Robustness analysis of stochastic biochemical systems
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 o...
| Main Authors: | , , , |
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| Format: | Journal article |
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Public Library of Science
2014
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| _version_ | 1797069364519763968 |
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| author | Česka, M Šafránek, D Dražan, S Brim, L |
| author_facet | Česka, M Šafránek, D Dražan, S Brim, L |
| author_sort | Česka, M |
| collection | OXFORD |
| 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 |
| first_indexed | 2024-03-06T22:23:20Z |
| format | Journal article |
| id | oxford-uuid:55cdeee3-14bf-4f62-9798-336f380a0a50 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T22:23:20Z |
| publishDate | 2014 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | oxford-uuid:55cdeee3-14bf-4f62-9798-336f380a0a502022-03-26T16:46:32ZRobustness analysis of stochastic biochemical systemsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:55cdeee3-14bf-4f62-9798-336f380a0a50Department of Computer SciencePublic Library of Science2014Česka, MŠafránek, DDražan, SBrim, LWe 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 |
| spellingShingle | Česka, M Šafránek, D Dražan, S Brim, L Robustness analysis of stochastic biochemical systems |
| title | Robustness analysis of stochastic biochemical systems |
| title_full | Robustness analysis of stochastic biochemical systems |
| title_fullStr | Robustness analysis of stochastic biochemical systems |
| title_full_unstemmed | Robustness analysis of stochastic biochemical systems |
| title_short | Robustness analysis of stochastic biochemical systems |
| title_sort | robustness analysis of stochastic biochemical systems |
| work_keys_str_mv | AT ceskam robustnessanalysisofstochasticbiochemicalsystems AT safranekd robustnessanalysisofstochasticbiochemicalsystems AT drazans robustnessanalysisofstochasticbiochemicalsystems AT briml robustnessanalysisofstochasticbiochemicalsystems |