Application of Stochastic Automata Networks for Creation of Continuous Time Markov Chain Models of Voltage Gating of Gap Junction Channels.

The primary goal of this work was to study advantages of numerical methods used for the creation of continuous time Markov chain models (CTMC) of voltage gating of gap junction (GJ) channels composed of connexin protein. This task was accomplished by describing gating of GJs using the formalism of t...

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Publicado en:BioMed Research International Vol. 2015; pp. 1 - 14
Autores principales: Snipas, Mindaugas, Pranevicius, Henrikas, Pranevicius, Mindaugas, Pranevicius, Osvaldas, Paulauskas, Nerijus, Bukauskas, Feliksas F.
Formato: equations & formulas research tables/charts Journal Article
Publicado: Wiley-Blackwell 2/1/2015
Acceso en línea:Ver este registro en EBSCOhost
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        10.1155/2015/936295
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        atl: Application of Stochastic Automata Networks for Creation of Continuous Time Markov Chain Models of Voltage Gating of Gap Junction Channels.
      aug:
        au:
          Snipas, Mindaugas
          Pranevicius, Henrikas
          Pranevicius, Mindaugas
          Pranevicius, Osvaldas
          Paulauskas, Nerijus
          Bukauskas, Feliksas F.
        affil: Department of Mathematical Modelling, Kaunas University of Technology, Studentų Street 50, 51368 Kaunas, Lithuania
      sug:
        subj:
          Cell Membrane Physiology
          Cell Communication Physiology
          Membrane Proteins
          Models, Statistical
          Probability
          Algorithms
          Funding Source
      ab: The primary goal of this work was to study advantages of numerical methods used for the creation of continuous time Markov chain models (CTMC) of voltage gating of gap junction (GJ) channels composed of connexin protein. This task was accomplished by describing gating of GJs using the formalism of the stochastic automata networks (SANs), which allowed for very efficient building and storing of infinitesimal generator of the CTMC that allowed to produce matrices of the models containing a distinct block structure. All of that allowed us to develop efficient numerical methods for a steady-state solution of CTMC models. This allowed us to accelerate CPU time, which is necessary to solve CTMC models, ∼20 times.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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