Optimizing Ethanol Production in Saccharomyces cerevisiae at Ambient and Elevated Temperatures through Machine Learning-Guided Combinatorial Promoter Modifications

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.