Temperature-Dependent Kinetic Modeling of Nitrogen-Limited Batch Fermentation by Yeast Species

Artai R. Moimenta, Romain Minebois, David Henriques, Amparo Querol and Eva Balsa-Canto ()
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Artai R. Moimenta: Biosystems and Bioprocess Engineering, Bio2Eng, IIM-CSIC Spanish National Research Council, 36208 Vigo, Spain
Romain Minebois: Yeastomics Laboratory, Food Biotechnology Department, IATA-CSIC Spanish National Research Council, 46980 Valencia, Spain
David Henriques: AquaBioTech, IIM-CSIC Spanish National Research Council, 36208 Vigo, Spain
Amparo Querol: Yeastomics Laboratory, Food Biotechnology Department, IATA-CSIC Spanish National Research Council, 46980 Valencia, Spain
Eva Balsa-Canto: Biosystems and Bioprocess Engineering, Bio2Eng, IIM-CSIC Spanish National Research Council, 36208 Vigo, Spain

Mathematics, 2025, vol. 13, issue 9, 1-20

Abstract: Yeast batch fermentation is widely used in industrial biotechnology, yet its performance is strongly influenced by temperature and nitrogen availability, which affect growth kinetics and metabolite production. The development of predictive models that accurately describe these effects is essential for automating and optimizing fermentation design, reducing trial-and-error experimentation, and improving process efficiency and product quality. However, most mathematical models focus on primary metabolism and lack a systematic approach to integrate the effects of temperature. Existing models often rely on empirical corrections with limited predictive power beyond specific experimental conditions. Furthermore, there is no unified framework for optimizing fermentation processes while accounting for the temperature-dependent metabolic responses. We addressed these gaps by developing a temperature-dependent kinetic model for nitrogen-limited batch fermentation by Saccharomyces cerevisiae . The modeling approach is based on advanced systems identification, integrating identifiability analyses (structural and practical), multi-experiment parameter estimation, and automated model selection to determine the most appropriate temperature dependencies for key metabolic processes. Validated across five industrial S. cerevisiae strains in an illustrative example related to wine fermentation, the model exhibited strong predictive performance (NRMSE < 10.5 % , median R 2 > 0.95 ) and enabled simulation-based process optimization, including nitrogen-supplementation strategies and strain selection for improved fermentation outcomes. By providing a systematic modeling framework that accounts for temperature effects, this work bridges a critical gap in predictive modeling and advances the rational design and control of industrial fermentation processes.

Keywords: system identification in fermentation; predictive modeling of yeast metabolism; temperature control in bioprocesses; optimization of nitrogen-limited fermentations; Saccharomyces cerevisiae metabolism; secondary metabolism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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