When the analysis indicates that parameter variation does not impact the model output, the parameter value can be fixed.
Moreover, the sensitivity of the Bürger Diehl model outputs to parameters is highly impacted by the imposed simulation conditions, thus resulting in different parameter subsets for model calibration.
The form of this model itself suggests that small changes in quenching parameters will have the largest effects on the model output, because quenching parameters appear only in the numerator of the expression function, while the repressor scaling factor and cooperativity parameters of the same form appear in both the numerator and denominator [Figure 1].
The model's parameter space was explored to quantify model output sensitivities to parameters characterizing properties for the gel drug product (volume, drug transport, initial loading) and host environment (thicknesses of the mucosal epithelium and stroma and the role of ambient vaginal fluid in diluting gel).
However, proper model implementation will require verification of the model against known output parameters.
Apart from modifications within H3 and H4 type histone, we are currently investigating H2A and H2B [22] modifications and their influence on the model evolution and output parameters.
The key driver of water flow through trees is the soil-to-leaf water potential gradient and hence this was designed as the model output, with input parameters of soil hydration (Ψpredawn) and leaf air vapour pressure deficit (VPD).
For a particular system, varying cooperativity or transcription factor activity alone may have little effect on model output, thus these parameters would have small first-order sensitivity indices, because the two effects can "cancel each other out" or compensate.
Watkins et al. (1996) compared the advantages and disadvantages of various GIS/Model interfaces and showed how the spatial analysis and visualization potential of GIS could be used to enhance parameter estimation/determination, scale effects, grid design, access the responsiveness of model outputs to parameter uncertainty and model discretization.
