Model-Checking Algorithms for Continuous-Time Markov Chains
IEEE Transactions on Software Engineering
Process Algebras for Quantitative Analysis
LICS '05 Proceedings of the 20th Annual IEEE Symposium on Logic in Computer Science
Tuning Systems: From Composition to Performance
The Computer Journal
APMC 3.0: Approximate Verification of Discrete and Continuous Time Markov Chains
QEST '06 Proceedings of the 3rd international conference on the Quantitative Evaluation of Systems
Probabilistic model checking of complex biological pathways
Theoretical Computer Science
Bio-PEPA: An Extension of the Process Algebra PEPA for Biochemical Networks
Electronic Notes in Theoretical Computer Science (ENTCS)
Relating continuous and discrete PEPA models of signalling pathways
Theoretical Computer Science
Analysis of signalling pathways using continuous time markov chains
Transactions on Computational Systems Biology VI
Transactions on Computational Systems Biology VII
PRISM: a tool for automatic verification of probabilistic systems
TACAS'06 Proceedings of the 12th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Some Investigations Concerning the CTMC and the ODE Model Derived From Bio-PEPA
Electronic Notes in Theoretical Computer Science (ENTCS)
Query-based verification of qualitative trends and oscillations in biochemical systems
Theoretical Computer Science
Design and development of software tools for Bio-PEPA
Winter Simulation Conference
Transactions on computational systems biology XIII
Trend-Based analysis of a population model of the AKAP scaffold protein
Transactions on Computational Systems Biology XIV
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Model-checking can provide valuable insight into the behaviour of biochemical systems, answering quantitative queries which are more difficult to answer using stochastic simulation alone. However, model-checking is a computationally intensive technique which can become infeasible if the system under consideration is too large. Moreover, the finite nature of the state representation used means that a priori bounds must be set for the numbers of molecules of each species to be observed in the system. In this paper we present an approach which addresses these problems by using stochastic simulation and the PRISM model checker in tandem. The stochastic simulation identifies reasonable bounds for molecular populations in the context of the considered experiment. These bounds are used to parameterise the PRISM model and limit its state space. A simulation pre-run identifies interesting time intervals on which model-checking should focus, if this information is not available from experimental data.