Current high-throughput experimental techniques make it feasible to infer gene regulatory interactions at the whole-genome level with reasonably good accuracy. Such experimentally inferred regulatory networks have become available for a number of simpler model organisms such as S. cerevisiae, and others. The availability of such networks provides an opportunity to compare gene regulatory processes at the whole genome level, and in particular, to assess similarity of regulatory interactions for homologous gene pairs either from the same or from different species.We present here a new technique for analyzing the regulatory interaction neighborhoods of paralogous gene pairs. Our central focus is the analysis of S. cerevisiae gene interaction graphs, which are of particular interest due to the ancestral whole-genome duplication (WGD) that allows to distinguish between paralogous transcription factors that are traceable to this duplication event and other paralogues. Similar analysis is also applied to E. coli and C. elegans networks.We compare paralogous gene pairs according to the presence and size of bi-fan arrays, classically associated in the literature with gene duplication, within other network motifs. We further extend this framework beyond transcription factor comparison to obtain topology-based similarity metrics based on the overlap of interaction neighborhoods applicable to most genes in a given organism.We observe that our network divergence metrics show considerably larger similarity between paralogues, especially those traceable to WGD. This is the case for both yeast and C. elegans, but not for E. coli regulatory network. While there is no obvious cross-species link between metrics, different classes of paralogues show notable differences in interaction overlap, with traceable duplications tending toward higher overlap compared to genes with shared protein families. Our findings indicate that divergence in paralogous interaction networks reflects a shared genetic origin, and that our approach may be useful for investigating structural similarity in the interaction networks of paralogous genes.

中文翻译：

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基于网络基序的旁系同源基因对调控模式分析

当前的高通量实验技术使得在全基因组水平上以相当好的准确度推断基因调控相互作用成为可能。这种通过实验推断出的调控网络已可用于许多更简单的模式生物，如酿酒酵母等。此类网络的可用性为在全基因组水平上比较基因调控过程提供了机会，特别是评估来自相同或不同物种的同源基因对的调控相互作用的相似性。我们在这里提出了一种新的分析技术旁系同源基因对的调控相互作用邻域。我们的中心重点是分析酿酒酵母基因相互作用图，由于祖先的全基因组复制（WGD）允许区分可追溯到该复制事件和其他旁系同源物的旁系同源转录因子，因此特别感兴趣。类似的分析也适用于大肠杆菌和秀丽隐杆线虫网络。我们根据双扇阵列的存在和大小比较旁系同源基因对，双扇阵列在文献中与基因复制经典相关，在其他网络基序中。我们进一步将此框架扩展到转录因子比较之外，以获得基于拓扑的相似性度量，该度量基于适用于给定生物体中大多数基因的相互作用邻域的重叠。我们观察到我们的网络分歧度量显示旁系同源物之间相当大的相似性，尤其是那些可追溯至WGD。酵母和 C 都是这种情况。线虫，但不适用于大肠杆菌监管网络。虽然指标之间没有明显的跨物种联系，但不同类别的旁系同源物在相互作用重叠方面表现出显着差异，与具有共享蛋白质家族的基因相比，可追溯的重复倾向于更高的重叠。我们的研究结果表明，旁系同源相互作用网络的差异反映了共同的遗传起源，并且我们的方法可能有助于研究旁系同源基因相互作用网络中的结构相似性。