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Aufsatz / Paper in SCI-Expanded-Zeitschrift

An exact stochastic hybrid model of excitable membranes including spatio-temporal evolution

Buckwar E., Riedler M.: An exact stochastic hybrid model of excitable membranes including spatio-temporal evolution, in: Journal of Mathematical Biology, Volume 63, Number 6, Page(s) 1051-1093, 2011.

BibTeX

@ARTICLE{
title = {An exact stochastic hybrid model of excitable membranes including spatio-temporal evolution},
type = {Aufsatz / Paper in SCI-Expanded-Zeitschrift},
author = {Buckwar, Evelyn and Riedler, Martin},
language = {EN},
abstract = {In this paper, we present a mathematical description for excitable biological membranes, in particular neuronal membranes. We aim to model the (spatio-) temporal dynamics, e.g., the travelling of an action potential along the axon, subject to noise, such as ion channel noise. Using the framework of Piecewise Deterministic Processes (PDPs) we provide an exact mathematical description—in contrast to pseudo-exact algorithms considered in the literature—of the stochastic process one obtains coupling a continuous time Markov chain model with a deterministic dynamic model of a macroscopic variable, that is coupling Markovian channel dynamics to the time-evolution of the transmembrane potential. We extend the existing framework of PDPs in finite dimensional state space to include infinite-dimensional evolution equations and thus obtain a stochastic hybrid model suitable for modelling spatio-temporal dynamics. We derive analytic results for the infinite-dimensional process, such as existence, the strong Markov property and its extended generator. Further, we exemplify modelling of spatially extended excitable membranes with PDPs by a stochastic hybrid version of the Hodgkin–Huxley model of the squid giant axon. Finally, we discuss the advantages of the PDP formulation in view of analytical and numerical investigations as well as the application of PDPs to structurally more complex models of excitable membranes.},
pages = {1051-1093},
publisher = {Springer},
journal = {Journal of Mathematical Biology},
volume = {63},
number = {6},
year = {2011},
url = {http://www.springerlink.com/content/bt1w9gqvv223r785/},
}

Details

Zusammenfassung: In this paper, we present a mathematical description for excitable biological membranes, in particular neuronal membranes. We aim to model the (spatio-) temporal dynamics, e.g., the travelling of an action potential along the axon, subject to noise, such as ion channel noise. Using the framework of Piecewise Deterministic Processes (PDPs) we provide an exact mathematical description—in contrast to pseudo-exact algorithms considered in the literature—of the stochastic process one obtains coupling a continuous time Markov chain model with a deterministic dynamic model of a macroscopic variable, that is coupling Markovian channel dynamics to the time-evolution of the transmembrane potential. We extend the existing framework of PDPs in finite dimensional state space to include infinite-dimensional evolution equations and thus obtain a stochastic hybrid model suitable for modelling spatio-temporal dynamics. We derive analytic results for the infinite-dimensional process, such as existence, the strong Markov property and its extended generator. Further, we exemplify modelling of spatially extended excitable membranes with PDPs by a stochastic hybrid version of the Hodgkin–Huxley model of the squid giant axon. Finally, we discuss the advantages of the PDP formulation in view of analytical and numerical investigations as well as the application of PDPs to structurally more complex models of excitable membranes.

Journal: Journal of Mathematical Biology
Volume: 63
Nummer: 6
Erscheinungsjahr: 2011
Seitenreferenz: 1051-1093
Anzahl Seiten: 42
Web: http://www.springerlink.com/content/bt1w9gqvv223r785/ (Journal article)
DOI: http://dx.doi.org/10.1007/s00285-010-0395-z
Verlag: Springer
Reichweite: International

Beteiligte

AutorInnen / HerausgeberInnen: Univ.-Prof. Dr. Evelyn Buckwar, DI Ph.D. Martin Riedler

Forschungseinheiten der JKU:

Wissenschaftszweige: 1103 Analysis | 1118 Wahrscheinlichkeitstheorie | 1165 Stochastik

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