PubAg

Main content area

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

Author:
Anane, Emmanuel, López C, Diana C., Barz, Tilman, Sin, Gurkan, Gernaey, Krist V., Neubauer, Peter, Cruz Bournazou, Mariano Nicolas
Source:
Biochemical engineering journal 2019 v.150 pp. 107247
ISSN:
1369-703X
Subject:
Escherichia coli, Monte Carlo method, algorithms, biotechnology, data collection, dynamic models, growth models, mechanistic models, prediction, uncertainty, uncertainty analysis
Abstract:
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.
Agid:
6456229