Online estimation of changing metabolic capacities in continuous Corynebacterium glutamicum cultivations growing on a complex sugar mixture

Peter Sinner, Marlene Stiegler, Oliver Goldbeck, Gerd M. Seibold, Christoph Herwig*, Julian Kager*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

Model-based state estimators enable online monitoring of bioprocesses and, thereby, quantitative process understanding during running operations. During prolonged continuous bioprocesses strain physiology is affected by selection pressure. This can cause time-variable metabolic capacities that lead to a considerable model-plant mismatch reducing monitoring performance if model parameters are not adapted accordingly. Variability of metabolic capacities therefore needs to be integrated in the in silico representation of a process using model-based monitoring approaches. To enable online monitoring of multiple concentrations as well as metabolic capacities during continuous bioprocessing of spent sulfite liquor with Corynebacterium glutamicum, this work presents a particle filtering framework that takes account of parametric variability. Physiological parameters are continuously adapted by Bayesian inference, using noninvasive off-gas measurements. Additional information on current parameter importance is derived from time-resolved sensitivity analysis. Experimental results show that the presented framework enables accurate online monitoring of long-term culture dynamics, whereas state estimation without parameter adaption failed to quantify substrate metabolization and growth capacities under conditions of high selection pressure. Online estimated metabolic capacities are further deployed for multi-objective optimization to identify time-variable optimal operating points. Thereby, the presented monitoring system forms a basis for adaptive control during continuous bioprocessing of lignocellulosic by-product streams. This article is protected by copyright. All rights reserved.
Original languageEnglish
JournalBiotechnology and Bioengineering
Volume119
Issue number2
Pages (from-to)575-590
Number of pages15
ISSN0006-3592
DOIs
Publication statusPublished - 2022

Keywords

  • Continuous bioprocessing
  • Model adaption
  • Nonlinear state estimation
  • Particle filter
  • Sensitivity analysis

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