Manke Thomas, Demetrius Lloyd, Vingron Martin
Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany.
Genome Inform. 2005;16(1):159-63.
We characterize protein interaction networks in terms of network entropy. This approach suggests a ranking principle, which strongly correlates with elements of functional importance, such as lethal proteins. Our combined analysis of protein interaction networks and functional profiles in single cellular yeast and multi-cellular worm shows that proteins with large contribution to network entropy are preferentially lethal. While entropy is inherently a dynamical concept, the present analysis incorporates only structural information. Our result therefore highlights the importance of topological features, which appear as correlates of an underlying dynamical property, and which in turn determine functional traits. We argue that network entropy is a natural extension of previously studied observables, such as pathway multiplicity and centrality. It is also applicable to networks in which the processes can be quantified and therefore serves as a link to study questions of structural and dynamical robustness in a unified way.
我们根据网络熵来表征蛋白质相互作用网络。这种方法提出了一种排序原则,该原则与功能重要性元素(如致死蛋白)密切相关。我们对单细胞酵母和多细胞蠕虫中的蛋白质相互作用网络与功能谱进行的综合分析表明,对网络熵贡献大的蛋白质更易具有致死性。虽然熵本质上是一个动态概念,但目前的分析仅纳入了结构信息。因此,我们的结果突出了拓扑特征的重要性,这些拓扑特征表现为潜在动态特性的相关因素,进而决定功能性状。我们认为网络熵是先前研究的可观测量(如通路多样性和中心性)的自然延伸。它也适用于其中过程可量化的网络,因此可作为以统一方式研究结构和动态稳健性问题的一个纽带。