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<p><i>Saccharomyces cerevisiae</i> is indispensable to industrial fermentation; however, many existing models fail to adequately represent the metabolic complexity of its growth on mixed carbon sources in defined media. In this study, we introduce a novel hybrid modeling framework for the batch cultivation of <i>S. cerevisiae</i>, utilizing sucrose, glucose, and fructose as carbon sources, and urea as a nitrogen source. The model decisively captures critical phenomena under aerobic conditions, including the Crabtree effect, diauxic shifts, and sequential sugar utilization—critical areas frequently oversimplified in current models. By integrating mechanistic kinetics with data-driven enhancements, the hybrid model significantly improves predictive accuracy relative to the purely mechanistic baseline, reducing the average prediction error by a factor of 1.9 during training and 2.0 during testing. This framework enables detailed simulation of culture dynamics and was carefully designed for modular integration into digital twin platforms and automated control systems, aligning perfectly with Industry 4.0 biomanufacturing trends. Furthermore, the model’s validation under conditions pertinent to emerging bioeconomies, such as those in Latin America, underscores its industrial applicability. Overall, this work delivers a scalable and precise tool for optimizing yeast-based bioprocesses, carrying significant implications for defined media formulation, metabolic engineering, and digital fermentation technologies.</p>

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Advancing hybrid modeling of Saccharomyces cerevisiae fermentation with mixed carbon sources and urea in a mini-stirred tank reactor

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月23日

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  • Jhonatan Valencia-Velásquez,
  • Hector Andres Yaker-Moreno,
  • Alejandro Martínez-Guerrero,
  • Francisco Ibáñez-Espinel,
  • José Ricardo Pérez-Correa,
  • Nelson H. Caicedo-Ortega

摘要

Saccharomyces cerevisiae is indispensable to industrial fermentation; however, many existing models fail to adequately represent the metabolic complexity of its growth on mixed carbon sources in defined media. In this study, we introduce a novel hybrid modeling framework for the batch cultivation of S. cerevisiae, utilizing sucrose, glucose, and fructose as carbon sources, and urea as a nitrogen source. The model decisively captures critical phenomena under aerobic conditions, including the Crabtree effect, diauxic shifts, and sequential sugar utilization—critical areas frequently oversimplified in current models. By integrating mechanistic kinetics with data-driven enhancements, the hybrid model significantly improves predictive accuracy relative to the purely mechanistic baseline, reducing the average prediction error by a factor of 1.9 during training and 2.0 during testing. This framework enables detailed simulation of culture dynamics and was carefully designed for modular integration into digital twin platforms and automated control systems, aligning perfectly with Industry 4.0 biomanufacturing trends. Furthermore, the model’s validation under conditions pertinent to emerging bioeconomies, such as those in Latin America, underscores its industrial applicability. Overall, this work delivers a scalable and precise tool for optimizing yeast-based bioprocesses, carrying significant implications for defined media formulation, metabolic engineering, and digital fermentation technologies.