Machine learning modeling of the effects of media formulated with various yeast extracts on heterologous protein production in Escherichia coli

Abstract

In microbial manufacturing, yeast extract is an important component of the growth media. The production of heterologous proteins often varies because of the yeast extract composition. To identify why this reduces protein production, the effects of yeast extract composition 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 the 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‐ to 5.0‐fold and 1.5‐ to 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 the number of significant components. The influence of specific components on cell growth and GFP production was confirmed with a validation cultivation.

Unlocking the genome of the non-sourdough Kazachstania humilis MAW1: insights into inhibitory factors and phenotypic properties

“…Interestingly, crude filtrates obtained after culturing K. humilis MAW1 in a neutral (pH 7.0) medium were found to stimulate the growth of some bacteria [ 19 ]. This supports the notion that yeasts are a source of nutritive compounds such as vitamins, amino acids, organic acids, and other metabolites that support bacterial growth, which is the reason why yeast extract is frequently included in culture media [ 21 , 34 , 35 ].…”

The newly sequenced gnome of non-sourdough-derived Kazachstania humilis MAW1 in terms of identification of factors inhibiting bacterial growth

“…Interestingly, crude ltrates obtained after culturing K. humilis MAW1 in a neutral (pH 7.0) medium were found to stimulate the growth of some bacteria [19]. This supports the notion that yeasts are a source of nutritive compounds such as vitamins, amino acids, organic acids and other metabolites that support bacterial growth, which is the reason why yeast extract is frequently included in culture media [21,34,35].…”

A review of algorithmic approaches for cell culture media optimization

“…Deeper ANNs with many hidden layers, also known as deep learning models, are less common in media optimization. Two studies have used ANNs with 4 hidden layers (Tachibana et al, 2021;Yoshida et al, 2022) as surrogate models to predict the expression of GFP in E. coli. Having more layers increases the ability of the network to fit data, however it should be noted that such overparameterized models generally require a high number of training datapoints to prevent overfitting and poor generalization.…”