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Article

A Machine Learning Approach for Efficient Selection of Enzyme Concentrations and Its Application for Flux Optimization

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Laboratory of Energy, Electronics and Processes (LE2P-EnergyLab), EA 4079, Faculty of Sciences and Technology, University of La Reunion, 97444 St Denis CEDEX, France
2
University of Paris, BIGR—Biologie Intégrée du Globule Rouge, Inserm, UMR_S1134, F-75015 Paris, France
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Laboratory of Excellence GR-Ex, Boulevard du Montparnasse, F-75015 Paris, France
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DSIMB—Dynamics of Structures and Interactions of Biological Macromolecules, UMR_S1134, BIGR, Inserm, Faculty of Sciences and Technology, University of La Reunion, F-97715 Saint-Denis, France
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PEACCEL—Protein Engineering Accelerator, 6 Square Albin Cachot, box 42, 75013 Paris, France
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ACIB—Austrian Centre of Industrial Biotechnology, Synthetic Biology Group, Petersgasse 14, 8010 Graz, Austria
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Unité Fonctionnalité et Ingénierie des Protéines (UFIP), UFR Sciences et Techniques, Université de Nantes, UMR 6286 CNRS, 2, Chemin de la Houssinière, 03 44322 Nantes CEDEX, France
*
Author to whom correspondence should be addressed.
Catalysts 2020, 10(3), 291; https://doi.org/10.3390/catal10030291
Received: 28 January 2020 / Revised: 25 February 2020 / Accepted: 28 February 2020 / Published: 4 March 2020
(This article belongs to the Special Issue Novel Enzyme and Whole-Cell Biocatalysts)
The metabolic engineering of pathways has been used extensively to produce molecules of interest on an industrial scale. Methods like gene regulation or substrate channeling helped to improve the desired product yield. Cell-free systems are used to overcome the weaknesses of engineered strains. One of the challenges in a cell-free system is selecting the optimized enzyme concentration for optimal yield. Here, a machine learning approach is used to select the enzyme concentration for the upper part of glycolysis. The artificial neural network approach (ANN) is known to be inefficient in extrapolating predictions outside the box: high predicted values will bump into a sort of “glass ceiling”. In order to explore this “glass ceiling” space, we developed a new methodology named glass ceiling ANN (GC-ANN). Principal component analysis (PCA) and data classification methods are used to derive a rule for a high flux, and ANN to predict the flux through the pathway using the input data of 121 balances of four enzymes in the upper part of glycolysis. The outcomes of this study are i. in silico selection of optimum enzyme concentrations for a maximum flux through the pathway and ii. experimental in vitro validation of the “out-of-the-box” fluxes predicted using this new approach. Surprisingly, flux improvements of up to 63% were obtained. Gratifyingly, these improvements are coupled with a cost decrease of up to 25% for the assay. View Full-Text
Keywords: machine learning; flux optimization; artificial neural network; synthetic biology; glycolysis; metabolic pathways optimization; cell-free systems machine learning; flux optimization; artificial neural network; synthetic biology; glycolysis; metabolic pathways optimization; cell-free systems
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MDPI and ACS Style

Ajjolli Nagaraja, A.; Charton, P.; Cadet, X.F.; Fontaine, N.; Delsaut, M.; Wiltschi, B.; Voit, A.; Offmann, B.; Damour, C.; Grondin-Perez, B.; Cadet, F. A Machine Learning Approach for Efficient Selection of Enzyme Concentrations and Its Application for Flux Optimization. Catalysts 2020, 10, 291. https://doi.org/10.3390/catal10030291

AMA Style

Ajjolli Nagaraja A, Charton P, Cadet XF, Fontaine N, Delsaut M, Wiltschi B, Voit A, Offmann B, Damour C, Grondin-Perez B, Cadet F. A Machine Learning Approach for Efficient Selection of Enzyme Concentrations and Its Application for Flux Optimization. Catalysts. 2020; 10(3):291. https://doi.org/10.3390/catal10030291

Chicago/Turabian Style

Ajjolli Nagaraja, Anamya, Philippe Charton, Xavier F. Cadet, Nicolas Fontaine, Mathieu Delsaut, Birgit Wiltschi, Alena Voit, Bernard Offmann, Cedric Damour, Brigitte Grondin-Perez, and Frederic Cadet. 2020. "A Machine Learning Approach for Efficient Selection of Enzyme Concentrations and Its Application for Flux Optimization" Catalysts 10, no. 3: 291. https://doi.org/10.3390/catal10030291

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