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.
- Parameter identifiability
- Ill-Conditioning analysis
- Uncertainty analysis
Anane, E., López C, D. C., Barz, T., Sin, G., Gernaey, K. V., Neubauer, P., & Cruz Bournazou, M. N. (2019). Output uncertainty of dynamic growth models: Effect of uncertain parameter estimates on model reliability. Biochemical Engineering Journal, 150, . https://doi.org/10.1016/j.bej.2019.107247