Runtime Verification of Biological Systems
Complex computational systems are ubiquitous and their study increasingly important. Given the ease with which it is possible to construct large systems with heterogeneous technology, there is strong motivation to provide automated means to verify their safety, efficiency and reliability. In another context, biological systems are supreme examples of complex systems for which there are no design specifications. In both cases it is usually difficult to reason at the level of the description of the systems and much more convenient to investigate properties of their executions.
To demonstrate runtime verification of complex systems we apply statistical model checking techniques to a model of robust biological oscillations taken from the literature. The model demonstrates some of the mechanisms used by biological systems to maintain reliable performance in the face of inherent stochasticity and is therefore instructive. To perform our investigation we use two recently developed SMC platforms: that incorporated in Uppaal and Plasma. Uppaal-smc offers a generic modeling language based on stochastic hybrid automata, while Plasma aims at domain specific support with the facility to accept biological models represented in chemical syntax.
Keywordsruntime verification synthetic biology statistical model checking genetic oscillator MITL frequency domain analysis Uppaal-smc Plasma
Unable to display preview. Download preview PDF.
- 3.Barkai, N., Leibler, S.: Biological rhythms: Circadian clocks limited by noise. Nature 403, 267–268 (2000)Google Scholar
- 11.Hérault, T., Lassaigne, R., Magniette, F., Peyronnet, S.: Approximate Probabilistic Model Checking. In: Steffen, B., Levi, G. (eds.) VMCAI 2004. LNCS, vol. 2937, pp. 307–329. Springer, Heidelberg (2004)Google Scholar
- 17.Laplante, S., Lassaigne, R., Magniez, F., Peyronnet, S., de Rougemont, M.: Probabilistic abstraction for model checking: An approach based on property testing. ACM TCS 8(4) (2007)Google Scholar
- 22.Sedwards, S.: A Natural Computation Approach To Biology: Modelling Cellular Processes and Populations of Cells With Stochastic Models of P Systems. PhD thesis, University of Trento (2009)Google Scholar