Symbolic model checking for real-time systems
Information and Computation
Model checking
Analysis of Timed Systems Using Time-Abstracting Bisimulations
Formal Methods in System Design
Taming the complexity of biochemical models through bisimulation and collapsing: theory and practice
Theoretical Computer Science - Special issue: Computational systems biology
TCTL Inevitability Analysis of Dense-Time Systems: From Theory to Engineering
IEEE Transactions on Software Engineering
Model checking genetic regulatory networks with parameter uncertainty
HSCC'07 Proceedings of the 10th international conference on Hybrid systems: computation and control
Machine learning biochemical networks from temporal logic properties
Transactions on Computational Systems Biology VI
Parallel Model Checking Large-Scale Genetic Regulatory Networks with DiVinE
Electronic Notes in Theoretical Computer Science (ENTCS)
Parameter Synthesis in Nonlinear Dynamical Systems: Application to Systems Biology
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
On algorithmic analysis of transcriptional regulation by LTL model checking
Theoretical Computer Science
Model checking genetic regulatory networks with parameter uncertainty
HSCC'07 Proceedings of the 10th international conference on Hybrid systems: computation and control
Hybrid systems and biology: continuous and discrete modeling for systems biology
SFM'08 Proceedings of the Formal methods for the design of computer, communication, and software systems 8th international conference on Formal methods for computational systems biology
Dynamically-Driven timed automaton abstractions for proving liveness of continuous systems
FORMATS'12 Proceedings of the 10th international conference on Formal Modeling and Analysis of Timed Systems
Timed Modelling of Gene Networks with Arbitrarily Precise Expression Discretization
Electronic Notes in Theoretical Computer Science (ENTCS)
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Recent studies have demonstrated the possibility to build genetic regulatory networks that confer a desired behavior to a living organism. However, the design of these networks is difficult, notably because of uncertainties on parameter values. In previous work, we proposed an approach to analyze genetic regulatory networks with parameter uncertainties. In this approach, the models are based on piecewise-multiaffine (PMA) differential equations, the specifications are expressed in temporal logic, and uncertain parameters are given by intervals. Abstractions are used to obtain finite discrete representations of the dynamics of the system, amenable to model checking. However, the abstraction process creates spurious behaviors along which time does not progress, called time-converging behaviors. Consequently, the verification of liveness properties, expressing that something will eventually happen, and implicitly assuming progress of time, often fails. In this work, we extend our previous approach to enforce progress of time. More precisely, we define transient regions as subsets of the state space left in finite time by every solution trajectory, show how they can be used to rule out time-converging behaviors, and provide sufficient conditions for their identification in PMA systems. This approach is implemented in RoVerGeNe and applied to the analysis of a network built in the bacterium E. coli.