Department of Electrical Engineering & Computer Science, Case Western Reserve University, Cleveland, USA.
BMC Bioinformatics. 2009 Oct 14;10:333. doi: 10.1186/1471-2105-10-333.
In systems biology, comparative analyses of molecular interactions across diverse species indicate that conservation and divergence of networks can be used to understand functional evolution from a systems perspective. A key characteristic of these networks is their modularity, which contributes significantly to their robustness, as well as adaptability. Consequently, analysis of modular network structures from a phylogenetic perspective may be useful in understanding the emergence, conservation, and diversification of functional modularity.
In this paper, we propose a phylogenetic framework for analyzing network modules, with applications that extend well beyond network-based phylogeny reconstruction. Our approach is based on identification of modular network components from each network separately, followed by projection of these modules onto the networks of other species to compare different networks. Subsequently, we use the conservation of various modules in each network to assess the similarity between different networks. Compared to traditional methods that rely on topological comparisons, our approach has key advantages in (i) avoiding intractable graph comparison problems in comparative network analysis, (ii) accounting for noise and missing data through flexible treatment of network conservation, and (iii) providing insights on the evolution of biological systems through investigation of the evolutionary trajectories of network modules. We test our method, MOPHY, on synthetic data generated by simulation of network evolution, as well as existing protein-protein interaction data for seven diverse species. Comprehensive experimental results show that MOPHY is promising in reconstructing evolutionary histories of extant networks based on conservation of modularity, it is highly robust to noise, and outperforms existing methods that quantify network similarity in terms of conservation of network topology.
These results establish modularity and network proximity as useful features in comparative network analysis and motivate detailed studies of the evolutionary histories of network modules.
在系统生物学中，对不同物种之间分子相互作用的比较分析表明，网络的保守性和差异性可以用于从系统角度理解功能进化。这些网络的一个关键特征是其模块性，这对它们的鲁棒性和适应性有很大贡献。因此，从系统发育的角度分析模块化网络结构可能有助于理解功能模块化的出现、保守性和多样化。
在本文中，我们提出了一种用于分析网络模块的系统发育框架，其应用范围远远超出了基于网络的系统发育重建。我们的方法基于从每个网络中分别识别模块化网络组件，然后将这些模块投影到其他物种的网络上以比较不同的网络。随后，我们使用每个网络中各种模块的保守性来评估不同网络之间的相似性。与传统方法相比，我们的方法具有以下关键优势：（i）避免了比较网络分析中难以处理的图比较问题；（ii）通过灵活处理网络保守性来处理噪声和缺失数据；（iii）通过研究网络模块的进化轨迹为生物系统的进化提供了深入的见解。我们在模拟网络进化生成的合成数据以及七种不同物种的现有蛋白质-蛋白质相互作用数据上测试了我们的方法 MOPHY。全面的实验结果表明，MOPHY 在基于模块保守性重建现有网络的进化历史方面很有前景，它对噪声高度稳健，并且优于现有方法，这些方法在网络拓扑保守性方面量化了网络相似性。
这些结果确立了模块性和网络接近性作为比较网络分析中有用的特征，并促使对网络模块的进化历史进行详细研究。
