Measuring the evolutionary rewiring of biological networks.
Measuring the evolutionary rewiring of biological networks.
复制标题
DOI：
10.1371/journal.pcbi.1001050
复制
发表时间：
2011-01-06
影响因子：
4.3
通讯作者：
Gerstein MB
中科院分区：
文献类型：
--
作者：
Shou C;Bhardwaj N;Lam HY;Yan KK;Kim PM;Snyder M;Gerstein MB
我们已经积累了大量的生物网络数据，并且预计还会有更多。很快，我们预计能够像我们通常对分子序列所做的那样比较许多不同的生物网络。长期以来，人们一直认为许多网络以不同的速度发生变化或“重新连接”。因此，开发一个框架来以统一的方式量化网络之间的差异非常重要。我们基于序列进化的简单模型的类比开发了这种形式主义，并用它对所有当前可用的生物网络进行网络重新布线的系统研究。我们发现，与序列类似，由于潜在替代的饱和，生物网络在大时间差异下显示出变化率降低。然而，不同类型的生物网络始终以不同的速率重新连接。利用比较基因组学和蛋白质组学数据，我们发现重连速率的顺序一致：转录调节、磷酸化调节、遗传相互作用、miRNA 调节、蛋白质相互作用和代谢途径网络，从快到慢。在我们对生物体之间的匹配网络进行的所有比较中都发现了这种顺序。为了进一步了解网络重新布线，我们将观察到的重新布线与通过模拟获得的重新布线进行了比较。我们还研究了我们的形式主义如何容易地映射到其他网络环境；特别是，我们展示了如何应用它来分析一系列“常见”网络的变化，例如家谱、共同作者和 Linux 内核函数依赖关系。生物网络代表细胞中各种类型的分子组织。在进化过程中，分子以不同的速率发生变化。因此，从网络重连的角度研究生物网络的演化非常重要。了解生物网络如何进化最终有助于解释细胞系统的一般机制。在过去的十年中，大量的高通量实验帮助揭示了许多物种中不同类型的网络。最近的研究提供了单个网络的进化率计算，并观察到它们之间不同的重新布线率。我们选择了一种系统方法，利用跨物种的实验数据来比较常见类型的生物网络之间的重连率差异。我们的分析表明，监管网络通常比非监管协作网络发展得更快。我们的分析还强调了该方法在解决其他有趣的生物学问题方面的未来应用。
We have accumulated a large amount of biological network data and expect even more to come. Soon, we anticipate being able to compare many different biological networks as we commonly do for molecular sequences. It has long been believed that many of these networks change, or “rewire”, at different rates. It is therefore important to develop a framework to quantify the differences between networks in a unified fashion. We developed such a formalism based on analogy to simple models of sequence evolution, and used it to conduct a systematic study of network rewiring on all the currently available biological networks. We found that, similar to sequences, biological networks show a decreased rate of change at large time divergences, because of saturation in potential substitutions. However, different types of biological networks consistently rewire at different rates. Using comparative genomics and proteomics data, we found a consistent ordering of the rewiring rates: transcription regulatory, phosphorylation regulatory, genetic interaction, miRNA regulatory, protein interaction, and metabolic pathway network, from fast to slow. This ordering was found in all comparisons we did of matched networks between organisms. To gain further intuition on network rewiring, we compared our observed rewirings with those obtained from simulation. We also investigated how readily our formalism could be mapped to other network contexts; in particular, we showed how it could be applied to analyze changes in a range of “commonplace” networks such as family trees, co-authorships and linux-kernel function dependencies. Biological networks represent various types of molecular organizations in a cell. During evolution, molecules have been shown to change at varying rates. Therefore, it is important to investigate the evolution of biological networks in terms of network rewiring. Understanding how biological networks evolve could eventually help explain the general mechanism of cellular system. In the past decade, a large amount of high-throughput experiments have helped to unravel the different types of networks in a number of species. Recent studies have provided evolutionary rate calculations on individual networks and observed different rewiring rates between them. We have chosen a systematic approach to compare rewiring rate differences among the common types of biological networks utilizing experimental data across species. Our analysis shows that regulatory networks generally evolve faster than non-regulatory collaborative networks. Our analysis also highlights future applications of the approach to address other interesting biological questions.
登录
查看更多内容
影响因子：
64.8
作者：
Gavin, AC;Bösche, M;Superti-Furga, G
通讯作者：
Superti-Furga, G
DOI：
10.1073/pnas.2235868100
发表时间：
2003-11-11
影响因子：
11.1
作者：
Hinman, VF;Nguyen, AT;Davidson, EH
通讯作者：
Davidson, EH
影响因子：
64.8
作者：
Fay, JC;Wyckoff, GJ;Wu, CI
通讯作者：
Wu, CI
影响因子：
5.6
作者：
Babu, MM;Teichmann, SA;Aravind, L
通讯作者：
Aravind, L
影响因子：
64.8
作者：
Dujon, B;Sherman, D;Souciet, JL
通讯作者：
Souciet, JL