Application of stochastic automata networks for creation of continuous time Markov chain models of voltage gating of gap junction channels. Academic Article uri icon

Overview

abstract

  • 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.

publication date

  • February 1, 2015

Research

keywords

  • Connexins
  • Gap Junctions
  • Markov Chains
  • Neural Networks, Computer

Identity

PubMed Central ID

  • PMC4331413

Scopus Document Identifier

  • 84924169435

Digital Object Identifier (DOI)

  • 10.1155/2015/936295

PubMed ID

  • 25705700

Additional Document Info

volume

  • 2015