• DocumentCode
    1496172
  • Title

    Inferring Contagion in Regulatory Networks

  • Author

    Fujita, André ; Sato, João Ricardo ; Demasi, Marcos Angelo Almeida ; Yamaguchi, Rui ; Shimamura, Teppei ; Ferreira, Carlos Eduardo ; Sogayar, Mari Cleide ; Miyano, Satoru

  • Author_Institution
    Comput. Sci. Res. Program, RIKEN, Tokyo, Japan
  • Volume
    8
  • Issue
    2
  • fYear
    2011
  • Firstpage
    570
  • Lastpage
    576
  • Abstract
    Several gene regulatory network models containing concepts of directionality at the edges have been proposed. However, only a few reports have an interpretable definition of directionality. Here, differently from the standard causality concept defined by Pearl, we introduce the concept of contagion in order to infer directionality at the edges, i.e., asymmetries in gene expression dependences of regulatory networks. Moreover, we present a bootstrap algorithm in order to test the contagion concept. This technique was applied in simulated data and, also, in an actual large sample of biological data. Literature review has confirmed some genes identified by contagion as actually belonging to the TP53 pathway.
  • Keywords
    biology computing; genetics; inference mechanisms; physiological models; Pearl; TP53 pathway; bootstrap algorithm; contagion; gene expression; gene regulatory network models; regulatory networks; Biological system modeling; Biology computing; Gene expression; Mathematics; Probability distribution; Random variables; Switches; Switching circuits; Testing; US Department of Transportation; Contagion; local correlation; regulatory network.; Algorithms; Gene Expression Profiling; Gene Regulatory Networks; Genomics; Oligonucleotide Array Sequence Analysis; Tumor Suppressor Protein p53;
  • fLanguage
    English
  • Journal_Title
    Computational Biology and Bioinformatics, IEEE/ACM Transactions on
  • Publisher
    ieee
  • ISSN
    1545-5963
  • Type

    jour

  • DOI
    10.1109/TCBB.2010.40
  • Filename
    5467038