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Probabilistic Verification for Systems Biology

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:

Some recent/ongoing projects in this area are:

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




Milan Ceska
(Faculty of Informatics, Masaryk University, Brno, Czech Republic)