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

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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

Mechanistic Models: Overview of Compartment Models

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...
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal assumptions,...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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...

You might also read

Related Articles

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

Sort by
Same author

Environmental former <i>Massilia</i> group bacteria secrete metabolites that promote <i>Leptospira</i> growth.

mSystems·2026
Same author

Multimodal PET Defines a 'Goldilocks' Thermal Window for Focused Ultrasound Ablation and Immunotherapy Combinations.

bioRxiv : the preprint server for biology·2026
Same author

HMEC-1 extracellular vesicles as regulators of endothelial cell activation under inflammation.

bioRxiv : the preprint server for biology·2026
Same author

Cellularized hydrogel-interfaced cantilever (CHIC) biosensors for monitoring 3D cell culture mechanical properties.

Biosensors & bioelectronics·2026
Same author

Genome-scale metabolic modelling identifies vaginal microbiome members as potential probiotics.

Nature microbiology·2026
Same author

Transcriptional analyses identify pericyte-centered signaling programs altered by sex and brain region in Alzheimer's Disease.

Communications biology·2026
Same journal

Physics-Informed Machine Learning in Biomedical Science and Engineering.

Annual review of biomedical engineering·2026
Same journal

Advancements and Challenges in Computer-Assisted Medical Interventions for Image-Guided Prostate Cancer Treatments.

Annual review of biomedical engineering·2026
Same journal

Recent Advances in mRNA Therapeutic Cancer Vaccines.

Annual review of biomedical engineering·2026
Same journal

Artificial Intelligence-Based Analysis of Laparoscopic Imaging for Intraoperative Surgical Decision Support.

Annual review of biomedical engineering·2026
Same journal

Viscoelasticity of the Heart: An Overview of Viscoelastic Measurements at Different Scales.

Annual review of biomedical engineering·2026
Same journal

Digital Twins for Biofluids.

Annual review of biomedical engineering·2026
See all related articles

Related Experiment Video

Updated: May 11, 2026

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
09:44

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

Multiscale computational models of complex biological systems.

Joseph Walpole1, Jason A Papin, Shayn M Peirce

  • 1Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.

Annual Review of Biomedical Engineering
|May 7, 2013
PubMed
Summary
This summary is machine-generated.

Biomedical engineering uses multiscale modeling to integrate complex biological data. Coupling continuous and discrete systems offers best practices for developing accurate models and gaining novel insights.

More Related Videos

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

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

Related Experiment Videos

Last Updated: May 11, 2026

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
09:44

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

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

Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Systems Biology

Background:

  • Multiscale modeling is crucial for integrating diverse biological data across spatial, temporal, and functional scales.
  • Advancements in computing and high-throughput experiments facilitate comprehensive investigation of biological phenomena.

Purpose of the Study:

  • To review recent multiscale models in biological systems.
  • To propose best practices for future multiscale model development.
  • To highlight the use of quantitative methods for analyzing model-derived data.

Main Methods:

  • Coupling continuous and discrete modeling techniques to capture biological information across spatial scales.
  • Utilizing quantitative biomedical engineering methods for data analysis.
  • Reviewing recently published multiscale models.

Main Results:

  • The integration of continuous and discrete systems effectively captures biological information across spatial scales.
  • Multiscale models enable deeper insights into biological systems through quantitative data analysis.
  • Successful models demonstrate the power of tailored modeling techniques.

Conclusions:

  • Coupling continuous and discrete systems represents a best practice for multiscale biological modeling.
  • Quantitative analysis of multiscale model data provides nonintuitive insights.
  • The field faces challenges but offers significant opportunities for future advancements.