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 Equations for Modeling Growth02:33

Exponential Equations for Modeling Growth

70
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...
70
Bacterial Growth Curve01:28

Bacterial Growth Curve

1.6K
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...
1.6K
Yeast Signaling01:28

Yeast Signaling

16.6K
Yeasts are single-celled organisms, but unlike bacteria, they are eukaryotes (cells with a nucleus). Cell signaling in yeast is similar to signaling in other eukaryotic cells. A ligand, such as a protein or a small molecule released from a yeast cell, attaches to a receptor on the cell surface. The binding stimulates second-messenger kinases to activate or inactivate transcription factors that further regulate gene expression. Many of the yeast intracellular signaling cascades have similar...
16.6K
Cells Coordinate Growth and Proliferation02:36

Cells Coordinate Growth and Proliferation

4.8K
Cell size is a significant factor impacting cellular design, function, and fitness. There exists some internal coordination by which cells double their masses before division, thus, achieving homeostasis. Coordination between cell growth and proliferation depends on the checkpoints in between cell cycle phases. Loss of coordination or failure in the checkpoint mechanism can drive the cell to uncontrolled growth and loss of cellular function. Like dividing cells that coordinate cellular growth,...
4.8K

You might also read

Related Articles

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

Sort by
Same author

GotEnzymes2: expanding coverage of enzyme kinetics and thermal properties.

Nucleic acids research·2025
Same author

Impaired Glucose Metabolism in Young Patients with First-episode Schizophrenia Aged from 16 to 35 Years.

Current neuropharmacology·2025
Same author

Functional DNA-enhanced quantum dots biosensor for rapid and sensitive β-lactamase detection in Haemophilus influenzae.

Biosensors & bioelectronics·2025
Same author

A potential biomarker of varying severity in patients with first-episode drug-naïve depressive disorder: Evidence from the alteration of EEG microstates.

Journal of affective disorders·2025
Same author

Burden and trends of drug use disorders in young adults: global insights from GBD 2021.

Frontiers in psychiatry·2025
Same author

Machine learning-based prediction of response to Ustekinumab with Crohn's disease.

Therapeutic advances in gastroenterology·2025
Same journal

Mathematical Modeling Shows that Overall Infection Burden is Reduced More by Vaccines that Decrease Spread or Accelerate Recovery than those that Lower Severe Infections or Death.

Bulletin of mathematical biology·2026
Same journal

Effects of Seasonal Births and Predation on Disease Spread.

Bulletin of mathematical biology·2026
Same journal

Identifiability, Sensitivity, and Genetic Algorithms in Bacterial Biofilm Selection Models.

Bulletin of mathematical biology·2026
Same journal

Slow Evolution Towards Generalism in a Model of Variable Dietary Range.

Bulletin of mathematical biology·2026
Same journal

CBINN: Cancer Biology-Informed Neural Network for Unknown Parameter Estimation and Missing Physics Identification.

Bulletin of mathematical biology·2026
Same journal

A Cost-Sensitive Behavioral Modeling Analysis of the Early Identification and Control of Infectious Diseases.

Bulletin of mathematical biology·2026
See all related articles

Related Experiment Videos

Game dynamic model for yeast development.

Yuanyuan Huang1, Zhijun Wu

  • 1Department of Mathematics, Program on Bioinformatics and Computational Biology, Iowa State University, Ames, IA 50011, USA. sunnyuan@iastate.edu

Bulletin of Mathematical Biology
|March 22, 2012
PubMed
Summary
This summary is machine-generated.

This study analyzes a game model for yeast strain co-development, simulating population growth and confirming evolutionary stability. The findings validate mathematical models for competing species evolution.

Related Experiment Videos

Area of Science:

  • Evolutionary biology
  • Mathematical modeling
  • Microbial ecology

Background:

  • Game theory and replicator equations are established tools for studying population dynamics and evolutionary stability.
  • Previous research by Gore et al. introduced a game model for co-evolving yeast strains.

Purpose of the Study:

  • To analyze the mathematical properties of the Gore et al. yeast co-development model.
  • To simulate yeast strain growth and compare it with experimental data.
  • To determine the equilibrium state and its evolutionary stability.

Main Methods:

  • Analysis of game theoretic model with varying experimental parameters.
  • Simulation of yeast strain population growth.
  • Computation and analysis of the system's equilibrium state.

Main Results:

  • The mathematical properties of the model were examined under different experimental conditions.
  • Simulated yeast growth aligned with experimental observations.
  • The equilibrium state of the system was proven to be asymptotically and evolutionarily stable.

Conclusions:

  • The analyzed game model accurately represents yeast strain co-development.
  • The study confirms the evolutionary stability of the equilibrium state in this system.
  • Mathematical models provide robust insights into the evolution of competing microbial populations.