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Genome-scale analysis of interaction dynamics reveals organization of biological networks.

机译:相互作用动力学的基因组规模分析揭示了生物网络的组织。

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SUMMARY: Analyzing large-scale interaction networks has generated numerous insights in systems biology. However, such studies have primarily been focused on highly co-expressed, stable interactions. Most transient interactions that carry out equally important functions, especially in signal transduction pathways, are yet to be elucidated and are often wrongly discarded as false positives. Here, we revisit a previously described Smith-Waterman-like dynamic programming algorithm and use it to distinguish stable and transient interactions on a genomic scale in human and yeast. We find that in biological networks, transient interactions are key links topologically connecting tightly regulated functional modules formed by stable interactions and are essential to maintaining the integrity of cellular networks. We also perform a systematic analysis of interaction dynamics across different technologies and find that high-throughput yeast two-hybrid is the only available technology for detecting transient interactions on a large scale. CONTACT: haiyuan.yu@cornell.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
机译:简介:分析大规模交互网络已经在系统生物学中产生了许多见解。但是,此类研究主要集中于高度共表达的稳定相互作用。大部分具有同等重要功能的瞬态相互作用,尤其是在信号转导途径中,尚待阐明,经常被误认为是误报。在这里,我们重温先前描述的类似于Smith-Waterman的动态规划算法,并使用它来区分人类和酵母菌在基因组规模上的稳定和瞬时相互作用。我们发现在生物网络中,瞬时相互作用是拓扑连接由稳定相互作用形成的紧密调节的功能模块的关键链接,对于维持蜂窝网络的完整性至关重要。我们还对不同技术之间的相互作用动力学进行了系统分析,发现高通量酵母双杂交技术是唯一可用于大规模检测瞬时相互作用的技术。联系人:haiyuan.yu@cornell.edu补充信息:补充数据可从在线生物信息学获得。

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