  We present a novel and simple method to numerically calculate Fisher
Information Matrices for stochastic chemical kinetics models. The linear noise
approximation is used to derive model equations and a likelihood function which
leads to an efficient computational algorithm. Our approach reduces the problem
of calculating the Fisher Information Matrix to solving a set of ordinary
differential equations. {This is the first method to compute Fisher Information
for stochastic chemical kinetics models without the need for Monte Carlo
simulations.} This methodology is then used to study sensitivity, robustness
and parameter identifiability in stochastic chemical kinetics models. We show
that significant differences exist between stochastic and deterministic models
as well as between stochastic models with time-series and time-point
measurements. We demonstrate that these discrepancies arise from the
variability in molecule numbers, correlations between species, and temporal
correlations and show how this approach can be used in the analysis and design
of experiments probing stochastic processes at the cellular level. The
algorithm has been implemented as a Matlab package and is available from the
authors upon request.
