  Chemical reaction networks describe interactions between biochemical species.
Once an underlying reaction network is given for a biochemical system, the
system dynamics can be modelled with various mathematical frameworks such as
continuous time Markov processes. In this manuscript, the identifiability of
the underlying network structure with a given stochastic system dynamics is
studied. It is shown that some data types related to the associated stochastic
dynamics can uniquely identify the underlying network structure as well as the
system parameters. The accuracy of the presented network inference is
investigated when given dynamical data is obtained via stochastic simulations.
