ArticleMicrobiologyOpen2021
Machine learning modeling of the effects of media formulated with various yeast extracts on heterologous protein production in Escherichia coli.
Article in MicrobiologyOpen, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 26 citations in OpenAlex.
- Redefining media blending mathematically: a systematic approach for screening of medium components.Applied microbiology and biotechnology · 2025Article
- Review
- Article
- Modelling ofBioengineering (Basel, Switzerland) · 2025Article
- Exploration of Yeast Species Suitable for Preparation of Stable Isotope-Labeled Internal Standards Extracts (SILIS).Mass spectrometry (Tokyo, Japan) · 2025Article
- Substitute Yeast Extract While Maintaining Performance: Showcase Amorpha-4,11-Diene Production.Microbial biotechnology · 2024Article
- Unlocking the genome of the non-sourdough Kazachstania humilis MAW1: insights into inhibitory factors and phenotypic properties.Microbial cell factories · 2024Article
- A review of algorithmic approaches for cell culture media optimization.Frontiers in bioengineering and biotechnology · 2023Review
- Machine learning modeling of the effects of media formulated with various yeast extracts on heterologous protein production in Escherichia coli.MicrobiologyOpen · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.