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The obtained models described with high accuracy, 99% Confidence Interval the dynamics of substrate utilization, product formation and cell growth.

Models described with CML-DEVS can be automatically translated (i.e. compiled) into the input language of different modeling and simulation tools.

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The approach is presented in a robust control framework with an uncertainty model described with coprime factor perturbations.

The MaxCal-generated model (described with four parameters) was benchmarked against synthetic data generated using a Gillespie algorithm on a known reaction network (with seven parameters).

Nonsignificant paths were trimmed from a model described with a series of multiple regression analyses.

Using the model described with the specified inputs, the average time for an individual patient clinic visit is 40 minutes, not including waiting time; thirty-three minutes of this time is accounted for by direct patient care and the remainder by general clinic services that indirectly benefit patients.

Simulation results evidenced that this model describes with high exactitude the process of steam pressure variation inside the boiler.

A simple parametric model is proposed Po="Pomin+(1−Pomin exp−K LAI, the two parameters (Pomin and K depending on the architectural characteristics. This model describes with great accuracy the gap fraction across development stages (RMSE = 0.0135).

The impact of the correlations was examined by repeating the parameterization of the models described below with both data sets separately and then both together.

In this section we provide a theoretical comparison of the MIP models described above with respect to optimal values of their LP-relaxations.

Finally a description of a numerical simulation model is described with examples of the effects of model variables when both analytical and simulation models are compared to observed data.

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