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Modelling microbial adaptation to changing availability of substrates.

Bernd W Brandt1, Fleur D L Kelpin, Ingeborg M M van Leeuwen

  • 1Department of Theoretical Biology, Faculty of Earth and Life Sciences, Vrije Universiteit, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands. bernd@bio.vu.nl

Water Research
|February 11, 2004
PubMed
Summary

Microorganisms adapt to changing nutrient conditions by synthesizing new enzymes. Our model shows this adaptation leads to a constant maximum substrate consumption rate, validating existing growth models.

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Area of Science:

  • Microbiology
  • Biochemical Engineering
  • Systems Biology

Background:

  • Microorganisms must adapt to fluctuating nutrient availability in their natural environments.
  • Changes in substrate concentration or type necessitate adaptive responses for microbial survival.

Purpose of the Study:

  • To present a mathematical model for slow microbial adaptation to changes in substitutable substrate availability.
  • To investigate the role of enzyme synthesis and reciprocal inhibition in microbial adaptation.

Main Methods:

  • Developed a model based on reciprocal inhibition of substrate-specific carriers and assimilatory machinery.
  • Derived inhibition kinetics from the kinetics of synthesizing units.
  • Applied the model to describe adaptation during diauxic growth.

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Main Results:

  • The model predicts a constant maximum specific substrate consumption rate for fully adapted microorganisms on a single limiting resource.
  • This property ensures the continued validity of established models like Monod and Pirt for single-substrate growth.
  • The simplest model version effectively describes diauxic growth adaptation with minimal parameters.

Conclusions:

  • The proposed model offers a novel framework for understanding microbial adaptation to nutrient shifts.
  • It highlights a key characteristic of adaptation: a substrate-independent maximum consumption rate.
  • The model's applicability is demonstrated through fitting to experimental diauxic growth data.