Soft sensor for monitoring dynamic changes in cell composition

Espinel-Ríos, Sebastián; Morabito, Bruno; Bettenbrock, Katja; Klamt, Steffen; Findeisen, Rolf

doi:10.1016/j.ifacol.2023.01.022

Cited by 3 publications

(2 citation statements)

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“…However, typically there are no commercial sensors for the complete intracellular biomass composition. To circumvent this challenge, some state estimators have been proposed for reconstructing the biomass composition (Espinel‐Ríos, Morabito, Bettenbrock, et al, 2022; Jabarivelisdeh et al, 2020). In the next section, we briefly describe the use of a full information estimator—an optimization‐based estimator that considers the process dynamics and process constraints, as well as past and current measurements.…”

Section: Model Predictive Control For Metabolic Cybergeneticsmentioning

confidence: 99%

“…In the next section, we briefly describe the use of a full information estimator—an optimization‐based estimator that considers the process dynamics and process constraints, as well as past and current measurements. For more details, we refer the reader to Espinel‐Ríos, Morabito, Bettenbrock, et al (2022).…”

Section: Model Predictive Control For Metabolic Cybergeneticsmentioning

confidence: 99%

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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: Model Predictive Control For Metabolic Cybergeneticsmentioning

confidence: 99%

Section: Model Predictive Control For Metabolic Cybergeneticsmentioning

confidence: 99%