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A model for diauxic growth.

G Van Dedem, M Moo-Young

    Biotechnology and Bioengineering
    |September 1, 1975
    PubMed
    Summary
    This summary is machine-generated.

    A new model predicts fermentation behavior using two carbon sources. It explains diauxic growth, where microbes switch to a preferred energy source when the first is depleted.

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    Area of Science:

    • Biochemical Engineering
    • Microbial Physiology
    • Mathematical Modeling

    Background:

    • Fermentations often utilize multiple carbon sources, leading to complex growth patterns.
    • Understanding microbial substrate utilization is crucial for optimizing bioprocesses.
    • Diauxic growth, characterized by sequential substrate consumption, is a well-documented phenomenon.

    Purpose of the Study:

    • To develop a predictive model for fermentation dynamics on dual carbon and energy sources.
    • To simulate both batch and continuous culture behaviors.
    • To elucidate the mechanisms underlying diauxic growth in microbial systems.

    Main Methods:

    • A mathematical model was formulated based on enzyme kinetics.
    • Key assumptions include constitutive permease for the preferred substrate and inducible/repressible permease for the second.

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  • Model simulations were performed for batch and continuous fermentation scenarios.

    • Main Results:

    • The model successfully predicts microbial growth patterns on two distinct carbon sources.
    • Simulations demonstrated characteristic diauxic growth curves, aligning with experimental observations.
    • The model highlights the roles of permease regulation (induction and catabolite repression) in substrate switching.

    Conclusions:

    • The developed model provides a robust framework for understanding and predicting fermentation behavior with multiple substrates.
    • The model's assumptions accurately capture the essential regulatory mechanisms driving diauxic growth.
    • This predictive capability can aid in the optimization of industrial fermentation processes.