A new strategy for dynamic metabolic flux estimation by integrating transient metabolome data into genome-scale metabolic models

Liu, Peng; Ye, Hua; Zhang, Wei; Xie, Tingting; Zhuang, Yingping; Xia, Jian; Noorman, Henk

doi:10.1007/s00449-021-02626-3

Cited by 3 publications

(2 citation statements)

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“…In addition, uncertainty will be accounted for during the MPC routine. Furthermore, the presented metabolic network-based model predictive control procedure can also be combined with high-throughput biological datasets [26,27] and advanced metabolomics techniques as e.g., low-frequency NMR, to improve the metabolic model predictions and process monitoring capabilities [8,27]. One of the current limitations of metabolic network models and methods as metabolic flux analysis is the inherent pseudo-steady state assumption.…”

Section: Discussionmentioning

confidence: 99%

“…Results from literature, e.g., [3], indicate that including such metabolic network models in bioprocess optimization results in an improved process performance compared with the use of an unstructured macroscopic bioprocess model. In addition, the intracellular fluxes can be estimated rather accurately from extracellular measurements using a low-or medium-complexity metabolic network as in e.g., [4][5][6][7][8], enabling an enhanced bioprocess monitoring by monitoring the flux state, hence the metabolic state of the microorganisms in the biochemical process.…”

Section: Introductionmentioning

confidence: 99%

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Metabolic Reaction Network-Based Model Predictive Control of Bioprocesses

et al. 2021

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Bioprocesses are increasingly used for the production of high added value products. Microorganisms are used in bioprocesses to mediate or catalyze the necessary reactions. This makes bioprocesses highly nonlinear and the governing mechanisms are complex. These complex governing mechanisms can be modeled by a metabolic network that comprises all interactions within the cells of the microbial population present in the bioprocess. The current state of the art in bioprocess control is model predictive control based on the use of macroscopic models, solely accounting for substrate, biomass, and product mass balances. These macroscopic models do not account for the underlying mechanisms governing the observed process behavior. Consequently, opportunities are missed to fully exploit the available process knowledge to operate the process in a more sustainable manner. In this article, a procedure is presented for metabolic network-based model predictive control. This procedure uses a combined moving horizon-model predictive control strategy to monitor the flux state and optimize the bioprocess under study. A CSTR bioreactor model has been combined with a small-scale metabolic network to illustrate the performance of the presented procedure.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Metabolic Reaction Network-Based Model Predictive Control of Bioprocesses

et al. 2021

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An industrial perspective on metabolic responses of Penicillium chrysogenum to periodic dissolved oxygen feast‐famine cycles in a scale‐down system

Wang,

Yang,

Haringa

et al. 2024

Biotech & Bioengineering

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While traveling through different zones in large‐scale bioreactors, microbes are most likely subjected to fluctuating dissolved oxygen (DO) conditions at the timescales of global circulation time. In this study, to mimic industrial‐scale spatial DO gradients, we present a scale‐down setup based on dynamic feast/famine regime (150 s) that leads to repetitive cycles with rapid changes in DO availability in glucose‐limited chemostat cultures of Penicillium chrysogenum. Such DO feast/famine regime induced a stable and repetitive pattern with a reproducible metabolic response in time, and the dynamic response of intracellular metabolites featured specific differences in terms of both coverage and magnitude in comparison to other dynamic conditions, for example, substrate feast/famine cycles. Remarkably, intracellular sugar polyols were considerably increased as the hallmark metabolites along with a dynamic and higher redox state nicotinamide adenine dinucleotide hydrogen/nicotinamide adenine dinucleotide of the cytosol. Despite the increased availability of NADPH for penicillin production under the oscillatory DO conditions, this positive effect may be counteracted by the decreased adenosine triphosphate supply. Moreover, it is interesting to note that not only the penicillin productivity was reduced under such oscillating DO conditions, but also that of the unrecyclable byproduct ortho‐hydroxyphenyl acetic acid and degeneration of penicillin productivity. Furthermore, dynamic flux profiles showed the most pronounced variations in central carbon metabolism, amino acid (AA) metabolism, energy metabolism and fatty acid metabolism upon the DO oscillation. Taken together, the metabolic responses of P. chrysogenum to DO gradients reported here are important for elucidating metabolic regulation mechanisms, improving bioreactor design and scale‐up procedures as well as for constructing robust cell strains to cope with heterogenous industrial culture conditions.

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