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Machine learning reveals novel compound for the improved production of chitooligosaccharides in Escherichia coli

Friederike Mey, Gaetan De Waele (UGent) , Wouter Demeester (UGent) , Chiara Guidi (UGent) , Dries Duchi (UGent) , Tomek Diederen, Hanne Kochuyt, Kirsten Van Huffel, Nicola Zamboni, Willem Waegeman (UGent) , et al.
(2025) NEW BIOTECHNOLOGY. 86. p.39-46
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Abstract
In order to improve predictability of outcome and reduce costly rounds of trial-and-error, machine learning models have been of increasing importance in the field of synthetic biology. Besides applications in predicting genome annotation, process parameters and transcription initiation frequency, such models have also been of help for pathway optimization. The latter is a common strategy in metabolic engineering and improves the production of a desirable compound by optimizing enzyme expression levels of the production pathway. However, engineering steps might not lead to sufficient improvement, and bottlenecks may remain hidden among the hundreds of metabolic reactions occurring in a living cell, especially if the production pathway is highly interconnected with other parts of the cell's metabolism. Here, we use the synthesis of chitooligosaccharides (COS) to show that the production from such complex pathways can be improved by using machine learning models and feature importance analysis to find new compounds with an impact on COS production. We screened Escherichia coli libraries of engineered transcription regulators with an expected broad range of metabolic diversity and trained several machine learning models to predict COS production titers. Subsequent feature analysis led to the finding of iron, whose addition we could show improved COS production in vivo up to two-fold. Additionally, the analysis revealed important clues for future engineering steps.
Keywords
Synthetic biology, Metabolic engineering, Global regulator engineering, Machine learning, Metabolomics, GENE-EXPRESSION, GLOBAL REGULATOR, PATHWAY

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MLA
Mey, Friederike, et al. “Machine Learning Reveals Novel Compound for the Improved Production of Chitooligosaccharides in Escherichia Coli.” NEW BIOTECHNOLOGY, vol. 86, 2025, pp. 39–46, doi:10.1016/j.nbt.2025.01.005.
APA
Mey, F., De Waele, G., Demeester, W., Guidi, C., Duchi, D., Diederen, T., … De Mey, M. (2025). Machine learning reveals novel compound for the improved production of chitooligosaccharides in Escherichia coli. NEW BIOTECHNOLOGY, 86, 39–46. https://doi.org/10.1016/j.nbt.2025.01.005
Chicago author-date
Mey, Friederike, Gaetan De Waele, Wouter Demeester, Chiara Guidi, Dries Duchi, Tomek Diederen, Hanne Kochuyt, et al. 2025. “Machine Learning Reveals Novel Compound for the Improved Production of Chitooligosaccharides in Escherichia Coli.” NEW BIOTECHNOLOGY 86: 39–46. https://doi.org/10.1016/j.nbt.2025.01.005.
Chicago author-date (all authors)
Mey, Friederike, Gaetan De Waele, Wouter Demeester, Chiara Guidi, Dries Duchi, Tomek Diederen, Hanne Kochuyt, Kirsten Van Huffel, Nicola Zamboni, Willem Waegeman, and Marjan De Mey. 2025. “Machine Learning Reveals Novel Compound for the Improved Production of Chitooligosaccharides in Escherichia Coli.” NEW BIOTECHNOLOGY 86: 39–46. doi:10.1016/j.nbt.2025.01.005.
Vancouver
1.
Mey F, De Waele G, Demeester W, Guidi C, Duchi D, Diederen T, et al. Machine learning reveals novel compound for the improved production of chitooligosaccharides in Escherichia coli. NEW BIOTECHNOLOGY. 2025;86:39–46.
IEEE
[1]
F. Mey et al., “Machine learning reveals novel compound for the improved production of chitooligosaccharides in Escherichia coli,” NEW BIOTECHNOLOGY, vol. 86, pp. 39–46, 2025.
@article{01JJYF5MY0N2X174CRX34AQ331,
  abstract     = {{In order to improve predictability of outcome and reduce costly rounds of trial-and-error, machine learning models have been of increasing importance in the field of synthetic biology. Besides applications in predicting genome annotation, process parameters and transcription initiation frequency, such models have also been of help for pathway optimization. The latter is a common strategy in metabolic engineering and improves the production of a desirable compound by optimizing enzyme expression levels of the production pathway. However, engineering steps might not lead to sufficient improvement, and bottlenecks may remain hidden among the hundreds of metabolic reactions occurring in a living cell, especially if the production pathway is highly interconnected with other parts of the cell's metabolism. Here, we use the synthesis of chitooligosaccharides (COS) to show that the production from such complex pathways can be improved by using machine learning models and feature importance analysis to find new compounds with an impact on COS production. We screened Escherichia coli libraries of engineered transcription regulators with an expected broad range of metabolic diversity and trained several machine learning models to predict COS production titers. Subsequent feature analysis led to the finding of iron, whose addition we could show improved COS production in vivo up to two-fold. Additionally, the analysis revealed important clues for future engineering steps.}},
  author       = {{Mey, Friederike and De Waele, Gaetan and Demeester, Wouter and Guidi, Chiara and Duchi, Dries and Diederen, Tomek and Kochuyt, Hanne and Van Huffel, Kirsten and Zamboni, Nicola and Waegeman, Willem and De Mey, Marjan}},
  issn         = {{1871-6784}},
  journal      = {{NEW BIOTECHNOLOGY}},
  keywords     = {{Synthetic biology,Metabolic engineering,Global regulator engineering,Machine learning,Metabolomics,GENE-EXPRESSION,GLOBAL REGULATOR,PATHWAY}},
  language     = {{eng}},
  pages        = {{39--46}},
  title        = {{Machine learning reveals novel compound for the improved production of chitooligosaccharides in Escherichia coli}},
  url          = {{http://doi.org/10.1016/j.nbt.2025.01.005}},
  volume       = {{86}},
  year         = {{2025}},
}
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