  A number of coupling strategies are presented for stochastically modeled
biochemical processes with time-dependent parameters. In particular, the
stacked coupling is introduced and is shown via a number of examples to provide
an exceptionally low variance between the generated paths. This coupling will
be useful in the numerical computation of parametric sensitivities and the fast
estimation of expectations via multilevel Monte Carlo methods. We provide the
requisite estimators in both cases.
