Is There a Best Symbolic Cycle-Detection Algorithm?
TACAS 2001 Proceedings of the 7th International Conference on Tools and Algorithms for the Construction and Analysis of Systems
Symbolic Model Checking of Biochemical Networks
CMSB '03 Proceedings of the First International Workshop on Computational Methods in Systems Biology
The potential of the cell processor for scientific computing
Proceedings of the 3rd conference on Computing frontiers
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
Parallel Model Checking Large-Scale Genetic Regulatory Networks with DiVinE
Electronic Notes in Theoretical Computer Science (ENTCS)
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Model checking genetic regulatory networks with parameter uncertainty
HSCC'07 Proceedings of the 10th international conference on Hybrid systems: computation and control
Model checking liveness properties of genetic regulatory networks
TACAS'07 Proceedings of the 13th international conference on Tools and algorithms for the construction and analysis of systems
Distributed explicit fair cycle detection: set based approach
SPIN'03 Proceedings of the 10th international conference on Model checking software
Probabilistic model checking of complex biological pathways
CMSB'06 Proceedings of the 2006 international conference on Computational Methods in Systems Biology
DiVinE: a tool for distributed verification
CAV'06 Proceedings of the 18th international conference on Computer Aided Verification
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Studies of cells in silico can greatly reduce the need for expensive and prolonged laboratory experimentation. The use of model checking for the analysis of biological networks has attracted much attention recently. The practical limitations are still the size of the model, and the time needed to generate the state space. This paper is focused on the model checking approach for analysis of piecewise-linear deterministic models of genetic regulatory networks. Firstly, the qualitative simulation algorithm of de Jong et al. that builds the heart of Genetic Network Analyzer (GNA) is revisited and its time complexity is studied in detail. Secondly, a novel algorithm that reduces the state space generation time is introduced. The new algorithm is developed as an abstraction of the original GNA algorithm. Finally, a fragment of linear time temporal logic for which the provided abstraction is conservative is identified. Efficiency of the new algorithm when implemented in the parallel model checking environment is demonstrated on a set of experiments performed on randomly modified biological models. In general, the achieved results bring a new insight into the field of qualitative simulation emerging in the context of systems biology.