相互作用数据、基因表达数据和基因本体注释,来重建高度相关的蛋白质相互作用网络,并识别信号通路中先前未知的成分。设计了一种新算法来重建高度相关的蛋白质相互作用网络,该网络由酿酒酵母中信号转导机制的候选蛋白质组成。通过接受者操作特征曲线分析证明了重建过程的高效性。对可能的线性通路进行识别和评分,能够重建酿酒酵母中葡萄糖诱导信号和高渗MAPK信号的特定子网络。这些通路的所有已知成分以及几个新的“候选”蛋白质都被识别出来,表明成功重建了酿酒酵母中涉及的两条模型通路。因此,这种综合方法被证明可用于:(i)预测新的信号通路;(ii)识别已记录通路中未知的成员;(iii)识别由一组通常包含相同功能机制的相关成分组成的网络模块。

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