• DocumentCode
    951258
  • Title

    System theoretical investigation of human epidermal growth factor receptor-mediated signalling

  • Author

    Zhang, Ye ; Shankaran, H. ; Opresko, L. ; Resat, H.

  • Author_Institution
    Comput. Biol. & Bioinf. Group, Pacific Northwest Nat. Lab., Richland, WA
  • Volume
    2
  • Issue
    5
  • fYear
    2008
  • fDate
    9/1/2008 12:00:00 AM
  • Firstpage
    273
  • Lastpage
    284
  • Abstract
    The partitioning of biological networks into coupled-functional modules is being increasingly applied for developing predictive models of biological systems. This approach has the advantage that predicting a system-level response does not require a mechanistic description of the internal dynamics of each module. Identification of the input-output characteristics of the network modules and the connectivity between the modules provide the necessary quantitative representation of system dynamics. However, the determination of the input-output relationships of the modules is not trivial; it requires the controlled perturbation of module inputs and systematic analysis of experimental data. In this report, the authors apply a system theoretical analysis approach to derive the time-dependent input-output relationships of the functional module for the human epidermal growth factor receptor (HER) mediated Erk and Akt signalling pathways. Using a library of cell lines expressing endogenous levels of epidermal growth factor receptor (EGFR) and varying levels of HER2, the authors show that a transfer function-based representation can be successfully applied to quantitatively characterise information transfer in this system.
  • Keywords
    cellular biophysics; neurophysiology; skin; Akt signalling pathways; Erk signalling pathways; HER2; biological networks partitioning; biological systems; cell lines; human epidermal growth factor receptor; information transfer; predictive models; system theoretical analysis; transfer function-based representation;
  • fLanguage
    English
  • Journal_Title
    Systems Biology, IET
  • Publisher
    iet
  • ISSN
    1751-8849
  • Type

    jour

  • DOI
    10.1049/iet-syb:20080116
  • Filename
    4648908