  A version of the time-parallel algorithm parareal is analyzed and applied to
stochastic models in chemical kinetics. A fast predictor at the macroscopic
scale (evaluated in serial) is available in the form of the usual reaction rate
equations. A stochastic simulation algorithm is used to obtain an exact
realization of the process at the mesoscopic scale (in parallel).
  The underlying stochastic description is a jump process driven by the Poisson
measure. A convergence result in this arguably difficult setting is established
suggesting that a homogenization of the solution is advantageous. We devise a
simple but highly general such technique.
  Three numerical experiments on models representative to the field of
computational systems biology illustrate the method. For non-stiff problems, it
is shown that the method is able to quickly converge even when stochastic
effects are present. For stiff problems we are instead able to obtain fast
convergence to a homogenized solution.
  Overall, the method builds an attractive bridge between on the one hand,
macroscopic deterministic scales and, on the other hand, mesoscopic stochastic
ones. This construction is clearly possible to apply also to stochastic models
within other fields.
