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

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.
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...
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,...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...

You might also read

Related Articles

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

Sort by
Same author

Physics-Informed Neural Operators for Parameter Inference in Multi-vessel Cardiovascular Networks.

Annals of biomedical engineering·2026
Same author

Nanoparticle Metrology of Silicates Using Time-Resolved Multiplexed Dye Fluorescence Anisotropy, Small Angle X-ray Scattering, and Molecular Dynamics Simulations.

Materials (Basel, Switzerland)·2024
Same author

Keratin intrinsic fluorescence as a mechanism for non-invasive monitoring of its glycation.

Methods and applications in fluorescence·2022
Same author

Impact of the Flavonoid Quercetin on β-Amyloid Aggregation Revealed by Intrinsic Fluorescence.

The journal of physical chemistry. B·2022
Same author

Collagen Glycation Detected by Its Intrinsic Fluorescence.

The journal of physical chemistry. B·2021
Same author

Detecting beta-amyloid glycation by intrinsic fluorescence - Understanding the link between diabetes and Alzheimer's disease.

Archives of biochemistry and biophysics·2021

Related Experiment Video

Updated: Jul 9, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Bayesian ranking of biochemical system models.

Vladislav Vyshemirsky1, Mark A Girolami

  • 1Department of Computing Science, University of Glasgow, Glasgow, G12 8QQ, UK. vvv@dcs.gla.ac.uk

Bioinformatics (Oxford, England)
|December 7, 2007
PubMed
Summary

Comparing computational methods for model selection in Systems Biology is crucial. Annealed Importance Sampling and Annealing-Melting Integration offer stable Bayes factor estimation for nonlinear biochemical models.

Area of Science:

  • Systems Biology
  • Computational Biology
  • Biochemical Modeling

Background:

  • Model selection is critical in Systems Biology for hypothesis evaluation.
  • Bayes factors are used for model comparison, but require marginal likelihood computation.
  • Calculating marginal likelihoods for nonlinear models is computationally challenging.

Purpose of the Study:

  • To evaluate and compare four distinct methods for estimating marginal likelihoods.
  • To identify robust methods for computing Bayes factors in biochemical systems.
  • To assess the stability and reliability of different estimation techniques.

Main Methods:

  • Prior Arithmetic Mean estimator
  • Posterior Harmonic Mean estimator
  • Annealed Importance Sampling

More Related Videos

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

Related Experiment Videos

Last Updated: Jul 9, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

  • Annealing-Melting Integration

    • Main Results:

    • Assessed four marginal likelihood estimation methods on a Systems Biology case study.
    • Investigated the variance of Bayes factor estimates.
    • Annealed Importance Sampling and Annealing-Melting Integration demonstrated superior stability for nonlinear model comparison.

    Conclusions:

    • Annealed Importance Sampling and Annealing-Melting Integration are reliable methods for estimating Bayes factors.
    • These stable methods aid in robust model ranking and hypothesis support in Systems Biology.
    • The study provides guidance for selecting appropriate computational tools for biochemical model analysis.