and also including the skewness is not enough. [When comparing the skewness of the p53 distribution with the skewness of the Michaelis-Menten enzyme kinetics system (Figure \[mmfigure1\]e), we find that the maximum value of the skewness for both systems is approximately equal, and in both systems the skewness does not have a large effect on the mean.]{}

![Analysis of distribution for p53 model. (a) Variance calculated based on SSA runs. (b) Variance calculated with SSA, LNA and moment approximation method. (c) Skewness calculated based on SSA runs (blue line) and skewness for normal distribution (cyan dashed line). (d-f) Histograms calculated based on SSA for points d, e and f in figure (c), and probability densitiy function of normal distribution calculated using mean and variance based on SSA (cyan line).[]{data-label="p53figure2"}](Figure4){width="48.00000%"}

![\[\][]{data-label="contour1"}](Figure5){width="50.00000%"}

Parameter Sensitivity Estimation
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Assessing parameter sensitivity is a key concern when fitting any parametric model [@Saltelli2004; @Erguler2011]. Such analyses allow us to quantify how rapidly the outputs of our model change as we vary its parameters, which can provide insights into the robustness of the model and the relative influence that each parameter has upon the model’s behaviour. However, sensitivity analyses of stochastic models can be difficult and/or computationally 