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

177
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...
177
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

202
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...
202
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

404
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,...
404

You might also read

Related Articles

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

Sort by
Same author

Agent-based modeling demonstrates how target-independent processes supplement killing by antibody-drug conjugates in cancer therapy.

PLoS computational biology·2026
Same author

The immunometabolic topography of cellular organization and bacterial control in tuberculosis granulomas.

Nature immunology·2026
Same author

In Vivo Auto-tuning of Antibody-Drug Conjugate Delivery to Maximize Efficacy Using High-Avidity, Low-Affinity Antibodies.

Molecular cancer therapeutics·2025
Same author

Development of the Human-Equine Attachment Scale.

Equine veterinary journal·2025
Same author

The Role of Transient Crosslinks in the Chromatin Search Response to DNA Damage.

International journal of molecular sciences·2025
Same author

Inference of weak-form partial differential equations describing migration and proliferation mechanisms in wound healing experiments on cancer cells.

PLoS computational biology·2025

Related Experiment Video

Updated: Dec 10, 2025

Multi-scale Analysis of Bacterial Growth Under Stress Treatments
12:08

Multi-scale Analysis of Bacterial Growth Under Stress Treatments

Published on: November 28, 2019

9.8K

Global sensitivity analysis of biological multi-scale models.

Marissa Renardy1, Caitlin Hult1,2, Stephanie Evans1

  • 1University of Michigan Medical School, Department of Microbiology and Immunology.

Current Opinion in Biomedical Engineering
|September 1, 2020
PubMed
Summary
This summary is machine-generated.

Global sensitivity analysis is crucial for understanding uncertainty in complex, multi-scale biological models. This review highlights its value in model development, analysis, and reduction for better biological insights.

More Related Videos

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.2K
Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.5K

Related Experiment Videos

Last Updated: Dec 10, 2025

Multi-scale Analysis of Bacterial Growth Under Stress Treatments
12:08

Multi-scale Analysis of Bacterial Growth Under Stress Treatments

Published on: November 28, 2019

9.8K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.2K
Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.5K

Area of Science:

  • Mathematical biology
  • Computational modeling
  • Systems biology

Background:

  • Biological systems are inherently complex, often requiring multi-scale models to capture behavior across different levels.
  • Multi-scale models, while powerful, face challenges due to nonlinearity, stochasticity, and unknown parameters, leading to uncertainty.
  • Quantifying and managing uncertainty is vital for reliable biological model development and analysis.

Purpose of the Study:

  • To review global sensitivity analysis (GSA) methods applicable to multi-scale and multi-compartment biological models.
  • To demonstrate the utility of GSA in enhancing the development, analysis, and reduction of these complex models.
  • To emphasize the advantages and practical applications of GSA techniques in systems biology.

Main Methods:

  • Overview of established global sensitivity analysis techniques.
  • Discussion of approaches for adapting GSA to multi-scale and multi-compartment modeling frameworks.
  • Illustrative examples of GSA application in model reduction strategies.

Main Results:

  • GSA provides a robust framework for assessing uncertainty in multi-scale biological models.
  • Sensitivity analysis can effectively guide model simplification and identify key parameters.
  • The application of GSA enhances the interpretability and reliability of complex biological models.

Conclusions:

  • Global sensitivity analysis is an indispensable tool for navigating uncertainty in multi-scale biological modeling.
  • Implementing GSA facilitates more robust model development, validation, and reduction.
  • This approach significantly improves the scientific value and predictive power of computational biology models.