Optimization of bioprocess productivity based on metabolic‐genetic network models with bilevel dynamic programming

Jabarivelisdeh, Banafsheh; Waldherr, Steffen

doi:10.1002/bit.26599

Cited by 27 publications

(13 citation statements)

References 38 publications

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“…We focus on deFBA, a specific constraint‐based modeling framework 32,38,39 . It allows capturing changes in the biomass composition due to temporal metabolic adaptions and considers the “cost” of producing such biomass components.…”

Section: Constraint‐based Modelingmentioning

confidence: 99%

Maximizing batch fermentation efficiency by constrained model‐based optimization and predictive control of adenosine triphosphate turnover

et al. 2022

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We present a constrained model‐based optimization and predictive control framework to maximize the production efficiency of batch fermentations based on the core idea of manipulating adenosine triphosphate (ATP) wasting. In many bioprocesses, enforced ATP wasting —rerouting ATP use towards an energetically possibly suboptimal path— allows increasing the metabolic flux towards the product, thereby enhancing product yields and specific productivities. However, this often comes at the expense of lower biomass yields and reduced volumetric productivities. To maximize the overall efficiency, we formulate ATP wasting as a model‐based optimal control problem. This allows for balancing trade‐offs between different objectives such as product yield and volumetric productivity for batch fermentations. Unlike static metabolic control, one obtains a higher degree of flexibility, adaptability, and competitiveness. This can be advantageous towards achieving a sustainable and economically efficient biotechnology industry. To compensate for model‐plant mismatch, disturbances, and uncertainties, we propose not only solving the optimal control problem once. Instead, we exploit the concept of moving horizon model predictive control combined with constraint‐based dynamic modeling to capture the fermentation dynamics. The approach is underlined considering the industrially relevant bioproduction of lactate by Escherichia coli. We discuss practical challenges for the described control strategy and provide an outlook towards future developments.

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Section: Constraint‐based Modelingmentioning

confidence: 99%

Maximizing batch fermentation efficiency by constrained model‐based optimization and predictive control of adenosine triphosphate turnover

et al. 2022

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“…To regain the bacterial growth but maintain a high yield, dynamic regulation of metabolism in a two-phase process has been proposed [16]. To this end, in [7], we have employed the deFBA model within a bilevel optimization to obtain the optimal temporal manipulation of the cellular metabolism for improved bioprocess productivity. This is implemened by dynamic manipulation of one or several reaction fluxes such that an optimal balance between biomass growth and product formation is achieved.…”

Section: Control Problem Formulation For Maximal Productivitymentioning

confidence: 99%

“…MPC computes a trajectory of control inputs by solving an optimization problem at each sampling time [11]. In this work, we consider a closed-loop control of the bioprocess by introducing feedback to the open-loop problem (7), based on the MPC. The optimal trajectory of the control inputs is determined by repeating the bilevel optimization (7) time, a shrinking horizon of length t f − T , where T is the current time, is used for the time the optimization is performed over.…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…The network includes glycerol dissimilation pathways and reactions for glycolysis, pentose phosphate pathway, anaerobic fermentation, and respiration together with appropriate production reactions for biomass components including catalytic enzymes, ribosomes and structural macromolecules. The main steps for generating such a reduced metabolic-genetic network are similar to what was presented in our other works [7,12]. All reactions with corresponding enzymes and their catalytic constants are given in Tables 1 and 2, and for simplicity, gene IDs are used for the naming of enzymes.…”

Section: Network Descriptionmentioning

confidence: 99%

“…In our previous work [7], we have implemented the dynamic enzyme-cost FBA (deFBA) model [8] within a bilevel framework. The deFBA-based bilevel approach improves bioprocess productivity by finding optimal strategies for dynamic manipulation of cellular metabolism in both genetic and process levels.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive predictive control of bioprocesses with constraint-based modeling and estimation

Jabarivelisdeh

Carius

Findeisen

et al. 2020

Computers & Chemical Engineering

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Control of biotechnological processes is currently recipe-based with insufficient ability to handle possible uncertainties, which results in suboptimal production processes. To address this problem, model-based optimization and control approaches can be implemented to derive optimal control strategies. However, for reliable performance of model-based control, it is crucial to use flexible and adaptive control strategies which address biological variability while compensating for uncertainties. In this work, we present an approach for adaptive control of a bioprocess based on dynamic flux balance models. A previously developed bilevel approach for bioprocess optimization is implemented inside a model predictive control (MPC) routine. To account for model uncertainties, a moving horizon estimation algorithm is combined with the MPC in order to estimate uncertain parameters of the underlying model online for different metabolic modes. We apply this method to maximize the productivity of a target metabolite under microaerobic conditions by adapting the degree of oxygen-limitation online.

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Simultaneous design of fermentation and microbe

Ziegler,

Manchanda,

Stumm

et al. 2024

AIChE Journal

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Constraint‐based optimization of microbial strains and model‐based bioprocess design have been used extensively to enhance yields in biotechnological processes. However, strain and process optimization are usually carried out in sequential steps, causing underperformance of the biotechnological process when scaling up to industrial fermentation conditions. Herein, we propose the optimization formulation SimulKnock that combines the optimization of a fermentation process with metabolic network design in a bilevel optimization program. The upper level maximizes space‐time yield and includes mass balances of a continuous fermentation, while the lower level is based on flux balance analysis. SimulKnock predicts optimal gene deletions and finds the optimal trade‐off between growth rate and product yield. Results of a case study with a genome‐scale metabolic model of Escherichia coli indicate higher space‐time yields than a sequential approach using OptKnock for almost all target products considered. By leveraging SimulKnock, we reduce the gap between strain and process optimization.

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Optimization of bioprocess productivity based on metabolic‐genetic network models with bilevel dynamic programming

Cited by 27 publications

References 38 publications

Maximizing batch fermentation efficiency by constrained model‐based optimization and predictive control of adenosine triphosphate turnover

Maximizing batch fermentation efficiency by constrained model‐based optimization and predictive control of adenosine triphosphate turnover

Adaptive predictive control of bioprocesses with constraint-based modeling and estimation

Simultaneous design of fermentation and microbe

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