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

A deep neural network-mediated optimization of bacterial medium for producing green fluorescence protein as a model of heterogeneous protein by an engineeredEscherichia 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. AbstractIn 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 engineeredEscherichia 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, respective...
Source: MicrobiologyOpen - Category: Microbiology Authors: Tags: ORIGINAL ARTICLE Source Type: research