"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." . "9" . "stochastic models; robustness analysis; probabilistic model checking"@en . . "\u0160afr\u00E1nek, David" . "Brim, Lubo\u0161" . "1932-6203" . "4"^^ . . . "RIV/00216224:14330/14:00075751" . "4"^^ . "Robustness Analysis of Stochastic Biochemical Systems" . "Robustness Analysis of Stochastic Biochemical Systems" . "Plos One" . "23"^^ . "10.1371/journal.pone.0094553" . . . "Dra\u017Ean, Sven" . . "Robustness Analysis of Stochastic Biochemical Systems"@en . "Robustness Analysis of Stochastic Biochemical Systems"@en . "RIV/00216224:14330/14:00075751!RIV15-MSM-14330___" . "\u010Ce\u0161ka, Milan" . . . "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."@en . . "14330" . "42981" . . . . "P(EE2.3.20.0256), S" . . . . . "000335227400014" . "4" . "[2056987CC10B]" . . . "US - Spojen\u00E9 st\u00E1ty americk\u00E9" . .