In this paper, we present the features of Romeo, a Time Petri Net (TPN) analyzer. The tool Romeo allows state space computation of TPN and on-the-fly model-checking of reachability properties. It performs translations from TPNs to Timed Automata (TAs) that preserve the behavioural semantics (timed bisimilarity) of the TPNs. Besides, our tool also deals with an extension of Time Petri Nets (Scheduling-TPNs) for which the valuations of transitions may be stopped and resumed, thus allowing the modeling preemption.

The analysis of the dynamics of Biological Regulatory Networks (BRNs) requires innovative methods to cope with the state-space explosion. This paper settles an original approach for deciding reachability properties based onProcess Hitting, which is a framework suitable for modelling dynamical complex systems. In particular, Process Hitting has been shown to be of interest in providing compact models of the dynamics of BRNs with discrete values. Process Hitting splits a finite number of processes into so-called sorts and describes the way each process is able to act upon (that is, to ‘hit’) another one (or itself) in order to ‘bounce’ it as another process of the same sort with further actions.By using complementary abstract interpretations of the succession of actions in Process Hitting, we build a very efficient static analysis to over- and under-approximate reachability properties, which avoids the need to build the underlying states graph. The analysis is proved to have a low theoretical complexity, in particular when the number of processes per sorts is limited, while a very large number of sorts can be managed.This makes such an approach very promising for the scalable analysis of abstract complex systems. We illustrate this through the analysis of a large BRN of 94 components. Our method replies quasi-instantaneously to reachability questions, while standard model-checking techniques regularly fail because of the combinatoric explosion of behaviours.

International audienceIn this paper, we introduce a framework allowing to model and analyse efficiently Gene Regulatory Networks in their temporal and stochastic aspects. The analysis of stable states and inference of René Thomas' discrete parameters derives from this logical formalism. We offer a compositional approach which comes with a natural translation to the Stochastic π-Calculus. The method we propose consists in successive refinements of generalized dynamics of Gene Regulatory Networks. We apply this method to the control of the differentiation in a Gene Regulatory Network generalizing metazoan segmentation processes

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