The evolutionary dynamics of protein-protein interaction networks inferred from the reconstruction of ancient networks.
The evolutionary dynamics of protein-protein interaction networks inferred from the reconstruction of ancient networks.
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DOI：
10.1371/journal.pone.0058134
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Makse HA
中科院分区：
文献类型：
--
作者：
Jin Y;Turaev D;Weinmaier T;Rattei T;Makse HA
细胞功能基于蛋白质复杂的相互作用，因此这些蛋白质-蛋白质相互作用（PPI）网络的结构和动力学是理解细胞功能的关键。在过去的几年里，对几种模式生物的大规模 PPI 网络进行了研究。已经开发了许多理论模型来解释网络的形成和当前的结构。基于基因复制和分歧的模型受到青睐，因为它们最接近地代表了网络进化的生物学基础。然而，研究通常基于模拟而不是经验数据，或者仅涵盖单一生物体。方法论的改进现在允许同时分析多种生物体的 PPI 网络以及祖先网络的直接建模。这提供了挑战现有网络演进假设的机会。我们利用来自七种模式生物的综合数据集的当今 PPI 网络，并开发了一个理论和生物信息学框架来研究 PPI 网络的进化动力学。开发了一种使用渗流分析的新颖过滤方法，以消除基于拓扑约束的低置信度交互。然后，我们重建了不同祖先的古代 PPI 网络，并推断出祖先蛋白质组以及祖先相互作用。使用不同进化水平上的直系同源群重建祖先蛋白质。开发了一种使用重复发散模型的随机方法，用于估计当今 PPI 网络中古代相互作用的概率。网络的节点、边、大小和模块的增长率表明乘法增长，并且与独立静态分析的结果一致。我们的结果支持进化的重复发散模型，并表明分形和乘性增长是 PPI 网络结构和动力学的一般属性。
Cellular functions are based on the complex interplay of proteins, therefore the structure and dynamics of these protein-protein interaction (PPI) networks are the key to the functional understanding of cells. In the last years, large-scale PPI networks of several model organisms were investigated. A number of theoretical models have been developed to explain both the network formation and the current structure. Favored are models based on duplication and divergence of genes, as they most closely represent the biological foundation of network evolution. However, studies are often based on simulated instead of empirical data or they cover only single organisms. Methodological improvements now allow the analysis of PPI networks of multiple organisms simultaneously as well as the direct modeling of ancestral networks. This provides the opportunity to challenge existing assumptions on network evolution. We utilized present-day PPI networks from integrated datasets of seven model organisms and developed a theoretical and bioinformatic framework for studying the evolutionary dynamics of PPI networks. A novel filtering approach using percolation analysis was developed to remove low confidence interactions based on topological constraints. We then reconstructed the ancient PPI networks of different ancestors, for which the ancestral proteomes, as well as the ancestral interactions, were inferred. Ancestral proteins were reconstructed using orthologous groups on different evolutionary levels. A stochastic approach, using the duplication-divergence model, was developed for estimating the probabilities of ancient interactions from today's PPI networks. The growth rates for nodes, edges, sizes and modularities of the networks indicate multiplicative growth and are consistent with the results from independent static analysis. Our results support the duplication-divergence model of evolution and indicate fractality and multiplicative growth as general properties of the PPI network structure and dynamics.
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影响因子：
4.6
作者：
Gallos LK;Barttfeld P;Havlin S;Sigman M;Makse HA
通讯作者：
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通讯作者：
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