Statistical properties of the MetaCore network of protein–protein interactions

被引：0
作者：
Ekaterina Kotelnikova
Klaus M. Frahm
José Lages
Dima L. Shepelyansky
机构：
[1] Clarivate Analytics,Laboratoire de Physique Théorique, CNRS, UPS
[2] Université de Toulouse,Équipe de Physique théorique et Astrophysique, Institut UTINAM, CNRS
[3] Université Bourgogne Franche-Comté,undefined
来源：
Applied Network Science | / 7卷
关键词：
Complex networks; MetaCore; Google matrix; PageRank; Protein–protein interactions network;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The MetaCore commercial database describes interactions of proteins and other chemical molecules and clusters in the form of directed network between these elements, viewed as nodes. The number of nodes goes beyond 40 thousands with almost 300 thousands links between them. The links have essentially bi-functional nature describing either activation or inhibition actions between proteins. We present here the analysis of statistical properties of this complex network applying the methods of the Google matrix, PageRank and CheiRank algorithms broadly used in the frame of the World Wide Web, Wikipedia, the world trade and other directed networks. We specifically describe the Ising PageRank approach which allows to treat the bi-functional type of protein–protein interactions. We also show that the developed reduced Google matrix algorithm allows to obtain an effective network of interactions inside a specific group of selected proteins. In addition to already known direct protein–protein interactions, this method allows to infer non trivial and unknown interactions between proteins arising from the summation over all the indirect pathways passing via the global bi-functional network. The developed analysis allows to establish an average action of each protein being more oriented to activation or inhibition. We argue that the described Google matrix analysis represents an efficient tool for investigation of influence of specific groups of proteins related to specific diseases.
引用
收藏
相关论文
共 189 条
  • [11] Ermann L(2019)Ising-PageRank model of opinion formation on social networks Phys A Stat Mech Appl 13 1-554
  • [12] Lages J(2020)Google matrix analysis of bi-functional SIGNOR network of protein–protein interactions Phys A Stat Mech Appl 21 1109-126
  • [13] Shepelyansky DL(2016)The p53 pathway: origins, inactivation in cancer, and emerging therapeutic approaches Ann Rev Biochem 598 129-16
  • [14] Du D(2018)Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks PLoS ONE 44 548-531
  • [15] Lee CF(2011)Prioritizing candidate disease genes by network-based boosting of genome-wide association data Genome Res 187 112-undefined
  • [16] Li X-Q(2021)An atlas of gene regulatory elements in adult mouse cerebrum Nature 12 5863-undefined
  • [17] Ekins S(2020)Integrative transcriptome and chromatin landscape analysis reveals distinct epigenetic regulations in human memory b cells Nat Commun 8 1-undefined
  • [18] Bugrim A(2016)SIGNOR: a database of causal relationships between biological entities Nucleic Acids Res 77 523-undefined
  • [19] Brovold L(1999)The p53 pathway J Pathol undefined undefined-undefined
  • [20] Kirillov E(2021)Tcf1 and lef1 provide constant supervision to mature cd8+ t cell identity and function by organizing genomic architecture Nat Commun undefined undefined-undefined
12345678910