• DocumentCode
    591208
  • Title

    A 2-state Markov model of IKACh based on a membrane voltage dependent muscarinic M2 receptor approach

  • Author

    Seemann, Gunnar ; Moss, Robin ; Kurz, Alexander K. E. ; Dossel, Olaf ; Tristani-Firouzi, Martin ; Sachse, Frank B

  • Author_Institution
    Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany
  • fYear
    2012
  • fDate
    9-12 Sept. 2012
  • Firstpage
    241
  • Lastpage
    244
  • Abstract
    The heart rate is mediated by the G protein-coupled muscarinic receptor (M2R) activating the acetylcholine (ACh)-dependent K+ current (IKACh). Here, a novel model for IKACh gating is presented based on recent findings that M2R agonist binding is voltage-sensitive. Furthermore, ACh and pilocarpine (Pilo) manifest opposite voltage-dependent IKACh modulation. In a previous work, a 4-state Markov model of M2R reconstructing the voltage-dependent change in agonist affinity was proposed. In this work, a 2-state Markov model of IKACh gating purely dependent on the Gβγ concentration is proposed. IKACh is modeled based on the description of Zhang et al. Measurement data are used to parametrize the combined M2R and IKACh model for both ACh and Pilo. The channel model has a linear Gβγ dependent forward and a constant backward rate. For ACh and Pilo, optimal values of model parameters are found reconstructing the measured opposite voltage-dependent change in agonist affinity. The combined model is able to reconstruct the measured data regarding the agonist and voltage-dependent properties of the M2R-IKACh channel complex. In future studies, this channel will be integrated in a sinus node model to investigate the effect of the channel properties on heart rate.
  • Keywords
    Adaptation models; Clamps; Data models; Markov processes; Mathematical model; Protocols; Voltage measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing in Cardiology (CinC), 2012
  • Conference_Location
    Krakow, Poland
  • ISSN
    2325-8861
  • Print_ISBN
    978-1-4673-2076-4
  • Type

    conf

  • Filename
    6420375