Bioprocess Monitoring: A Moving Horizon Estimation Experimental Application

Tuveri, Andrea; Holck, Haakon Eng; Nakama, Caroline S.M.; Matias, José; Jäschke, Johannes; Imsland, Lars; Bar, Nadav

doi:10.1016/j.ifacol.2022.07.448

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…It is worth noting that other state estimation methods, such as Kalman filters, have been proposed in the literature for inferring unmeasured states (Elsheikh et al, 2021;Haseltine & Rawlings, 2005;Tuveri et al, 2021Tuveri et al, , 2022. Kalman filters, however, are most effective with unconstrained systems.…”

Section: Reconstructing Unmeasured Cell Componentsmentioning

confidence: 99%

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

Espinel‐Ríos,

Morabito,

Pohlodek

et al. 2023

Biotech & Bioengineering

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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.

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Section: Reconstructing Unmeasured Cell Componentsmentioning

confidence: 99%

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

Espinel‐Ríos,

Morabito,

Pohlodek

et al. 2023

Biotech & Bioengineering

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Moving Horizon Parameter Estimation for an Enzyme‐Catalyzed Transamination Reaction with Integrated Product Removal

Behrens,

Tiedemann,

Kunde

et al. 2024

Chemie Ingenieur Technik

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Chiral amines are key building blocks in producing active pharmaceutical ingredients and can be synthesized via the amine transaminase pathway under mild conditions with eco‐friendly solvents. For model‐based design and control, mathematical models can be utilized. This requires reliable parameterization of the model. In this study, a model for the production of (S)‐(3 methoxyphenyl)‐ethylamine is presented. Parameter identifiability is analyzed using the method of profile likelihoods. The parameter identifiability is then tested in two different batch operation scenarios by using moving horizon estimation, which can be applied for on‐line model adaption and control purposes. In a batch process, identifiability decreases over time. A repetitive batch strategy improves parameter identifiability.

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A regularized Moving Horizon Estimator for combined state and parameter estimation in a bioprocess experimental application

Tuveri¹,

Nakama²,

Matias³

et al. 2023

Computers & Chemical Engineering

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Bioprocess Monitoring: A Moving Horizon Estimation Experimental Application

Cited by 6 publications

References 19 publications

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

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

Moving Horizon Parameter Estimation for an Enzyme‐Catalyzed Transamination Reaction with Integrated Product Removal

A regularized Moving Horizon Estimator for combined state and parameter estimation in a bioprocess experimental application

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