Smooth muscle modeling and experimental identification: application to bladder isometric contraction

Jeremy Laforet 1 David Guiraud 2, 1, * David Andreu 2, 1 Hubert Taillades 3 Christine Azevedo Coste 1
* Auteur correspondant
1 DEMAR - Artificial movement and gait restoration
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : This paper presents an original smooth muscle model based on the Huxley microscopic approach. This model is the main part of a comprehensive lower urinary track model. The latter is used for simulation studies and is assessed through experiments on rabbits, for which a subset of parameters is estimated, using intravesical pressure measurements in isometric conditions. Bladder contraction is induced by electrical stimulation that determines the onset and thus synchronizes simulation and experimental data. Model sensitivity versus parameter accuracy is discussed and allows the definition of a subset of four parameters that must be accurately identified in order to obtain good fitting between experimental and acquired data. Preliminary experimental data are presented as well as model identification results. They show that the model is able to follow the pressure changes induced by an artificial stimulus in isometric contractions. Moreover, the model gives an insight into the internal changes in calcium concentration and the ratio of the different chemical species present in the muscle cells, in particular the bounded and unbounded actin and myosin and the normalized concentration of intracellular calcium.
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Journal of Neural Engineering, IOP Publishing, 2011, 8 (3), pp.13. 〈10.1088/1741-2560/8/3/036024〉
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Contributeur : David Guiraud <>
Soumis le : mardi 31 mai 2011 - 13:44:24
Dernière modification le : jeudi 22 février 2018 - 17:48:01

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Jeremy Laforet, David Guiraud, David Andreu, Hubert Taillades, Christine Azevedo Coste. Smooth muscle modeling and experimental identification: application to bladder isometric contraction. Journal of Neural Engineering, IOP Publishing, 2011, 8 (3), pp.13. 〈10.1088/1741-2560/8/3/036024〉. 〈lirmm-00597214〉

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