Lethality and centrality in protein networks Cell biology traditionally identifies proteins based on their individual actions as catalysts, signaling molecules, or building blocks of cells and microorganisms. Lethality and centrality in protein networks. The centrality-lethality rule shows that a highly connected protein is more important to an organism than a poorly connected BibTeX. Plain Text. Performance of current approaches has been less than satisfactory as the lethality of a protein is a functional characteristic that cannot be determined solely by network topology. Furthermore, a significant number of lethal proteins have low connectivity in the interaction networks but are overlooked by most current methods.

Lethality and centrality in protein networks bibtex

[Lethality and centrality in protein networks Oltvai, Z. N.}, biburl = {https://www. perfumeadele.com}. PDF | In this paper we present the first mathematical analysis of the protein interaction network found in the yeast, S. cerevisiae. We show that, (a) the identified. Oltvai ZN: Lethality and centrality in protein networks. Article in Show abstract . Gene co-citation networks associated with worker sterility in honey bees. Nature. May 3;() Lethality and centrality in protein networks. Jeong H(1), Mason SP, Barabási AL, Oltvai ZN. Author information. In this contribution, we revisit the organisation of protein networks, particularly the centrality–lethality hypothesis, which hypothesises that nodes with higher. The protein–protein interaction (PPI) network has a small number of Our network analysis suggests that the centrality-lethality rule is Citation: He X, Zhang J () Why Do Hubs Tend to Be Essential in Protein Networks?. Author Summary Analysis of protein interaction networks in the budding Citation: Zotenko E, Mestre J, O'Leary DP, Przytycka TM () Why Do . To confirm the centrality-lethality rule in the tested networks we used the. | The protein–protein interaction (PPI) network has a small number of highly that the centrality-lethality rule is unrelated to the network Citation: He X, Zhang J ( ) Why do hubs tend to be essential in protein networks?. In a protein interaction network, a set of collective influencers (CI) is defined .. A. L. & Oltvai, Z. N. Lethality and centrality in protein networks. Algorithms for centrality indices. In Brandes and Lethality and centrality in protein networks. Nature High-betweenness proteins in the yeast protein interaction network. Journal of The pagerank citation ranking: Bringing order to the web.] Lethality and centrality in protein networks bibtex Lethality and centrality in protein networks. The centrality-lethality rule shows that a highly connected protein is more important to an organism than a poorly connected BibTeX. Plain Text. Lethality and centrality in protein networks. But our post-genomic view is expanding the protein's role into an element in a network of protein–protein interactions as well. Here we provide quantitative support for this idea by demonstrating that the phenotypic consequence of a single gene deletion in the yeast Saccharomyces cerevisiae is affected to a large extent by the topological position of its protein product in the complex hierarchical web of molecular interactions. Lethality and centrality in protein networks Cell biology traditionally identifies proteins based on their individual actions as catalysts, signaling molecules, or building blocks of cells and microorganisms. Currently, we witness the emergence of a post-genomic view that expands the protein’s role, regarding it as an element in a network of. Lethality and centrality in protein networks. Abstract. The most highly connected proteins in the cell are the most important for its survival. Proteins are. Researchers have shown that the lethality of a protein can be computed based on its topological position in the protein-protein interaction (PPI) network. Performance of current approaches has been less than satisfactory as the lethality of a protein is a functional characteristic that cannot be determined solely by network topology. Lethality and centrality in protein networks The most highly connected proteins in the cell are the most important for its survival. Figure 1 Characteristics of the yeast proteome. a, Map of protein–protein interactions. The largest cluster, which contains ~78% of all proteins, is shown. In this contribution, we revisit the organisation of protein networks, particularly the centrality–lethality hypothesis, which hypothesises that nodes with higher centrality in a network are more likely to produce lethal phenotypes on removal, compared to nodes with lower centrality. We consider the protein networks of a diverse set of Keywords: essentiality, centrality, biological network, topological network analysis, biological centrality. Citation: Jalili M, Salehzadeh-Yazdi A, Gupta S, Wolkenhauer O, Yaghmaie M, Resendis-Antonio O and Alimoghaddam K () Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks. Front. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): 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. The next step in the understanding of the genome organization, after the determination of complete sequences, involves proteomics. The proteome includes the whole set of protein-protein interactions, and two recent independent studies have shown that its topology displays a number of surprising features shared by other complex networks, both natural and artificial. This paper proposes an alternative way to identify nodes with high betweenness centrality. It introduces a new metric, κ-path centrality, and a randomized algorithm for estimating it, and shows empirically that nodes with high κ-path centrality have high node betweenness centrality. Figure 1. e-MDSet proteins follow the centrality-lethality rule. (A) In a toy network we defined a minimum dominating set (MDSet) as an optimized subset of nodes (red square symbol) from where each remaining (i.e. non-MDSet) node (gray circle symbol) can be immediately reached by one step. The phenomenological network concept of degrees of separation is applied to three-dimensional protein structure networks and reveals how amino acid residues can be connected to each other within six degrees of separation. This work also illuminates the unique features of protein networks in comparison to other networks currently studied. When it comes to Social Network Analysis (SNA) a common practice is to use centrality measures such as Betweenness (Bridgeness), Closeness and Degree. For instance, we often represent “Hubness” (in-degree) by colour scale, and “Bridgeness” (betwee.

LETHALITY AND CENTRALITY IN PROTEIN NETWORKS BIBTEX


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