Application of stochastic automata networks for creation of continuous time markov chain models of voltage gating of gap junction channels

Mindaugas Snipas, Henrikas Pranevicius, Mindaugas Pranevicius, Osvaldas Pranevicius, Nerijus Paulauskas, Feliksas F. Bukauskas

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish (US)
Article number936295
JournalBioMed Research International
Volume2015
DOIs
StatePublished - 2015

Fingerprint

Markov Chains
Gap Junctions
Markov processes
Electric potential
Connexins
Numerical methods
Program processors
Proteins

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)

Cite this

Application of stochastic automata networks for creation of continuous time markov chain models of voltage gating of gap junction channels. / Snipas, Mindaugas; Pranevicius, Henrikas; Pranevicius, Mindaugas; Pranevicius, Osvaldas; Paulauskas, Nerijus; Bukauskas, Feliksas F.

In: BioMed Research International, Vol. 2015, 936295, 2015.

Research output: Contribution to journalArticle

Snipas, Mindaugas ; Pranevicius, Henrikas ; Pranevicius, Mindaugas ; Pranevicius, Osvaldas ; Paulauskas, Nerijus ; Bukauskas, Feliksas F. / Application of stochastic automata networks for creation of continuous time markov chain models of voltage gating of gap junction channels. In: BioMed Research International. 2015 ; Vol. 2015.
@article{c7604293abbb4f97acb8afe5839f3791,
title = "Application of stochastic automata networks for creation of continuous time markov chain models of voltage gating of gap junction channels",
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.",
author = "Mindaugas Snipas and Henrikas Pranevicius and Mindaugas Pranevicius and Osvaldas Pranevicius and Nerijus Paulauskas and Bukauskas, {Feliksas F.}",
year = "2015",
doi = "10.1155/2015/936295",
language = "English (US)",
volume = "2015",
journal = "BioMed Research International",
issn = "2314-6133",
publisher = "Hindawi Publishing Corporation",

}

TY - JOUR

T1 - Application of stochastic automata networks for creation of continuous time markov chain models of voltage gating of gap junction channels

AU - Snipas, Mindaugas

AU - Pranevicius, Henrikas

AU - Pranevicius, Mindaugas

AU - Pranevicius, Osvaldas

AU - Paulauskas, Nerijus

AU - Bukauskas, Feliksas F.

PY - 2015

Y1 - 2015

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=84924169435&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84924169435&partnerID=8YFLogxK

U2 - 10.1155/2015/936295

DO - 10.1155/2015/936295

M3 - Article

C2 - 25705700

AN - SCOPUS:84924169435

VL - 2015

JO - BioMed Research International

JF - BioMed Research International

SN - 2314-6133

M1 - 936295

ER -