Abstract
Despite increased understanding of cancer pathogenesis, translating this knowledge into therapy remains challenging. Radical progress depends on utilizing molecular biology knowledge to understand processes by which genetic information is executed in response to microen-vironmental perturbations. Understanding of the molecular machinery of the cell requires computer simulation of the complex network of molecular interactions and Petri net offers ideal framework. Lack of quantitative data about molecular amounts and transition rates necessitates development of qualitative methods providing useful insight with minimal knowledge on quantitative parameters. Here we show work in progress on the modeling of molecular interaction network involved in the evolution of prostate cancer. We use statistical model checking of qualitative model and show that the effects of genetic and pharmacological perturbations on prostate cancer evolution can be predicted by the number of token game trajectories reaching nodes representing proliferation and cell death events.