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

doi:10.1155/2015/936295

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

Biomed Res Int. 2015:2015:936295. doi: 10.1155/2015/936295. Epub 2015 Feb 1.

Authors

Mindaugas Snipas¹, Henrikas Pranevicius², Mindaugas Pranevicius³, Osvaldas Pranevicius⁴, Nerijus Paulauskas⁵, Feliksas F Bukauskas⁶

Affiliations

¹ Department of Mathematical Modelling, Kaunas University of Technology, Studentų Street 50, 51368 Kaunas, Lithuania ; Laboratory of Systems Control and Automation, Lithuanian Energy Institute, Breslaujos Street 3, 44403 Kaunas, Lithuania.
² Department of Applied Informatics, Vytautas Magnus University, Vileikos Street 8-409, 44404 Kaunas, Lithuania ; Department of Business Informatics Research in Systems, Kaunas University of Technology, Studentų Street 56, 5142 Kaunas, Lithuania.
³ Department of Anesthesiology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.
⁴ Department of Anesthesiology, New York Hospital Queens, 56-45 Main Street, Flushing, NY 11355, USA.
⁵ Department of Business Informatics Research in Systems, Kaunas University of Technology, Studentų Street 56, 5142 Kaunas, Lithuania ; Institute of Cardiology, Lithuanian University of Health Sciences, Sukileliu Street 17, 50009 Kaunas, Lithuania.
⁶ Department of Neuroscience, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.

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 types

Research Support, Non-U.S. Gov't

MeSH terms

Connexins / chemistry*
Gap Junctions / chemistry*
Humans
Ion Channels / chemistry
Markov Chains*
Models, Theoretical
Neural Networks, Computer*

Substances

Connexins
Ion Channels

Grants and funding

R01 NS072238/NS/NINDS NIH HHS/United States