Understanding biological processes, and especially how the cells interact and make decisions, is an essential step towards predictive biology and personalized medicine. Systems biology seeks sound scientific understanding of biological processes through a cycle of experimental research and hypothesis generation formed with the help of computational tools for in silico modelling. Probabilistic models arise naturally as a representation of behaviour of, for example, biochemical signalling pathways, gene networks and cellular interactions. The models can be formulated in stochastic process calculi and analysed using techniques such as stochastic simulation as well as probabilistic model checking.

Our work in this area involves:

• formulating (discrete stochastic) models of biological systems such as signalling pathways and applying probabilistic verification to support hypothesis forming, confirmation or refutation, and to guide selection of experiment
• investigating the theoretical foundations for methods for scalable analysis of quantitative properties of biological systems
• developing software tools and implementation techniques to support the formulation and analysis of biological systems

Some recent/ongoing projects in this area are:

• Predictive modeling of signalling pathways via probabilistic model checking with PRISM
• e-Science Pilot Project on Integrative Biology
• CancerGrid: Open standards for clinical cancer informatics

There is also a list of related publications and some pointers to relevant case studies.