Model-based Analysis and Optimisation of a Continuous Corynebacterium glutamicum Bioprocess Utilizing Lignocellulosic Waste

Sinner, Peter; Kager, Julian; Daume, Sven; Herwig, Christoph

doi:10.1016/j.ifacol.2019.12.255

Cited by 11 publications

(2 citation statements)

References 21 publications

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“…Based on the recent work of Prell et al ( 2021 ) plasmid copy numbers can be estimated to be in the approximate range of 100–130 for pEKEx3 and 10–20 for pVWEx1. From both a physiological (Sasaki et al, 2011 ) and process technological perspective (Sinner et al, 2019 ), mannose consumption can be regarded as the main bottleneck, causing sugar loss to the effluent and limiting biomass space time yield of the analyzed continuous process utilizing the mannose‐rich by‐product stream SSL. This is also reflected by the high sensitivities regarding mannose growth capacity on the measured process outputs.…”

Section: Resultsmentioning

confidence: 99%

Online estimation of changing metabolic capacities in continuous Corynebacterium glutamicum cultivations growing on a complex sugar mixture

Sinner

Stiegler

Goldbeck

et al. 2021

Biotech & Bioengineering

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Model-based state estimators enable online monitoring of bioprocesses and, thereby, quantitative process understanding during running operations. During prolonged continuous bioprocesses strain physiology is affected by selection pressure. This can cause time-variable metabolic capacities that lead to a considerable model-plant mismatch reducing monitoring performance if model parameters are not adapted accordingly. Variability of metabolic capacities therefore needs to be integrated in the in silico representation of a process using model-based monitoring approaches. To enable online monitoring of multiple concentrations as well as metabolic capacities during continuous bioprocessing of spent sulfite liquor with Corynebacterium glutamicum, this study presents a particle filtering framework that takes account of parametric variability. Physiological parameters are continuously adapted by Bayesian inference, using noninvasive off-gas measurements. Additional information on current parameter importance is derived from time-resolved sensitivity analysis. Experimental results show that the presented framework enables accurate online monitoring of long-term culture dynamics, whereas state estimation without parameter adaption failed to quantify substrate metabolization and growth capacities under conditions of high selection pressure. Online estimated metabolic capacities are further deployed for multiobjective optimization to identify timevariable optimal operating points. Thereby, the presented monitoring system forms a basis for adaptive control during continuous bioprocessing of lignocellulosic byproduct streams.

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Section: Resultsmentioning

confidence: 99%

Online estimation of changing metabolic capacities in continuous Corynebacterium glutamicum cultivations growing on a complex sugar mixture

Sinner

Stiegler

Goldbeck

et al. 2021

Biotech & Bioengineering

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“…For optimization purposes, modeling has proven to be a powerful tool, enabling the exploration of a wider range of operating conditions while minimizing cost, compared with the experimental approach [24][25][26][27][28][29]. To our knowledge, the only dynamic model dealing with C. maltaromaticum strains has been published by Ellouze et al [6].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling of Carnobacterium maltaromaticum CNCM I-3298 Growth and Metabolite Production and Model-Based Process Optimization

Puentes

Girardeau

Passot

et al. 2021

Foods

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Carnobacterium maltaromaticum is a species of lactic acid bacteria found in dairy, meat, and fish, with technological properties useful in food biopreservation and flavor development. In more recent years, it has also proven to be a key element of biological time–temperature integrators for tracking temperature variations experienced by perishable foods along the cold-chain. A dynamic model for the growth of C. maltaromaticum CNCM I-3298 and production of four metabolites (formic acid, acetic acid, lactic acid, and ethanol) from trehalose in batch culture was developed using the reaction scheme formalism. The dependence of the specific growth and production rates as well as the product inhibition parameters on the operating conditions were described by the response surface method. The parameters of the model were calibrated from eight experiments, covering a broad spectrum of culture conditions (temperatures between 20 and 37 °C; pH between 6.0 and 9.5). The model was validated against another set of eight independent experiments performed under different conditions selected in the same range. The model correctly predicted the growth kinetics of C. maltaromaticum CNCM I-3298 as well as the dynamics of the carbon source conversion, with a mean relative error of 10% for biomass and 14% for trehalose and the metabolites. The paper illustrates that the proposed model is a valuable tool for optimizing the culture of C. maltaromaticum CNCM I-3298 by determining operating conditions that favor the production of biomass or selected metabolites. Model-based optimization may thus reduce the number of experiments and substantially speed up the process development, with potential applications in food technology for producing starters and improving the yield and productivity of the fermentation of sugars into metabolites of industrial interest.

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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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Model-based Analysis and Optimisation of a Continuous Corynebacterium glutamicum Bioprocess Utilizing Lignocellulosic Waste

Cited by 11 publications

References 21 publications

Online estimation of changing metabolic capacities in continuous Corynebacterium glutamicum cultivations growing on a complex sugar mixture

Online estimation of changing metabolic capacities in continuous Corynebacterium glutamicum cultivations growing on a complex sugar mixture

Dynamic Modeling of Carnobacterium maltaromaticum CNCM I-3298 Growth and Metabolite Production and Model-Based Process Optimization

Simultaneous design of fermentation and microbe

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