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 Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

153
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...
153
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

153
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...
153
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

122
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...
122
Block Diagram Reduction01:22

Block Diagram Reduction

356
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
356
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

219
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
219
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

855
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
855

You might also read

Related Articles

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

Sort by
Same author

Data-driven analysis of fine-scale badger movement in the UK.

PLoS computational biology·2025
Same author

Investigating Endogenous Opioids Unravels the Mechanisms Behind Opioid-Induced Constipation, a Mathematical Modeling Approach.

International journal of molecular sciences·2025
Same author

Random walk based snapshot clustering for detecting community dynamics in temporal networks.

Scientific reports·2025
Same author

Clustering time-evolving networks using the spatiotemporal graph Laplacian.

Chaos (Woodbury, N.Y.)·2025
Same author

Exploring transcription modalities from bimodal, single-cell RNA sequencing data.

NAR genomics and bioinformatics·2024
Same author

Neural parameter calibration and uncertainty quantification for epidemic forecasting.

PloS one·2024
Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026
Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026
Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026
Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026
Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026
Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Nov 5, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.6K

Data-driven model reduction of agent-based systems using the Koopman generator.

Jan-Hendrik Niemann1,2, Stefan Klus2,3, Christof Schütte1,2

  • 1Modeling and Simulation of Complex Processes, Zuse Institute Berlin, Berlin, Germany.

Plos One
|May 13, 2021
PubMed
Summary
This summary is machine-generated.

Koopman operator theory enables reduced models for complex agent-based systems. This approach efficiently captures emergent collective behavior from simulation data, even for large populations.

More Related Videos

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.9K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.4K

Related Experiment Videos

Last Updated: Nov 5, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.6K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.9K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.4K

Area of Science:

  • Complex Systems Science
  • Computational Social Science
  • Dynamical Systems Theory

Background:

  • Agent-based models (ABMs) simulate social systems but face computational challenges with large numbers of agents.
  • Emergent patterns in ABMs arise from agent interactions, making direct analysis difficult.
  • Efficient modeling of large-scale social dynamics is crucial for understanding collective behavior.

Purpose of the Study:

  • To develop a data-driven method for creating reduced-order models of agent-based systems.
  • To leverage Koopman operator theory for learning coarse-grained dynamics.
  • To represent the reduced dynamics using ordinary or stochastic differential equations.

Main Methods:

  • Utilized Koopman operator theory applied to simulation data from agent-based models.
  • Derived aggregated state variables representing collective behavior.
  • Formulated reduced dynamical systems (ordinary/stochastic differential equations).

Main Results:

  • Successfully generated reduced models that capture the essential dynamics of agent-based systems.
  • Demonstrated good agreement between reduced system predictions and analytical results for benchmark problems.
  • The accuracy of the reduced models improves with a larger number of agents.

Conclusions:

  • Koopman operator theory provides an effective framework for model reduction in agent-based systems.
  • This data-driven approach significantly reduces computational cost for large-scale simulations.
  • The derived coarse-grained models offer insights into the collective behavior of social systems.