Stochastic Reaction Networks (SRNs)\, that are int ended to describe the time evolution of interactin g particle systems where one particle interacts wi th the others through a finite set of reaction cha nnels. SRNs have been mainly developed to model bi ochemical reactions but they also have application s in neural networks\, virus kinetics\, and dynami cs of social networks\, among others. \;

Regarding simulation\, our novel Multi-level Monte Carlo (MLMC) hybrid methods prov ide accurate estimates of expected values of a giv en observable at a prescribed final time. They con trol the global approximation error up to a user-s elected accuracy and up to a certain confidence le vel\, with near optimal computational work. \;

With respect to statistical inference\, we first present a multi-scale approach\, where we i ntroduce a deterministic systematic way of using u p-scaled likelihoods for parameter estimation. In a second approach\, we derive a new forward-revers e representation for simulating stochastic bridges between consecutive observations. This allows us to use the well-known EM Algorithm to infer the re action rates. \;

LOCATION:Seminar Room 1\, Newton Institute CONTACT:INI IT END:VEVENT END:VCALENDAR