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Related Concept Videos

Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
Population Growth00:57

Population Growth

Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.However, realistic environmental conditions limit the number of...
Pharmacokinetic–Pharmacodynamic Relationship: Model Components01:14

Pharmacokinetic–Pharmacodynamic Relationship: Model Components

Pharmacokinetic-pharmacodynamic (PK–PD) modeling is essential in drug development and clinical pharmacology. It provides a quantitative framework to predict drug behavior and response over time. This approach integrates pharmacokinetics (PK), which describes the drug's absorption, distribution, metabolism, and excretion, with pharmacodynamics (PD), which characterizes the drug’s biological effects and mechanisms of action.The disposition kinetics of a drug determine its plasma...
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...
Exponential Equations for Modeling Growth01:26

Exponential Equations for Modeling Growth

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 the relative...
Speciation Rates01:07

Speciation Rates

Speciation can proceed at markedly different rates, and evolutionary biologists commonly describe these differences through the models of gradualism and punctuated equilibrium. Both patterns explain how new species arise, but they differ in the tempo and continuity of evolutionary change. In both cases, evolutionary change arises from heritable variation within populations, with natural selection often shaping traits that improve survival and reproduction under specific environmental conditions.

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Related Experiment Video

Updated: Jun 22, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

Threshold dynamics in a time-delayed epidemic model with dispersal.

Michael C White1, Xiao-Qiang Zhao

  • 1Department of Mathematics and Statistics, Memorial University of Newfoundland, PO Box 4200, St. John's, NL, Canada A1C 5S7. mikewhite@nl.rogers.com

Mathematical Biosciences
|July 1, 2009
PubMed
Summary

This study shows that diseases persist when the basic reproduction number exceeds one, even with population dispersal between two areas. A small initial infected population helps the disease die out if the basic reproduction number is below one.

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Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
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Area of Science:

  • Mathematical Biology
  • Epidemiology
  • Population Dynamics

Background:

  • Investigates the global dynamics of a time-delayed mathematical model.
  • Considers population dispersal between two distinct patches.

Purpose of the Study:

  • To analyze disease persistence and extinction in a spatially structured population.
  • To determine the role of the basic reproduction number and initial conditions.

Main Methods:

  • Application of persistence theory for a general class of birth functions.
  • Mathematical modeling of disease spread with time delays and dispersal.
  • Numerical simulations using biologically relevant birth functions.

Main Results:

  • Disease persistence is proven when the basic reproduction number (R0) is greater than one.
  • Disease extinction is demonstrated if R0 is less than one and the initial infected population is small.
  • Dispersal's relevance to disease dynamics is illustrated.

Conclusions:

  • The basic reproduction number is a critical threshold for disease persistence.
  • Initial population size significantly influences disease extinction dynamics.
  • Population dispersal plays a key role in the spatial spread of diseases.