| KLARNER, Hannes, Adam STRECK, David ŠAFRÁNEK, Juraj KOLČÁK and Heike SIEBERT. Parameter Identification and Model Ranking of Thomas Networks. In Computational Methods in Systems Biology: 10th International Conference, CMSB 2012, London, UK, October 3-5, 2012. Proceedings. Berlin: Springer, 2012, p. 207-226. ISBN 978-3-642-33635-5. Available from: https://dx.doi.org/10.1007/978-3-642-33636-2_13. |
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@inproceedings{1071805,
author = {Klarner, Hannes and Streck, Adam and Šafránek, David and Kolčák, Juraj and Siebert, Heike},
address = {Berlin},
booktitle = {Computational Methods in Systems Biology: 10th International Conference, CMSB 2012, London, UK, October 3-5, 2012. Proceedings},
doi = {http://dx.doi.org/10.1007/978-3-642-33636-2_13},
keywords = {Thomas network; parameter identification; model checking},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Berlin},
isbn = {978-3-642-33635-5},
pages = {207-226},
publisher = {Springer},
title = {Parameter Identification and Model Ranking of Thomas Networks},
url = {http://link.springer.com/chapter/10.1007/978-3-642-33636-2_13},
year = {2012}
}
TY - JOUR
ID - 1071805
AU - Klarner, Hannes - Streck, Adam - Šafránek, David - Kolčák, Juraj - Siebert, Heike
PY - 2012
TI - Parameter Identification and Model Ranking of Thomas Networks
PB - Springer
CY - Berlin
SN - 9783642336355
KW - Thomas network
KW - parameter identification
KW - model checking
UR - http://link.springer.com/chapter/10.1007/978-3-642-33636-2_13
L2 - http://link.springer.com/chapter/10.1007/978-3-642-33636-2_13
N2 - We propose a new methodology for identification and analysis of discrete gene networks as defined by René Thomas, supported by a tool chain: (i) given a Thomas network with partially known kinetic parameters, we reduce the number of acceptable parametrizations to those that fit time-series measurements and reflect other known constraints by an improved technique of coloured LTL model checking performing efficiently on Thomas networks in distributed environment; (ii) we introduce classification of acceptable parametrizations to identify most optimal ones; (iii) we propose two ways of visualising parametrizations dynamics wrt time-series data. Finally, computational efficiency is evaluated and the methodology is validated on bacteriophage \lambda case study.
ER -
KLARNER, Hannes, Adam STRECK, David ŠAFRÁNEK, Juraj KOLČÁK and Heike SIEBERT. Parameter Identification and Model Ranking of Thomas Networks. In \textit{Computational Methods in Systems Biology: 10th International Conference, CMSB 2012, London, UK, October 3-5, 2012. Proceedings}. Berlin: Springer, 2012, p.~207-226. ISBN~978-3-642-33635-5. Available from: https://dx.doi.org/10.1007/978-3-642-33636-2\_{}13.
|
