Journal Article

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

Tachibana, Seiga, Chiou, Tai‐Ying, Konishi, Masaaki

2021

Overview

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. A deep neural network‐mediated optimization of bacterial medium for producing green fluorescence protein as a model of heterogeneous protein by an engineered Escherichia coli is demonstrated in this article. Using gas chromatography/mass spectrometry profiling for the medium components including various yeast extracts, a deep learning algorithm estimated the culture from the profiling with preferable accuracy, and permutation algorithm and sensitivity analysis with the trained model estimated significant components. Supplementation of the components led to improve growth and protein production.

• Publisher: John Wiley & Sons, Inc,John Wiley and Sons Inc,Wiley
• Subject: Algorithms / Amino acids / Artificial neural networks / Composition / cultivation / Culture Media - chemistry / Culture Media - metabolism / deep learning / E coli / Escherichia coli / Escherichia coli - genetics / Escherichia coli - growth & development / Escherichia coli - metabolism / Fluorescence / Gas chromatography / Gas Chromatography-Mass Spectrometry / Green fluorescent protein / Green Fluorescent Proteins - biosynthesis / Green Fluorescent Proteins - genetics / Growth media / Laboratories / Learning algorithms / Machine Learning / Mass spectrometry / Mass spectroscopy / Metabolomics / Microorganisms / Neural networks / Neural Networks, Computer / Original / Permutations / Principal components analysis / protein / Protein expression / Proteins / Recombinant Proteins - biosynthesis / Recombinant Proteins - genetics / Yeast / yeast extract