Publication details

Robustness Analysis of Stochastic Biochemical Systems

Authors

ČEŠKA Milan ŠAFRÁNEK David DRAŽAN Sven BRIM Luboš

Year of publication 2014
Type Article in Periodical
Magazine / Source Plos One
MU Faculty or unit

Faculty of Informatics

Citation
Web http://www.sciencedirect.com/science/article/pii/S1571066114000590
Doi http://dx.doi.org/10.1371/journal.pone.0094553
Field Informatics
Keywords stochastic models; robustness analysis; probabilistic model checking
Description 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.
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