Abstract
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.
Highlights
Many chemical molecules like peptides, organic acids, etc., are synthesized by different methods such as chemical reactions [1,2,3,4,5] and fermentation process for their application in everyday life.Due to the depletion of non-renewable resources, synthesis of these molecules through a biological system is essential on an industrial scale [6,7]
In order to explore this “glass ceiling” space, we developed a new methodology (GC-ANN, for glass ceiling ANN) to predict the flux for the upper part of glycolysis, given enzyme concentrations using an artificial neural network
The total four-enzyme concentration was constant in the system, which reduced the degree of freedom to limit the enzyme concentrations to three
Summary
Many chemical molecules like peptides, organic acids, etc., are synthesized by different methods such as chemical reactions [1,2,3,4,5] and fermentation process for their application in everyday life.Due to the depletion of non-renewable resources, synthesis of these molecules through a biological system is essential on an industrial scale [6,7]. Many chemical molecules like peptides, organic acids, etc., are synthesized by different methods such as chemical reactions [1,2,3,4,5] and fermentation process for their application in everyday life. Scientists have been successful in producing different chemical molecules through microbial fermentation by optimizing the process [7,8,9,10]. Microbial systems are scalable, use inexpensive synthetic media and have lower batch-to-batch variability [11]. Microbial systems such as Escherichia coli or yeasts have no or only limited capacity for post-translational modifications. Microbial biosynthesis may show low productivity and the coproduction of by-products is possible, which make product recovery complex and protracted [12]
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