costly [@Gunawan2005; @Plyasunov2007], and often involve simulating many times in order to obtain Monte Carlo estimates of sensitivity coefficients. The development of efficient methods for stochastic sensitivity analyses has been the focus of much recent research [@Plyasunov2007; @Komorowski2012b; @Sheppard2012].

In the context of our proposed approach, a natural and straightforward way to assess parameter sensitivity is to consider the rate at which the moments vary with the parameters. This motivates the calculation of simple sensitivity coefficients [@Varma1999; @Saltelli2004] of the form $s_{ij}(t) = \frac{\partial m_i(t, \boldsymbol{\theta})}{\partial \theta_j}$, where $m_i$ is the (estimated) $i$-th moment and $\theta_j$ is the $j$-th parameter. Within our moment approximation framework, the $s_{ij}$’s may either be estimated by perturbing the model’s parameters and computing a finite difference approximation, or obtained automatically by employing the CVODES solver of @Serban2003 when solving the system of ODEs (Equations and ). In Figure \[sens-fig01\], we reconsider the dimerisation model of Section III A. We focus upon the sensitivity of the mean and 2nd and 3rd central moments of the two molecular species to the parameter $k_1$ (similar results are obtained for the sensitivity to $k_2$). Figures \[sens-fig01\]a, d and g show how the moments estimated from 100,000 SSA runs vary 