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Optimizing Ethanol Production in Saccharomyces cerevisiae at Ambient and Elevated Temperatures through Machine Learning-Guided Combinatorial Promoter Modifications
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Document Title
Optimizing Ethanol Production in Saccharomyces cerevisiae at Ambient and Elevated Temperatures through Machine Learning-Guided Combinatorial Promoter Modifications
Author
Khamwachirapithak P. Sae-Tang K. Mhuantong W. Tanapongpipat S. Zhao X.-Q. Liu C.-G. Wei D.-Q. Champreda V. Runguphan W.
Affiliations
National Center for Genetic Engineering and Biotechnology (BIOTEC) National Science and Technology Development Agency (NSTDA) 111 Thailand Science Park Phahonyothin Road Pathum Thani Khlong Luang 12120 Thailand; State Key Laboratory of Microbial Metabolism Joint International Research Laboratory of Metabolic & Developmental Sciences School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai 200240 China; Department of Bioinformatics and Biological Statistics School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai 200240 China
Type
Article
Source Title
ACS Synthetic Biology
ISSN
21615063
Year
2023
Volume
12
Issue
10
Page
2897-2908
Open Access
All Open Access Hybrid Gold Green
Publisher
American Chemical Society
DOI
10.1021/acssynbio.3c00199
Abstract
Bioethanol has gained popularity in recent decades as an ecofriendly alternative to fossil fuels due to increasing concerns about global climate change. However economically viable ethanol fermentation remains a challenge. High-temperature fermentation can reduce production costs but Saccharomyces cerevisiae yeast strains normally ferment poorly under high temperatures. In this study we present a machine learning (ML) approach to optimize bioethanol production in S. cerevisiae by fine-tuning the promoter activities of three endogenous genes. We created 216 combinatorial strains of S. cerevisiae by replacing native promoters with five promoters of varying strengths to regulate ethanol production. Promoter replacement resulted in a 63% improvement in ethanol production at 30 ?C. We created an ML-guided workflow by utilizing XGBoost to train high-performance models based on promoter strengths and cellular metabolite concentrations obtained from ethanol production of 216 combinatorial strains at 30 ?C. This strategy was then applied to optimize ethanol production at 40 ?C where we selected 31 strains for experimental fermentation. This reduced experimental load led to a 7.4% increase in ethanol production in the second round of the ML-guided workflow. Our study offers a comprehensive library of promoter strength modifications for key ethanol production enzymes showcasing how machine learning can guide yeast strain optimization and make bioethanol production more cost-effective and efficient. Furthermore we demonstrate that metabolic engineering processes can be accelerated and optimized through this approach. ? 2023 The Authors. Published by American Chemical Society.
Keyword
ensemble decision tree | Ethanol | machine learning | stress tolerance | Yeast
Industrial Classification
Knowledge Taxonomy Level 1
Knowledge Taxonomy Level 2
Knowledge Taxonomy Level 3
License
CC BY
Rights
Authors
Publication Source
Scopus