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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.
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.
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.
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As can be seen in Fig. 7, the means and standard deviations of the model-data differences for the Y component are quite stable when changing the number of annual differences used (one, four or twelve, see above), with a few exceptions (for example, model A).
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.
The standard deviation of the model error was around 6 dB for the best model.
For neural nets, they are summarized as the mean and standard deviation of the model accuracy.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com