The modi cation of gene expression is being researched in the production of useful chemicals by metabolic engineering of the yeast Saccharomyces cerevisiae. When the expression levels of many metabolic enzyme genes are modi ed simultaneously, the expression ratio of these genes becomes diverse; the relationship between the gene expression ratio and chemical productivity remains unclear. In other words, it is challenging to predict phenotypes from genotypes. However, the productivity of useful chemicals can be improved if this relationship is clari ed. In this study, we aimed to construct a machinelearning model that can be used to clarify the relationship between gene expression levels and D-lactic acid productivity and predict the optimal gene expression level for e cient D-lactic acid production in yeast. A machine-learning model was constructed using data on D-lactate dehydrogenase and glycolytic genes expression (13 dimensions) and D-lactic acid productivity. The coe cient of determination of the completed machine-learning model was 0.6932 when using the training data and 0.6628 when using the test data. Using the constructed machine-learning model, we predicted the optimal gene expression level for high D-lactic acid production. We successfully constructed a machine-learning model to predict both D-lactic acid productivity and the suitable gene expression ratio for the production of D-lactic acid. The technique established in this study could be key for predicting phenotypes from genotypes, a problem faced by recent metabolic engineering strategies.

The modification of gene expression is being researched in the production of useful chemicals by metabolic engineering of the yeast Saccharomyces cerevisiae. When the expression levels of many metabolic enzyme genes are modified simultaneously, the expression ratio of these genes becomes diverse; the relationship between the gene expression ratio and chemical productivity remains unclear. In other words, it is challenging to predict phenotypes from genotypes. However, the productivity of useful chemicals can be improved if this relationship is clarified. In this study, we aimed to construct a machine-learning model that can be used to clarify the relationship between gene expression levels and D-lactic acid productivity and predict the optimal gene expression level for efficient D-lactic acid production in yeast. A machine-learning model was constructed using data on D-lactate dehydrogenase and glycolytic genes expression (13 dimensions) and D-lactic acid productivity. The coefficient of determination of the completed machine-learning model was 0.6932 when using the training data and 0.6628 when using the test data. Using the constructed machine-learning model, we predicted the optimal gene expression level for high D-lactic acid production. We successfully constructed a machine-learning model to predict both D-lactic acid productivity and the suitable gene expression ratio for the production of D-lactic acid. The technique established in this study could be key for predicting phenotypes from genotypes, a problem faced by recent metabolic engineering strategies.