  We present a novel multiscale simulation approach for modeling stochasticity
in chemical reaction networks. The approach seamlessly integrates
exact-stochastic and "leaping" methodologies into a single "partitioned
leaping" algorithmic framework. The technique correctly accounts for stochastic
noise at significantly reduced computational cost, requires the definition of
only three model-independent parameters and is particularly well-suited for
simulating systems containing widely disparate species populations. We present
the theoretical foundations of partitioned leaping, discuss various options for
its practical implementation and demonstrate the utility of the method via
illustrative examples.
