ArticleBMC bioinformatics2007
Influence of degree correlations on network structure and stability in protein-protein interaction networks.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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14 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Topological analysis as a tool for detection of abnormalities in protein-protein interaction data.Bioinformatics (Oxford, England) · 2022Article
- DLS: A Link Prediction Method Based on Network Local Structure for Predicting Drug-Protein Interactions.Frontiers in bioengineering and biotechnology · 2020Article
- Inferring pathogen-host interactions between Leptospira interrogans and Homo sapiens using network theory.Scientific reports · 2019Article
- Network robustness assessed within a dual connectivity framework: joint dynamics of the Active and Idle Networks.Scientific reports · 2017Article
- Co-Expression Network Models Suggest that Stress Increases Tolerance to Mutations.Scientific reports · 2015Article
- Randomness and preserved patterns in cancer network.Scientific reports · 2014Article
- Antitumoral effects of 9-cis retinoic acid in adrenocortical cancer.Cellular and molecular life sciences : CMLS · 2014Article
- Construction and analysis of the protein-protein interaction networks based on gene expression profiles of Parkinson's disease.PloS one · 2014Article
- Article
- Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods.PloS one · 2010Article
- Exploring biological network structure with clustered random networks.BMC bioinformatics · 2009Article
- Topological properties of protein interaction networks from a structural perspective.Biochemical Society transactions · 2008Review
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2 authors.
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Abstract
backgroundThe existence of negative correlations between degrees of interacting proteins is being discussed since such negative degree correlations were found for the large-scale yeast protein-protein interaction (PPI) network of Ito et al. More recent studies observed no such negative correlations for high-confidence interaction sets. In this article, we analyzed a range of experimentally derived interaction networks to understand the role and prevalence of degree correlations in PPI networks. We investigated how degree correlations influence the structure of networks and their tolerance against perturbations such as the targeted deletion of hubs.
resultsFor each PPI network, we simulated uncorrelated, positively and negatively correlated reference networks. Here, a simple model was developed which can create different types of degree correlations in a network without changing the degree distribution. Differences in static properties associated with degree correlations were compared by analyzing the network characteristics of the original PPI and reference networks. Dynamics were compared by simulating the effect of a selective deletion of hubs in all networks.
conclusionConsiderable differences between the network types were found for the number of components in the original networks. Negatively correlated networks are fragmented into significantly less components than observed for positively correlated networks. On the other hand, the selective deletion of hubs showed an increased structural tolerance to these deletions for the positively correlated networks. This results in a lower rate of interaction loss in these networks compared to the negatively correlated networks and a decreased disintegration rate. Interestingly, real PPI networks are most similar to the randomly correlated references with respect to all properties analyzed. Thus, although structural properties of networks can be modified considerably by degree correlations, biological PPI networks do not actually seem to make use of this possibility.
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