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

267
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...
267
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

230
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...
230
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

339
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...
339
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

482
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
482
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

226
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...
226
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

325
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
325

You might also read

Related Articles

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

Sort by
Same author

Viral evolution during primary infection in immunocompromised hosts.

PLoS computational biology·2026
Same author

Multivariate Host-Pathogen Interactions Driving Heterogeneous Viral Shedding and CD8<sup>+</sup> T cell Control of Influenza Virus Infection.

medRxiv : the preprint server for health sciences·2025
Same author

Calibrated Ecosystem Models Cannot Predict the Consequences of Conservation Management Decisions-Clarification.

Ecology letters·2025
Same author

The Intersection of SARS-CoV-2 and Diabetes.

Microorganisms·2025
Same author

Virtual Clinical Trial Reveals Significant Clinical Potential of Targeting Tumor-Associated Macrophages and Microglia to Treat Glioblastoma.

CPT: pharmacometrics & systems pharmacology·2025
Same author

Calibrated Ecosystem Models Cannot Predict the Consequences of Conservation Management Decisions.

Ecology letters·2024

Related Experiment Video

Updated: Jan 11, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.3K

Trajectory-matching ABC-MCMC for simulating heterogeneous dynamics in mechanistic models.

Fatemeh Beigmohammadi1,2, Jordan J A Weaver3, Solène Hegarty-Cremer1,2

  • 1Sainte-Justine University Hospital Research Centre, Montréal, Québec, Canada.

Biorxiv : the Preprint Server for Biology
|November 19, 2025
PubMed
Summary
This summary is machine-generated.

We developed trajectory-matching ABC-MCMC (TM-ABC-MCMC) to model biological system heterogeneity. This new computational method efficiently generates virtual patient cohorts and supports virtual clinical trials.

Keywords:
Markov chain Monte-Carloapproximate Bayesian computationmechanistic mathematical modelstrajectory matchingvirtual patient cohorts

More Related Videos

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.5K
Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

3.1K

Related Experiment Videos

Last Updated: Jan 11, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.3K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.5K
Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

3.1K

Area of Science:

  • Computational Biology
  • Systems Biology
  • Mathematical Modeling

Background:

  • Complex biological systems exhibit inherent heterogeneity, complicating experimental and clinical outcome analysis.
  • Mechanistic mathematical models are crucial for studying this heterogeneity.
  • Integrating computational techniques like virtual patient cohorts and trials is gaining traction in research and regulatory settings.

Purpose of the Study:

  • To address limitations of existing computational methods for modeling biological heterogeneity.
  • To introduce a novel technique, trajectory-matching ABC-MCMC (TM-ABC-MCMC), for enhanced virtual patient cohort generation and virtual clinical trials.

Main Methods:

  • Developed trajectory-matching ABC-MCMC (TM-ABC-MCMC), a model-based computational approach.
  • Constrained model trajectories within data bounds to generate parameter heterogeneity.
  • Tested TM-ABC-MCMC performance against existing ABC-MCMC algorithms on diverse mechanistic models.

Main Results:

  • TM-ABC-MCMC accurately reproduces observed biological noise across systems of varying complexity.
  • The method maintains computational efficiency compared to standard ABC-MCMC.
  • Demonstrated effective generation of heterogeneity in mechanistic mathematical models.

Conclusions:

  • TM-ABC-MCMC offers a novel and efficient approach for modeling biological heterogeneity.
  • This technique has significant implications for model-based experimental design.
  • Facilitates the creation of more realistic virtual patient cohorts and virtual clinical trials.