Anastasis Georgoulas

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Formal modelling languages such as process algebras are widespread and effective tools in computational modelling. However, handling data and uncertainty in a statistically meaningful way is an open problem in formal modelling, severely hampering the usefulness of these elegant tools in many real world applications. Here we introduce ProPPA, a process(More)
We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules. Despite the increasing prominence of such models in fields ranging from biology to smart cities, Bayesian inference for such systems remains challenging, as these are continuous time, discrete state systems with(More)
Formal methods have long been employed to capture the dynamics of biological systems in terms of Continuous Time Markov Chains. The formal approach enables the use of elegant analysis tools such as model checking, but usually relies on a complete specification of the model of interest and cannot easily accommodate uncertain data. In contrast, data-driven(More)
We present a software tool for the automatic translation of models from the Narrative Language, a semiformal language for biological modelling, into the Bio-PEPA process algebra. This provides biologists with an easy way to describe systems and at the same time gives them access to the simulation and analysis techniques provided by Bio-PEPA. We present(More)
We present two process algebra models of a Kai-protein based circadian clock. Our models are represented in the Bio-PEPA and the continuous pi-calculus process algebras. The circadian clock is not based on transcription and has been shown to persist with a rhythmic signal when removed from a living cell. Our models allow us to speculate as to the mechanisms(More)
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