What question did this study set out to answer?

This research aims to enhance β-carotene production in Saccharomyces cerevisiae through machine learning and transcriptomic analysis.

August 13, 2026ACS OmegaOpen Access

Integrating MachineLearning and Transcriptomics toEnhance β-Carotene Production in Saccharomyces cerevisiae

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Authors

PKPeerapat Khamwachirapithak KSKittapong Sae-Tang SBSuriyaporn Bubphasawan

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Overview

Randomized trial integrates machine learning and transcriptomics to improve β-carotene production in yeast, suggesting a novel approach for biotechnological applications.

Key Points

This research aims to enhance β-carotene production in Saccharomyces cerevisiae through machine learning and transcriptomic analysis.
Machine learning-guided combinatorial strain engineering to fine-tune MVA pathway enzymes.
Application of XGBoost in the Design-Build-Test-Learn cycle for rapid optimization.
Transcriptomic insights used to guide iron supplementation in fermentation cultures.
Fine-tuning MVA gene expression improved β-carotene titer by 139%.
Supplementation with iron resulted in a 70.54% increase in β-carotene titer.
Maximum β-carotene yield reached 72.07 mg/L at 72 hours, a 67.79% increase compared to non-optimized strains.

Cite This Study

Khamwachirapithak et al. (2026) studied this question.

synapsesocial.com/papers/6a7d76bd2b0e0cff3f64050c https://doi.org/10.1021/acsomega.6c06012

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August 13, 2026ACS OmegaOpen Access

Integrating MachineLearning and Transcriptomics toEnhance β-Carotene Production in Saccharomyces cerevisiae

View Full Paper

Ask AI

Bookmark

Authors

PKPeerapat Khamwachirapithak KSKittapong Sae-Tang SBSuriyaporn Bubphasawan

Discussion

Member takes

Overview

Randomized trial integrates machine learning and transcriptomics to improve β-carotene production in yeast, suggesting a novel approach for biotechnological applications.

Key Points

This research aims to enhance β-carotene production in Saccharomyces cerevisiae through machine learning and transcriptomic analysis.
Machine learning-guided combinatorial strain engineering to fine-tune MVA pathway enzymes.
Application of XGBoost in the Design-Build-Test-Learn cycle for rapid optimization.
Transcriptomic insights used to guide iron supplementation in fermentation cultures.
Fine-tuning MVA gene expression improved β-carotene titer by 139%.
Supplementation with iron resulted in a 70.54% increase in β-carotene titer.
Maximum β-carotene yield reached 72.07 mg/L at 72 hours, a 67.79% increase compared to non-optimized strains.

Cite This Study

Khamwachirapithak et al. (2026) studied this question.

synapsesocial.com/papers/6a7d76bd2b0e0cff3f64050c https://doi.org/10.1021/acsomega.6c06012

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