In this work, a methodology for the model-based identifiable parameter determination (MBIPD) is presented. This systematic approach is proposed to be used for structure and parameter identification of nonlinear models of biological reaction networks. Usually, this kind of problems are over-parameterized with large correlations between parameters. Hence, the related inverse problems for parameter determination and analysis are mathematically ill-posed and numerically difficult to solve. The proposed MBIPD methodology comprises several tasks: (i) model selection, (ii) tracking of an adequate initial guess, and (iii) an iterative parameter estimation step which includes an identifiable parameter subset selection (SsS) algorithm and accuracy analysis of the estimated parameters. The SsS algorithm is based on the analysis of the sensitivity matrix by rank revealing factorization methods. Using this, a reduction of the parameter search space to a reasonable subset, which can be reliably and efficiently estimated from available measurements, is achieved. The simultaneous saccharification and fermentation (SSF) process for bio-ethanol production from cellulosic material is used as case study for testing the methodology. The successful application of MBIPD to the SSF process demonstrates a relatively large reduction in the identified parameter space. It is shown by a cross-validation that using the identified parameters (even though the reduction of the search space), the model is still able to predict the experimental data properly. Moreover, it is shown that the model is easily and efficiently adapted to new process conditions by solving reduced and well conditioned problems.

In this work, the development and application of published models for describing the behavior of plant cell cultures is reviewed. The structure of each type of model is analyzed and the new tendencies for the modeling of biotechnological processes that can be applied in plant cell cultures are presented. This review is a tool for clarifying the main features that characterize each type of model in the field of plant cell cultures and can be used as a support on the selection of the more suitable model type, taking into account the purpose of specific research.

In this work, a mechanistic model for predicting the dynamic behavior of extracellular and intracellular nutrients, biomass production, and the main metabolites involved in the central carbon metabolism in plant cell cultures of Thevetia peruviana is presented. The proposed model is the first mechanistic model implemented for plant cell cultures of this species, and includes 28 metabolites, 33 metabolic reactions, and 61 parameters. Given the over-parametrization of the model, its nonlinear nature and the strong correlation among the effects of the parameters, a parameter estimation routine based on identifiability analysis was implemented. This routine reduces the parameter's search space by selecting the most sensitive and linearly independent parameters. Results have shown that only 19 parameters are identifiable. Finally, the model was used for analyzing the fluxes distribution in plant cell cultures of T. peruviana. This analysis shows high uptake of phosphates and parallel uptake of glucose and fructose. Furthermore, it has pointed out the main central carbon metabolism routes for promoting biomass production in this cell culture.