  We study networks of biochemical reactions modelled by continuous-time Markov
processes. Such networks typically contain many molecular species and reactions
and are hard to study analytically as well as by simulation. Particularly, we
are interested in reaction networks with intermediate species such as the
substrate-enzyme complex in the Michaelis-Menten mechanism. These species are
virtually in all real-world networks, they are typically short-lived, degraded
at a fast rate and hard to observe experimentally.
  We provide conditions under which the Markov process of a multiscale reaction
network with intermediate species is approximated in finite dimensional
distribution by the Markov process of a simpler reduced reaction network
without intermediate species. We do so by embedding the Markov processes into a
one-parameter family of processes, where reaction rates and species abundances
are scaled in the parameter. Further, we show that there are close links
between these stochastic models and deterministic ODE models of the same
networks.
