Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Exponential Growth01:29

Exponential Growth

91
Bacterial populations exhibit exponential growth when conditions such as nutrient availability and temperature are favorable. In this phase, cells reproduce through binary fission, where each cell divides into two identical daughter cells. This process causes the population to double at regular intervals, resulting in a growth rate that is directly proportional to the current number of cells. As the population increases, the number of new cells formed during each generation also grows, creating...
91
Exponential Equations for Modeling Growth02:33

Exponential Equations for Modeling Growth

280
Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
280
Microbial Growth Measurement: Indirect Methods01:27

Microbial Growth Measurement: Indirect Methods

1.7K
Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...
1.7K
Bacterial Growth Curve01:28

Bacterial Growth Curve

3.6K
The bacterial growth curve is a fundamental concept in microbiology that describes the dynamics of bacterial population growth in a closed system with controlled environmental conditions, such as temperature and nutrient availability. This curve is divided into four distinct phases: lag, log (exponential), stationary, and death phases, each reflecting a unique stage of bacterial adaptation and growth. During the lag phase, bacteria acclimate to their surroundings by synthesizing essential...
3.6K
Nonlinear Pharmacokinetics: Michaelis-Menten Equation01:18

Nonlinear Pharmacokinetics: Michaelis-Menten Equation

1.2K
The Michaelis–Menten equation is a fundamental model for describing capacity-limited kinetics in drug metabolism. It offers insights into the rate of decline of plasma drug concentration Cp over time, with Vmax and KM as pivotal parameters.
Vmax represents the maximum achievable process rate, while KM, known as the Michaelis constant, signifies the drug concentration at which the process rate reaches half its maximum. This relationship between Vmax, KM, and Cp gives rise to three distinct...
1.2K
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

2.3K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
2.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Unravelling cumulative effects of human pressures on demersal fish traits using a driver-pressure-state-impact framework.

Marine environmental research·2026
Same author

Multicentennial cycles in continental demography synchronous with solar activity and climate stability.

Nature communications·2024
Same author

Phytoplankton and particle size spectra indicate intense mixotrophic dinoflagellates grazing from summer to winter.

Journal of plankton research·2022
Same author

Physics or biology? Persistent chlorophyll accumulation in a shallow coastal sea explained by pathogens and carnivorous grazing.

PloS one·2019
Same author

Effect of phytoplankton size diversity on primary productivity in the North Pacific: trait distributions under environmental variability.

Ecology letters·2018
Same author

Intermittency in processing explains the diversity and shape of functional grazing responses.

Oecologia·2012

Related Experiment Video

Updated: Feb 28, 2026

Enhanced Reproducibility and Precision of High-Throughput Quantification of Bacterial Growth Data Using a Microplate Reader
09:15

Enhanced Reproducibility and Precision of High-Throughput Quantification of Bacterial Growth Data Using a Microplate Reader

Published on: July 27, 2022

2.7K

A generic model for changes in microbial kinetic coefficients.

Kai W Wirtz1

  • 1University of Oldenburg, Institute for Chemistry and Biology of the Marine Environment, P.O. Box 2503, 26111 Oldenburg, Germany. wirtz@icbm.de

Journal of Biotechnology
|June 18, 2002
PubMed
Summary
This summary is machine-generated.

Microbial growth models like Monod's have limitations. A new optimization model explains E. coli acclimation dynamics under varying glucose, predicting long-term changes and short-term responses to stress and substrate shifts.

More Related Videos

Precise, High-throughput Analysis of Bacterial Growth
09:00

Precise, High-throughput Analysis of Bacterial Growth

Published on: September 19, 2017

25.0K
A Toolkit to Enable Hydrocarbon Conversion in Aqueous Environments
20:28

A Toolkit to Enable Hydrocarbon Conversion in Aqueous Environments

Published on: October 2, 2012

14.7K

Related Experiment Videos

Last Updated: Feb 28, 2026

Enhanced Reproducibility and Precision of High-Throughput Quantification of Bacterial Growth Data Using a Microplate Reader
09:15

Enhanced Reproducibility and Precision of High-Throughput Quantification of Bacterial Growth Data Using a Microplate Reader

Published on: July 27, 2022

2.7K
Precise, High-throughput Analysis of Bacterial Growth
09:00

Precise, High-throughput Analysis of Bacterial Growth

Published on: September 19, 2017

25.0K
A Toolkit to Enable Hydrocarbon Conversion in Aqueous Environments
20:28

A Toolkit to Enable Hydrocarbon Conversion in Aqueous Environments

Published on: October 2, 2012

14.7K

Area of Science:

  • Microbial Physiology
  • Mathematical Biology
  • Systems Biology

Background:

  • Monod's theory describes microbial growth kinetics but has limitations under variable conditions.
  • Acclimation patterns in kinetic coefficients for Escherichia coli (E. coli) under fluctuating glucose levels are diverse and sometimes contradictory.
  • Existing models struggle to fully capture the spectrum of microbial adaptation phenomena.

Purpose of the Study:

  • To introduce and apply a novel model based on an optimization assumption to understand microbial acclimation.
  • To analyze adaptation phenomena in both steady-state and transient growth situations for E. coli.
  • To predict long-term phenotypic/genotypic changes and explain short-term recovery patterns and metabolic stress effects.

Main Methods:

  • Application of a new optimization-based model to analyze microbial growth kinetics.
  • Calculation of adaptation rates based on differential growth benefits.
  • Quantification of metabolic stress in relation to internal and external cellular states.
  • Modeling of lag phenomena and oscillations in anabolic activity without explicit time or metabolite parameters.

Main Results:

  • The model demonstrates a non-linear trade-off between maximum growth rate and substrate affinity, bounding the dynamics of kinetic coefficients.
  • Long-term effects of phenotypic and genotypic changes under glucose limitation are robustly predicted and explained by adaptive significance.
  • Short-term recovery patterns after substrate excess are explained by cellular internal states and inoculum history.
  • Lag phenomena and oscillations in anabolic activity are reproduced by the model under continuous growth acceleration.

Conclusions:

  • The new optimization model provides a powerful framework for understanding microbial acclimation beyond Monod's theory.
  • Apparent kinetic characteristics, both slow and fast adjustments, are strongly linked to a dynamic optimization strategy.
  • The model successfully predicts diverse adaptation phenomena, including responses to metabolic stress and substrate availability changes.
  • Understanding cellular internal states and inoculum history is crucial for predicting short-term microbial responses.