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The quantile-quantile (q-q) plot in Figure 8 shows the quantile deviations of the models NW, RW with respect to a homogeneous model WW.
This implies that the unavoidable deviations of the models from the native structures would not significantly affect the position of the specificity pocket residues and, therefore, would not alter the conclusions of our structural analysis.
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An implicit unconditionally stable numerical difference scheme of the linear form of the derived model is used to illustrate deviations of the model from the homogeneous medium.
Hence, the deviations of the model from the experimental results are close to the range of variability of the experimental data.
In modeling experiments, the parameters were chosen to minimize the summed absolute deviations of the model from the mean data on the time courses and life histories of the puncta or structures in each experimental condition.
Stochastic approaches offer a natural way of assessing deviations of the model output from data based on the likelihood function.
However, many biological and biochemical systems operate close to a steady-state where a linearised version of a detailed non-linear model well describes the dynamics of the deviations of the model variables around such a steady-state.
The PDE view also facilitates model assessment and parameter estimation studies, since the model likelihood naturally offers a way of assessing deviations of the model output from experimental data.
Table 1 includes the averages and the standard deviations of the model parameters (Kmax and Sl), expressed in time terms, obtained from the fitting of the UV-H survival curves of all microorganisms tested at different temperatures.
The y-axis is the average AUC and standard deviation of the models.
Figure 7 Confidence intervals calculated from the ensemble standard deviation of the models present in the model ensembles.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com