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@article{1186386,
author = {Češka, Milan and Šafránek, David and Dražan, Sven and Brim, Luboš},
article_location = {SAN FRANCISCO},
article_number = {4},
doi = {http://dx.doi.org/10.1371/journal.pone.0094553},
keywords = {stochastic models; robustness analysis; probabilistic model checking},
language = {eng},
issn = {1932-6203},
journal = {Plos One},
title = {Robustness Analysis of Stochastic Biochemical Systems},
url = {http://www.sciencedirect.com/science/article/pii/S1571066114000590},
volume = {9},
year = {2014}
}
TY - JOUR
ID - 1186386
AU - Češka, Milan - Šafránek, David - Dražan, Sven - Brim, Luboš
PY - 2014
TI - Robustness Analysis of Stochastic Biochemical Systems
JF - Plos One
VL - 9
IS - 4
SP - 1-23
EP - 1-23
PB - PUBLIC LIBRARY SCIENCE
SN - 19326203
KW - stochastic models
KW - robustness analysis
KW - probabilistic model checking
UR - http://www.sciencedirect.com/science/article/pii/S1571066114000590
N2 - We propose a new framework for rigorous robustness analysis of stochastic biochemical systems that is based on probabilistic model checking techniques. We adapt the general definition of robustness introduced by Kitano to the class of stochastic systems modelled as continuous time Markov Chains in order to extensively analyse and compare robustness of biological models with uncertain parameters. The framework utilises novel computational methods that enable to effectively evaluate the robustness of models with respect to quantitative temporal properties and parameters such as reaction rate constants and initial conditions. We have applied the framework to gene regulation as an example of a central biological mechanism where intrinsic and extrinsic stochasticity plays crucial role due to low numbers of DNA and RNA molecules. Using our methods we have obtained a comprehensive and precise analysis of stochastic dynamics under parameter uncertainty. Furthermore, we apply our framework to compare several variants of two-component signalling networks from the perspective of robustness with respect to intrinsic noise caused by low populations of signalling components. We have successfully extended previous studies performed on deterministic models (ODE) and showed that stochasticity may significantly affect obtained predictions. Our case studies demonstrate that the framework can provide deeper insight into the role of key parameters in maintaining the system functionality and thus it significantly contributes to formal methods in computational systems biology.
ER -
ČEŠKA, Milan, David ŠAFRÁNEK, Sven DRAŽAN a Luboš BRIM. Robustness Analysis of Stochastic Biochemical Systems. \textit{Plos One}. SAN FRANCISCO: PUBLIC LIBRARY SCIENCE, roč.~9, č.~4, s.~1-23. ISSN~1932-6203. doi:10.1371/journal.pone.0094553. 2014.
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