A machine learning approach for efficient selection of enzyme concentrations and its application for flux optimization

Anamya Ajjolli Nagaraja, Philippe Charton, Xavier F. Cadet, Nicolas Fontaine, Mathieu Delsaut, Birgit Wiltschi, Alena Voit, Bernard Offmann, Cedric Damour, Brigitte Grondin-Perez, Frederic Cadet*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number291
Issue number3
Publication statusPublished - 4 Mar 2020


  • Artificial neural network
  • Cell-free systems
  • Flux optimization
  • Glycolysis
  • Machine learning
  • Metabolic pathways optimization
  • Synthetic biology

ASJC Scopus subject areas

  • Catalysis
  • Physical and Theoretical Chemistry

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