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比较相互作用组学

Comparative interactomics.

作者信息

Cesareni Gianni, Ceol Arnaud, Gavrila Caius, Palazzi Luisa Montecchi, Persico Maria, Schneider Maria Victoria

机构信息

Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 00133 Rome, Italy.

出版信息

FEBS Lett. 2005 Mar 21;579(8):1828-33. doi: 10.1016/j.febslet.2005.01.064.

Abstract

The behavior, morphology and response to stimuli in biological systems are dictated by the interactions between their components. These interactions, as we observe them now, are therefore shaped by genetic variations and selective pressure. Similar to what has been achieved by comparing genome structures and protein sequences, we hope to obtain valuable information about systems' evolution by comparing the organization of interaction networks and by analyzing their variation and conservation. Equally, significantly we can learn whether and how to extend the network information obtained experimentally in well-characterized model systems to different organisms. We conclude from our analysis that, despite the recent completion of several high throughput experiments aimed at the description of complete interactomes, the available interaction information is not yet of sufficient coverage and quality to draw any biologically meaningful conclusion from the comparison of different interactomes. Thus, the transfer of network information obtained from simple organism to evolutionary distant species should be carried out and considered with caution. By using smaller higher-confidence datasets, a larger fraction of interactions is shown to be conserved; this suggests that with the development of more accurate experimental and informatic approaches, we will soon be in the position to study the network evolution.

摘要

生物系统中的行为、形态以及对刺激的反应是由其组成部分之间的相互作用所决定的。因此,正如我们现在所观察到的,这些相互作用是由基因变异和选择压力塑造而成的。类似于通过比较基因组结构和蛋白质序列所取得的成果,我们希望通过比较相互作用网络的组织方式,并分析其变异和保守性,来获取有关系统进化的有价值信息。同样重要的是,我们能够了解是否以及如何将在特征明确的模型系统中通过实验获得的网络信息扩展到不同的生物体。我们从分析中得出结论,尽管最近完成了几项旨在描述完整相互作用组的高通量实验,但现有的相互作用信息在覆盖范围和质量上仍不足以从不同相互作用组的比较中得出任何具有生物学意义的结论。因此,从简单生物体获得的网络信息向进化距离较远的物种转移时应谨慎进行并加以考虑。通过使用较小的高可信度数据集,更大比例的相互作用被证明是保守的;这表明随着更精确的实验和信息学方法的发展,我们很快就能研究网络进化。

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