Cell-free biosynthesis meets dynamic optimization and control: a fed-batch framework. Issue 23 (2022)
- Record Type:
- Journal Article
- Title:
- Cell-free biosynthesis meets dynamic optimization and control: a fed-batch framework. Issue 23 (2022)
- Main Title:
- Cell-free biosynthesis meets dynamic optimization and control: a fed-batch framework
- Authors:
- Espinel-Ríos, Sebastián
Huber, Nicolas
Alcalá-Orozco, Edgar Alberto
Morabito, Bruno
Rexer, Thomas F.T.
Reichl, Udo
Klamt, Steffen
Findeisen, Rolf - Abstract:
- Abstract: Cell-free biosynthesis uses the machinery of cells, such as the metabolic reactions, to carry out conversion processes in vitro. This can be more beneficial than in vivo approaches like fermentations. Some advantages of these synthetic biology processes include higher product yields, rates and titers, and more flexibility in pathway design. Cell-free biosynthesis is still in early stages and, unlike in vivo production, there are very few examples of model-based optimization. Moreover, we encounter static optimizations in most cases, neglecting the dynamic nature of the processes. We present an optimal control framework to maximize the efficiency of cell-free biosynthesis. We focus on fed-batch setups as they allow enhancing the reaction rates via the feeding, extending production processes for longer times, and minimizing the potential negative effects of enzyme kinetics with substrate inhibition. Our framework can in principle handle several cost functions and exploit both static and dynamic degrees of freedom. An aspect that can hinder model-based optimization is model uncertainty, which can arise due to uncertain parameters, oversimplified model assumptions or unknown reaction mechanisms. To counteract this, we propose the use of model predictive control during the process operation. In addition, we outline the use of moving horizon estimation as an observer in the case of unmeasured states. We consider the de novo cell-free synthesis of uridineAbstract: Cell-free biosynthesis uses the machinery of cells, such as the metabolic reactions, to carry out conversion processes in vitro. This can be more beneficial than in vivo approaches like fermentations. Some advantages of these synthetic biology processes include higher product yields, rates and titers, and more flexibility in pathway design. Cell-free biosynthesis is still in early stages and, unlike in vivo production, there are very few examples of model-based optimization. Moreover, we encounter static optimizations in most cases, neglecting the dynamic nature of the processes. We present an optimal control framework to maximize the efficiency of cell-free biosynthesis. We focus on fed-batch setups as they allow enhancing the reaction rates via the feeding, extending production processes for longer times, and minimizing the potential negative effects of enzyme kinetics with substrate inhibition. Our framework can in principle handle several cost functions and exploit both static and dynamic degrees of freedom. An aspect that can hinder model-based optimization is model uncertainty, which can arise due to uncertain parameters, oversimplified model assumptions or unknown reaction mechanisms. To counteract this, we propose the use of model predictive control during the process operation. In addition, we outline the use of moving horizon estimation as an observer in the case of unmeasured states. We consider the de novo cell-free synthesis of uridine diphosphate-N-acetylglucosamine as a biomedical relevant case study, where we were able to maximize the volumetric productivity in simulations, and indirectly also the titer and enzyme efficiency use. … (more)
- Is Part Of:
- IFAC-PapersOnLine. Volume 55:Issue 23(2022)
- Journal:
- IFAC-PapersOnLine
- Issue:
- Volume 55:Issue 23(2022)
- Issue Display:
- Volume 55, Issue 23 (2022)
- Year:
- 2022
- Volume:
- 55
- Issue:
- 23
- Issue Sort Value:
- 2022-0055-0023-0000
- Page Start:
- 92
- Page End:
- 97
- Publication Date:
- 2022
- Subjects:
- Cell-free -- fed-batch -- optimization -- control -- state estimation -- UDP-GlcNAc
Automatic control -- Periodicals
629.805 - Journal URLs:
- https://www.journals.elsevier.com/ifac-papersonline/ ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.ifacol.2023.01.021 ↗
- Languages:
- English
- ISSNs:
- 2405-8963
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 25679.xml