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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Optimal Foraging00:48

Optimal Foraging

How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.

You might also read

Related Articles

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

Sort by
Same author

Quantifying the contributions of asymptomatic and symptomatic colonized patients to <i>Clostridioides difficile</i> acquisition in oncological units.

medRxiv : the preprint server for health sciences·2026
Same author

Data-driven modeling of amyloid-β targeted antibodies for Alzheimer's disease.

NPJ systems biology and applications·2025
Same author

An agent-based model for household COVID-19 transmission in Gauteng, South Africa.

PloS one·2025
Same author

Data-Driven Modeling of Amyloid-beta Targeted Antibodies for Alzheimer's Disease.

Research square·2025
Same author

Optimal control of species augmentation in a competition model.

Mathematical biosciences·2025
Same author

The contribution of community transmission to the burden of hospital-associated pathogens: A systematic scoping review of epidemiological models.

One health (Amsterdam, Netherlands)·2025

Related Experiment Video

Updated: May 16, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Optimal control in individual-based models: implications from aggregated methods.

Paula Federico1, Louis J Gross, Suzanne Lenhart

  • 1Department of Mathematics, Computer Science, and Physics, Capital University, Columbus, Ohio 43209, USA.

The American Naturalist
|December 14, 2012
PubMed
Summary
This summary is machine-generated.

Optimal control strategies derived from aggregated models can effectively manage harmful species in individual-based models (IBMs) when resource heterogeneity is low. High heterogeneity may limit the effectiveness of these simplified management approaches.

Related Experiment Videos

Last Updated: May 16, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Ecology
  • Population Dynamics
  • Ecological Modeling

Background:

  • Individual-based models (IBMs) are increasingly used in ecology, offering detailed insights into population dynamics.
  • Aggregated models (AMs) provide a simpler approach, but their applicability to IBM-driven systems is unclear.
  • Guidance is lacking on managing populations effectively when IBMs reveal complex individual behaviors.

Purpose of the Study:

  • To assess if optimal control theory applied to AMs can effectively manage populations simulated by IBMs.
  • To determine if individual interactions, spatial distribution, and landscape heterogeneity impact the effectiveness of AM-derived controls.
  • To evaluate the sensitivity of management strategies to individual behavior details in population responses.

Main Methods:

  • Developed a simple resource-consumer IBM to simulate population dynamics.
  • Applied optimal control theory to an aggregated model (AM) representing the same system.
  • Compared the effectiveness of AM-derived control strategies against the IBM under varying degrees of spatial resource heterogeneity.

Main Results:

  • Optimal control derived from AMs effectively managed the harmful species in the IBM under conditions of weak spatial resource heterogeneity.
  • The effectiveness of AM-derived controls was limited in scenarios with strong spatial heterogeneity in the resource.
  • Individual interactions and spatial distribution influenced population responses, impacting control efficacy.

Conclusions:

  • AM-derived optimal control can be a viable strategy for managing populations modeled by IBMs, particularly when spatial heterogeneity is minimal.
  • Significant spatial heterogeneity necessitates a combined approach, integrating simplified models with detailed simulation models for effective population management.
  • Management strategies may be robust to individual-level complexities when landscape features are relatively uniform.