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Toward a modeling, optimization, and predictive control framework for fed‐batch metabolic cybergenetics

Espinel‐Ríos, Sebastián ; Morabito, Bruno ; Pohlodek, Johannes ; Bettenbrock, Katja ; Klamt, Steffen ; Findeisen, Rolf (2024)
Toward a modeling, optimization, and predictive control framework for fed‐batch metabolic cybergenetics.
In: Biotechnology and Bioengineering, 2024, 121 (1)
doi: 10.26083/tuprints-00027245
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Toward a modeling, optimization, and predictive control framework for fed‐batch metabolic cybergenetics
Language: English
Date: 21 May 2024
Place of Publication: Darmstadt
Year of primary publication: January 2024
Place of primary publication: New York
Publisher: Wiley
Journal or Publication Title: Biotechnology and Bioengineering
Volume of the journal: 121
Issue Number: 1
DOI: 10.26083/tuprints-00027245
Corresponding Links:
Origin: Secondary publication DeepGreen

Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes extracellular process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, and so on. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little‐to‐no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism‐relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model‐based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint‐based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model‐based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed‐batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity.

Uncontrolled Keywords: constraint‐based modeling, dynamic metabolic control, metabolic cybergenetics, model predictive control, optogenetics, state estimation
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-272452
Classification DDC: 500 Science and mathematics > 570 Life sciences, biology
600 Technology, medicine, applied sciences > 621.3 Electrical engineering, electronics
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control and Cyber-Physical Systems (CCPS)
Date Deposited: 21 May 2024 13:48
Last Modified: 23 May 2024 10:25
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/27245
PPN: 518465977
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