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: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

329
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...
329
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

234
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
234
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

255
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...
255
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

225
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...
225
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

312
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
312
Clearance Models: Physiological Models01:09

Clearance Models: Physiological Models

266
Drug clearance is a critical pharmacokinetic process involving the irreversible removal of drugs from the body through various organs over a specified time period. Physiological models are indispensable in determining organ-specific clearance, defined by the proportion of the drug eliminated per unit of time from the organ's blood volume.
The organ's clearance rate depends on the blood flow to the organ and the extraction ratio (E). The extraction ratio describes the organ's...
266

You might also read

Related Articles

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

Sort by
Same author

Temporal sleep spindle clustering and slow-oscillation coupling in motor memory consolidation and generalization.

Communications biology·2026
Same author

Symptomatic and Asymptomatic Carotid Plaques Classification using CT Images and Hybrid Deep Transfer Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Mamba-CAM-Sleep: A Mamba-based Channel Attention Model for Sleep Staging Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Adaptive long-range modeling of EEG and ECG with Mamba and dynamic graph learning.

Scientific reports·2025
Same author

Enhancing sleep in professional rugby players: Observation and sleep interventions.

Journal of sports sciences·2025
Same author

Baseline individual factors associated with clinical outcomes in adults with non-specific low back pain following manual therapy: a systematic review.

BMC complementary medicine and therapies·2025
Same journal

Bridging the Gap - Advancing Microfluidics From Laboratory to Point-of-Care.

IEEE reviews in biomedical engineering·2026
Same journal

Review of Current Advances in Ultrasound Computed Tomography for Medical Imaging.

IEEE reviews in biomedical engineering·2026
Same journal

Gas Embolism: Fundamentals, Diagnosis, and Treatment.

IEEE reviews in biomedical engineering·2026
Same journal

Sonogenetics for Precision Medicine: A Focus on Immunoengineering and Genome Engineering.

IEEE reviews in biomedical engineering·2026
Same journal

Current Trends in Ultrasound Wearables: Spotlight on System Architecture.

IEEE reviews in biomedical engineering·2026
Same journal

A Perspective on Non-Invasive Blood Pressure Monitoring: Bridging Emerging Principles, Enabling Technologies and Extended Applications.

IEEE reviews in biomedical engineering·2026
See all related articles

Related Experiment Video

Updated: Jan 8, 2026

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

7.0K

Modeling Brain-Heart Interaction: A Review of Mechanistic Dynamical Models.

Sara Nour Sadoun, Arnaud Boutin, Francois Cottin

    IEEE Reviews in Biomedical Engineering
    |December 23, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Brain-heart interaction (BHI) is crucial for autonomic regulation and mental processes. This review focuses on mechanistic dynamical systems models to understand BHI, identify biomarkers, and improve clinical applications.

    More Related Videos

    Anatomically Realistic Neonatal Heart Model for Use in Neonatal Patient Simulators
    10:05

    Anatomically Realistic Neonatal Heart Model for Use in Neonatal Patient Simulators

    Published on: February 5, 2019

    6.4K
    Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
    12:09

    Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

    Published on: January 8, 2013

    14.0K

    Related Experiment Videos

    Last Updated: Jan 8, 2026

    Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
    09:20

    Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

    Published on: February 13, 2021

    7.0K
    Anatomically Realistic Neonatal Heart Model for Use in Neonatal Patient Simulators
    10:05

    Anatomically Realistic Neonatal Heart Model for Use in Neonatal Patient Simulators

    Published on: February 5, 2019

    6.4K
    Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
    12:09

    Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

    Published on: January 8, 2013

    14.0K

    Area of Science:

    • Neuroscience
    • Physiology
    • Cardiovascular Science

    Background:

    • Brain-heart interaction (BHI) is vital for autonomic regulation, cognition, and emotion.
    • BHI dysfunction is linked to cardiovascular, neurological, and psychiatric disorders.
    • Understanding BHI requires viewing the body as an interoceptive network.

    Purpose of the Study:

    • To review mechanistic, physiology-inspired dynamical systems models of BHI.
    • To identify physiological subsystems, assumptions, strengths, and limitations of these models.
    • To outline technical perspectives for inferring latent interoceptive quantities and integrating brain modeling.

    Main Methods:

    • Review of state-of-the-art dynamical models of BHI.
    • Analysis of physiological subsystems, model assumptions, and limitations.
    • Identification of technical requirements for advanced BHI modeling.

    Main Results:

    • Detailed account of current dynamical models for BHI.
    • Delineation of methodological directions for BHI research.
    • Highlighting applications for explanatory insight, prediction, and clinical targets.

    Conclusions:

    • Mechanistic dynamical modeling offers a powerful approach to understanding BHI.
    • Future work should focus on inferring unobservable interoceptive signals and integrating neural mechanisms.
    • These models can lead to improved diagnostics and treatments for various conditions.