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bzip转录因子相互作用网络对祖先状态推断

Inferring ancestral states of the bZIP transcription factor interaction network
课程网址: http://videolectures.net/pmnp07_pinney_ias/  
主讲教师: John Pinney
开课单位: 曼彻斯特大学
开课时间: 2007-09-07
课程语种: 英语
中文简介:
随着全基因组蛋白质相互作用网络数据集可用于各种物种,进化生物学家有机会解决围绕这些复杂系统演变的一些未解决的问题。可以比较来自不同生物的蛋白质相互作用网络,以研究基因重复,缺失和“重新布线”过程如何塑造其当代结构的进化[1,2]。然而,目前对齐来自多个物种的观测网络的方法通常缺乏有关网络演化的有意义结论所必需的系统发育背景。在这里,我们展示了概率建模如何为多种蛋白质相互作用网络的定量分析提供平台。我们将这种技术应用于bZIP转录因子家族的祖先网络的重建[3],并发现使用另一种基于序列的方法来预测亮氨酸拉链相互作用可以获得极好的一致性[4]。进一步的分析表明,与简单的基于简约法的方法相比,我们的概率方法对观察到的网络数据中的噪声存在更加稳健[5]。此外,证据在多个物种上的整合意味着可以使用相同的方法来改善现存物种的噪声相互作用数据的质量。这是第一次使用网络进化的显式概率模型重建蛋白质相互作用网络的祖先状态。我们预计它将成为探索生物化学网络演化历史的更一般方法的基础。
课程简介: As whole-genome protein interaction network datasets become available for a wide range of species, evolutionary biologists have the opportunity to address some of the unanswered questions surrounding the evolution of these complex systems. Protein interaction networks from divergent organisms may be compared to investigate how gene duplication, deletion and ‘re-wiring’ processes may have shaped the evolution of their contemporary structures [1,2]. However, current approaches to aligning observed networks from multiple species are generally lacking the phylogenetic context necessary for meaningful conclusions to be drawn regarding network evolution. Here we show how probabilistic modeling can provide a platform for the quantitative analysis of multiple protein interaction networks. We apply this technique to the reconstruction of ancestral networks for the bZIP family of transcription factors [3] and find that excellent agreement is obtained with an alternative, sequence-based method for the prediction of leucine zipper interactions [4]. Further analysis shows our probabilistic method to be significantly more robust to the presence of noise in the observed network data than a simple parsimony-based approach [5]. In addition, the integration of evidence over multiple species means that the same method may be used to improve the quality of noisy interaction data for extant species. This is the first time that ancestral states of a protein interaction network have been reconstructed using an explicit probabilistic model of network evolution. We anticipate that it will form the basis of more general methods for probing the evolutionary history of biochemical networks.
关 键 词: 全基因组蛋白质; 网络数据集; 进化生物学
课程来源: 视频讲座网
最后编审: 2019-09-13:lxf
阅读次数: 55