Heterologous Lignan Production in Stirred-Tank Reactors—Metabolomics-Assisted Bioprocess Development for an In Vivo Enzyme Cascade

Steinmann, Andrea; Finger, Maurice; Nowacki, Christian; Decembrino, Davide; Hubmann, Georg; Girhard, Marco; Urlacher, Vlada B.; Lütz, Stephan

doi:10.3390/catal12111473

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…The design of the reactor, including factors such as its diameter, shape, capacity, and wall material, also affects the course of the process. Some reactors, known as heterogeneous reactors, simultaneously handle multiple states of matter, adding another layer of complexity to their monitoring and efficiency optimization [8].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Neural Network Loss Function in Electrical Tomography to Increase Energy Efficiency in Industrial Reactors

Kulisz,

Kłosowski,

Rymarczyk

et al. 2024

Energies

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This paper presents innovative machine-learning solutions to enhance energy efficiency in electrical tomography for industrial reactors. Addressing the key challenge of optimizing the neural model’s loss function, a classifier tailored to precisely recommend optimal loss functions based on the measurement data is designed. This classifier recommends which model, equipped with given loss functions, should be used to ensure the best reconstruction quality. The novelty of this study lies in the optimal adjustment of the loss function to a specific measurement vector, which allows for better reconstructions than that by traditional models trained based on a constant loss function. This study presents a methodology enabling the development of an optimal loss function classifier to determine the optimal model and loss function for specific datasets. The approach eliminates the randomness inherent in traditional methods, leading to more accurate and reliable reconstructions. In order to achieve the set goal, four models based on a simple LSTM network structure were first trained, each connected with various loss functions: HMSE (half mean squared error), Huber, l1loss (L1 loss for regression tasks—mean absolute error), and l2loss (L2 loss for regression tasks—mean squared error). The best classifier training results were obtained for support vector machines. The quality of the obtained reconstructions was evaluated using three image quality indicators: PSNR, ICC, and MSE. When applied to simulated cases and real measurements from the Netrix S.A. laboratory, the classifier demonstrated effective performance, consistently recommending models that produced reconstructions that closely resembled the real objects. Such a classifier can significantly optimize the use of EIT in industrial reactors by increasing the accuracy and efficiency of imaging, resulting in improved energy management and efficiency.

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Section: Introductionmentioning

confidence: 99%

Optimizing the Neural Network Loss Function in Electrical Tomography to Increase Energy Efficiency in Industrial Reactors

Kulisz,

Kłosowski,

Rymarczyk

et al. 2024

Energies

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Plasmid-free production of the plant lignan pinoresinol in growing Escherichia coli cells

Luelf,

Wassing,

Böhmer

et al. 2024

Microb Cell Fact

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Background The high-value aryl tetralin lignan (+)-pinoresinol is the main precursor of many plant lignans including (-)-podophyllotoxin, which is used for the synthesis of chemotherapeutics. As (-)-podophyllotoxin is traditionally isolated from endangered and therefore limited natural sources, there is a particular need for biotechnological production. Recently, we developed a reconstituted biosynthetic pathway from (+)-pinoresinol to (-)-deoxypodophyllotoxin, the direct precursor of (-)-podophyllotoxin, in the recombinant host Escherichia coli . However, the use of the expensive substrate (+)-pinoresinol limits its application from the economic viewpoint. In addition, the simultaneous expression of multiple heterologous genes from different plasmids for a multi-enzyme cascade can be challenging and limits large-scale use. Results In this study, recombinant plasmid-free E. coli strains for the multi-step synthesis of pinoresinol from ferulic acid were constructed. To this end, a simple and versatile plasmid toolbox for CRISPR/Cas9-assisted chromosomal integration has been developed, which allows the easy transfer of genes from the pET vector series into the E. coli chromosome. Two versions of the developed toolbox enable the efficient integration of either one or two genes into intergenic high expression loci in both E. coli K-12 and B strains. After evaluation of this toolbox using the fluorescent reporter mCherry, genes from Petroselinum crispum and Zea mays for the synthesis of the monolignol coniferyl alcohol were integrated into different E. coli strains. The product titers achieved with plasmid-free E. coli W3110(T7) were comparable to those of the plasmid-based expression system. For the subsequent oxidative coupling of coniferyl alcohol to pinoresinol, a laccase from Corynebacterium glutamicum was selected. Testing of different culture media as well as optimization of gene copy number and copper availability for laccase activity resulted in the synthesis of 100 mg/L pinoresinol using growing E. coli cells. Conclusions For efficient and simple transfer of genes from pET vectors into the E. coli chromosome, an easy-to-handle molecular toolbox was developed and successfully tested on several E. coli strains. By combining heterologous and endogenous enzymes of the host, a plasmid-free recombinant E. coli growing cell system has been established that enables the synthesis of the key lignan pinoresinol. Supplementary Information The online version contains supplementary material available at 10.1186/s12934-024-02562-3.

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Heterologous Lignan Production in Stirred-Tank Reactors—Metabolomics-Assisted Bioprocess Development for an In Vivo Enzyme Cascade

Cited by 2 publications

References 36 publications

Optimizing the Neural Network Loss Function in Electrical Tomography to Increase Energy Efficiency in Industrial Reactors

Optimizing the Neural Network Loss Function in Electrical Tomography to Increase Energy Efficiency in Industrial Reactors

Plasmid-free production of the plant lignan pinoresinol in growing Escherichia coli cells

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