Modeling the effects of media formulated with various yeast extracts on heterologous protein production by Escherichia coli using machine learning

In microbial manufacturing, yeast extract is an important component of growth media. The production of heterologous proteins is often varied because of yeast extract composition. To identify why this reduces protein production, the effects of yeast extract compositions on the growth and green fluorescent protein (GFP) production of engineered Escherichia coli were investigated using a deep neural network (DNN)-mediated metabolomics approach. We observed 205 peaks from various yeast extracts using gas chromatography-mass spectrometry. Principal component analyses of the peaks identified at least three different clusters. Using 20 different compositions of yeast extract in M9 media, the yields of cells and GFP in the yeast extract-containing media were higher than those in the control without yeast extract by approximately 3.0–5.0 fold and 1.5–2.0 fold, respectively. We compared machine learning models and found that DNN best fit the data. To estimate the importance of each variable, we performed DNN with a mean increase error calculation based on a permutation algorithm. This method identified the significant components of yeast extract. DNN learning with varying numbers of input variables provided numbers of the significant components. The influence of specific components on cell growth and GFP production was confirmed with a validation cultivation.