Apr 23, 2014

Robustness analysis of stochastic biochemical systems

PloS One
Milan CeskaLuboš Brim

Abstract

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 determi...Continue Reading

  • References28
  • Citations1

Mentioned in this Paper

Biochemical Pathway
Fluctuation
Stochastic Processes
HP gene
Sigmoid Colon
Hyperplastic Polyp
Complex (molecular entity)
Aggregation
Protein Phosphorylation
Anatomic Bifurcation

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