  Hybrid modeling provides an effective solution to cope with multiple time
scales dynamics in systems biology. Among the applications of this method, one
of the most important is the cell cycle regulation. The machinery of the cell
cycle, leading to cell division and proliferation, combines slow growth,
spatio-temporal re-organisation of the cell, and rapid changes of regulatory
proteins concentrations induced by post-translational modifications. The
advancement through the cell cycle comprises a well defined sequence of stages,
separated by checkpoint transitions. The combination of continuous and discrete
changes justifies hybrid modelling approaches to cell cycle dynamics. We
present a piecewise-smooth version of a mammalian cell cycle model, obtained by
hybridization from a smooth biochemical model. The approximate hybridization
scheme, leading to simplified reaction rates and binary event location
functions, is based on learning from a training set of trajectories of the
smooth model. We discuss several learning strategies for the parameters of the
hybrid model.
