Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data
Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data
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DOI：
10.1093/bioinformatics/bti1133
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发表时间：
2005-01
期刊：
影响因子：
5.8
通讯作者：
Naoki Nariai;Y. Tamada;S. Imoto;S. Miyano
中科院分区：
文献类型：
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作者：
Naoki Nariai;Y. Tamada;S. Imoto;S. Miyano
细胞中的生物过程是通过基因调控、信号转导和蛋白质之间的相互作用来正确执行的。为了理解这种分子网络，我们提出了一种统计方法，从DNA微阵列数据、蛋白质相互作用数据和其他全基因组数据中同时估计基因调控网络和蛋白质相互作用网络。结果根据每个生物信息源的可靠性，将贝叶斯网络和马尔可夫网络统一起来估计基因调控网络和蛋白质-蛋白质相互作用网络。通过同时构建酿酒酵母细胞周期的基因调控网络和蛋白质-蛋白质相互作用网络，我们预测了几个目前功能未知的基因的作用。通过使用我们的概率模型，我们可以检测到高通量数据的假阳性，例如酵母双杂交数据。在全基因组实验中，我们发现了可能的基因调控关系和大型蛋白质复合体之间的蛋白质-蛋白质相互作用，这些复合体是生物过程复杂调控机制的基础。
MOTIVATION Biological processes in cells are properly performed by gene regulations, signal transductions and interactions between proteins. To understand such molecular networks, we propose a statistical method to estimate gene regulatory networks and protein-protein interaction networks simultaneously from DNA microarray data, protein-protein interaction data and other genome-wide data. RESULTS We unify Bayesian networks and Markov networks for estimating gene regulatory networks and protein-protein interaction networks according to the reliability of each biological information source. Through the simultaneous construction of gene regulatory networks and protein-protein interaction networks of Saccharomyces cerevisiae cell cycle, we predict the role of several genes whose functions are currently unknown. By using our probabilistic model, we can detect false positives of high-throughput data, such as yeast two-hybrid data. In a genome-wide experiment, we find possible gene regulatory relationships and protein-protein interactions between large protein complexes that underlie complex regulatory mechanisms of biological processes.