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Output uncertainty of dynamic growth models: Effect of uncertain parameter estimates on model reliability

Anane, Emmanuel, López C, Diana C., Barz, Tilman, Sin, Gurkan, Gernaey, Krist V., Neubauer, Peter, Cruz Bournazou, Mariano Nicolas
Biochemical engineering journal 2019 v.150 pp. 107247
Escherichia coli, Monte Carlo method, algorithms, biotechnology, data collection, dynamic models, growth models, mechanistic models, prediction, uncertainty, uncertainty analysis
Mechanistic models are simplifications of bio-physical systems, for which the true values of the model parameters are sometimes unknown. Therefore, before using model-based predictions to study or improve a process, it is essential to ensure that the outputs of the model are reliable.This paper covers the development and application of a framework for practical identifiability and uncertainty analyses of dynamic growth models for bioprocesses. By exploring the numerical properties of the sensitivity matrix, a simple algorithm to determine the presence of non-identifiable parameters in models with high output uncertainty is presented. The framework detects the existence of non-identifiable parameters within the model and proposes a regularisation technique, in conjunction with Monte Carlo Analysis. As an example, the framework was used to analyse a macro-kinetic growth model of Escherichia coli describing a fed-batch process. The results show a reduction in the uncertainty of model outputs from a maximum coefficient of variation of 748% to 5% after regularization, and a 15-fold improvement in the accuracy of model predictions for two independent validation datasets. The presented framework aims to improve the reliability of model predictions and promote a more thorough handling of dynamical models to extend their use in biotechnology.