  Stochastic modelling provides an indispensable tool for understanding how
random events at the molecular level influence cellular functions. In practice,
the common challenge is to calibrate a large number of model parameters against
the experimental data. A related problem is to efficiently study how the
behaviour of a stochastic model depends on its parameters, i.e. whether a
change in model parameters can lead to a significant qualitative change in
model behaviour (bifurcation). In this paper, tensor-structured parametric
analysis (TPA) is presented. It is based on recently proposed low-parametric
tensor-structured representations of classical matrices and vectors. This
approach enables simultaneous computation of the model properties for all
parameter values within a parameter space. This methodology is exemplified to
study the parameter estimation, robustness, sensitivity and bifurcation
structure in stochastic models of biochemical networks. The TPA has been
implemented in Matlab and the codes are available at http://www.stobifan.org .
