In order to assist the performance evaluation of complex stochastic models, automatic program tools were developed since a long time. Stochastic Petri nets (SPN) are applied as an effective model description language supported by several analytical and simulation tools. The analytical description and the numerical analysis of non-Markovian stochastic Petri net models gained attention recently. ...

Recently hybrid modelling and simulation of biochemical systems have attracted increasing interest. This is motivated by the need of simulating systems which integrate different sub-cellular models, and the fact that bio networks themselves are inherently stochastic, however stochastic simulation is time expensive. Compared to other methods of biological modelling, Petri nets are characterized ...

Petri nets have been widely used to model dynamic systems, namely manufacturing systems. In this paper we introduce the use of Petri nets to model robotic tasks. Diierent views of the robotic task model can be modeled by distinct Petri net types: interpreted Petri nets for task design and execution, generalized stochastic Petri nets for task quantitative performance evaluation and ordinary Petr...

To exploit the vast data obtained from high throughput molecular biology, a variety of modelling and analysis techniques must be fully utilised. In this thesis, Petri nets are investigated within the context of computational systems biology, with the specific focus of facilitating the creation and analysis of models of biological pathways. The analysis of qualitative models of genetic networks ...

We describe the use of stochastic Petri nets (SPNs) and stochastic reward nets (SRNs) which are SPNs augmented with the ability to specify output measures as reward-based functions, for the evaluation of reliability for complex systems. The solution of SRNs involves generation and analysis of the corresponding Markov reward model. The use of SRNs in modeling complex systems is illustrated throu...

Due to the heterogeneity of many real biochemical systems, stochastic simulation methods do not scale well as systems become more complex and larger, whereas approximations provided by continuous models fail to capture the stochastic behavior of molecular species at very low numbers. A hybrid simulation method is a natural idea to resolve this dilemma. In this paper, we propose a novel notion o...