2019/09/24 by Klaus M. Frahm, Dima L. Shepelyansky · 9 citations
Biochemistry, Genetics and Molecular Biology · Chemistry · Physics and Astronomy · #Bioinformatics and Genomic Networks #Biology #Chemistry #Complex Network Analysis Techniques #Computational biology #Computer network #Computer science #Frame (networking) #Function (biology) #Genetics #Matrix (chemical analysis) #Network analysis #PageRank #Physics #Protein Structure and Dynamics #Protein–protein interaction #Theoretical computer science #q-bio.MN
paper · pdf · doi:10.1016/j.physa.2020.125019
published in Physica A Statistical Mechanics and its Applications 559, 125019 (Elsevier BV) · 23 pages, 18 figures, additional material at: http://www.quantware.ups-tlse.fr/QWLIB/google4signornet/
arxiv created 2019/09/24 · openalex publication_date 2020/08/05 · arxiv updated 2020/08/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Directed protein networks with only a few thousand of nodes are rather complex and do not allow to extract easily the effective influence of one protein to another taking into account all indirect pathways via the global network. Furthermore, the different types of activation and inhibition actions between proteins provide a considerable challenge in the frame work of network analysis. At the same time these protein interactions are of crucial importance and at the heart of cellular functioning. We develop the Google matrix analysis of the protein-protein network from the open public database SIGNOR. The developed approach takes into account the bi-functional activation or inhibition nature of interactions between each pair of proteins describing it in the frame work of Ising-spin matrix transitions. We also apply a recently developed linear response theory for the Google matrix which highlights a pathway of proteins whose PageRank probabilities are most sensitive with respect to two proteins selected for the analysis. This group of proteins is analyzed by the reduced Google matrix algorithm which allows to determine the effective interactions between them due to direct and indirect pathways in the global network. We show that the dominating activation or inhibition function of each protein can be characterized by its magnetization. The results of this Google matrix analysis are presented for three examples of selected pairs of proteins. The developed methods work rapidly and efficiently even for networks with several million of nodes and can be applied to various biological networks.