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

E. Anane, Diana C. López Cárdenas, Tilman Barz, Gurkan Sin, Krist V. Gernaey, Peter Neubauer, Mariano Nicolas Cruz Bournazou

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung


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.
FachzeitschriftBiochemical Engineering Journal
PublikationsstatusVeröffentlicht - 2019

Research Field

  • Efficiency in Industrial Processes and Systems


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