Abstract

Bio-PEPA is a novel stochastic process algebra which has been recently developed for modelling biological pathways.  In Bio-PEPA a reagent-centric style of modelling is adopted, and a variety of different analysis techniques can be applied to a single model expression.  Such an approach facilitates easy validation of analysis results when the analyses address the same issues and enhanced insight when the analyses are complementary. Currently supported analysis techniques include stochastic simulation at the molecular level, ordinary differential equations, probabilistic model checking and numerical analysis of a continuous time Markov chain. This talk will introduce the Bio-PEPA formalism and discuss the mapping
to the different analysis techniques.

CV

Jane Hillston is Professor of Quantitative Modelling in the School of Informatics at the University of Edinburgh and holds an Advanced Research Fellowship from the Engineering and Physical Sciences Research Council.
Her principal research interests are in the use of stochastic process algebras to model and analyse computer systems and intracellular pathways, and the development of efficient solution techniques for such models.
Prof Hillston received the BA and MS degrees in Mathematics from the University of York (UK) and Lehigh University (USA), respectively. After a brief period working in industry, she joined the Department of Computer Science at the University of Edinburgh, as a research assistant in 1989.  She received the PhD degree in computer science from that university in 1994. Her work on the stochastic process algebra PEPA was recognized by the British Computer Society in 2004 who awarded her the first Roger Needham Award.