Efficient and Simple Gaussian Process Supported Stochastic Model Predictive Control for Bioreactors using HILO-MPC

Morabito, Bruno; Pohlodek, Johannes; Kranert, Lena; Espinel-Ríos, Sebastián; Findeisen, Rolf

doi:10.1016/j.ifacol.2022.07.562

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…67−69 In this context, if one manages to capture the uncertainty of the hybrid model, e.g., via Gaussian processes, stochastic optimization and control approaches could be unlocked, which offer in principle more robustness and can deal with probability constraints. 67,70…”

Section: Preliminary Accuracy Check Of the Constraint-based Modelmentioning

confidence: 99%

Hybrid Physics-Informed Metabolic Cybergenetics: Process Rates Augmented with Machine-Learning Surrogates Informed by Flux Balance Analysis

Espinel-Ríos,

Avalos

2024

Ind. Eng. Chem. Res.

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Metabolic cybergenetics is a promising concept that interfaces gene expression and cellular metabolism with computers for real-time dynamic metabolic control. The focus is on control at the transcriptional level, serving as a means to modulate intracellular metabolic fluxes. Recent strategies in this field have employed constraint-based dynamic models for process optimization, control, and estimation. However, this results in bilevel dynamic optimization problems, which pose considerable numerical and conceptual challenges. In this study, we present an alternative hybrid physics-informed dynamic modeling framework for metabolic cybergenetics, aimed at simplifying optimization, control, and estimation tasks. By utilizing machine-learning surrogates, our approach effectively embeds the physics of metabolic networks into the process rates of structurally simpler macrokinetic models coupled with gene expression. These surrogates, informed by flux balance analysis, link the domains of manipulatable intracellular enzymes to metabolic exchange fluxes. This ensures that critical knowledge captured by the system's metabolic network is preserved. The resulting models can be integrated into metabolic cybergenetic schemes involving single-level optimizations. Additionally, the hybrid modeling approach maintains the number of system states at a necessary minimum, easing the burden of process monitoring and estimation. Our hybrid physics-informed metabolic cybergenetic framework is demonstrated using a computational case study on the optogenetically assisted production of itaconate by Escherichia coli.

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Section: Preliminary Accuracy Check Of the Constraint-based Modelmentioning

confidence: 99%

Hybrid Physics-Informed Metabolic Cybergenetics: Process Rates Augmented with Machine-Learning Surrogates Informed by Flux Balance Analysis

Espinel-Ríos,

Avalos

2024

Ind. Eng. Chem. Res.

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“…we covered only open-loop optimization in classic batch processes with homogeneous light penetration (Espinel-Ríos, Morabito, Pohlodek, et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Dynamic optimization allows finding the optimal dynamic operation conditions, for example, in del Rio‐Chanona et al (2019), Jabarivelisdeh et al (2018), Jabarivelisdeh and Waldherr (2016), Nimmegeers et al (2018), Ryu et al (2019). Feedback control schemes, especially predictive control approaches as in Jabarivelisdeh et al (2020), Jabarivelisdeh and Waldherr (2018), Morabito et al (2019, 2021, 2022), allow one to counteract unknown disturbances such as changes in feed conditions or nonmodeled dynamics, while maximizing the production efficiency and rendering a consistent process performance.…”

Section: Introductionmentioning

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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“…Dynamic optimization allows finding the optimal dynamic operation conditions, e.g., in del Rio-Chanona et al (2019); ; Jabarivelisdeh and Waldherr (2016); Nimmegeers et al (2018); Ryu et al (2019)). Feedback control schemes, especially predictive control approaches as in Jabarivelisdeh et al (2020); ; Morabito et al (2019Morabito et al ( 2022Morabito et al ( 2021, allow one to counteract unknown disturbances such as changes in feed conditions or non-modeled dynamics, while maximizing the production efficiency and rendering a consistent process performance.…”

Section: Introductionmentioning

confidence: 99%

“…We consider a fed-batch regime with non-homogeneous light penetration, applying both open-loop and feedback control. Note that in a previous study we covered only open-loop optimization in classic batch processes with homogeneous light penetration (Espinel-Ríos, Morabito, Pohlodek, et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Towards a modeling, optimization and predictive control framework for fed-batch metabolic cybergenetics

Espinel-Ríos¹,

Bruno²,

Pohlodek³

et al. 2023

Preprint

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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, etc. 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.

show abstract

Efficient and Simple Gaussian Process Supported Stochastic Model Predictive Control for Bioreactors using HILO-MPC

Cited by 7 publications

References 17 publications

Hybrid Physics-Informed Metabolic Cybergenetics: Process Rates Augmented with Machine-Learning Surrogates Informed by Flux Balance Analysis

Hybrid Physics-Informed Metabolic Cybergenetics: Process Rates Augmented with Machine-Learning Surrogates Informed by Flux Balance Analysis

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

Towards a modeling, optimization and predictive control framework for fed-batch metabolic cybergenetics

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