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

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
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...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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)...
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...

You might also read

Related Articles

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

Sort by
Same author

Explaining hidden mechanisms: a generative model for causal graphs with nonlinear latent factors.

Frontiers in artificial intelligence·2026
Same author

Correction: Machine learning model for predicting the cold-heat pattern in Kampo medicine: a multicenter prospective observational study.

Frontiers in pharmacology·2026
Same author

Case Report: Mixed ductal-lobular carcinoma consisting of invasive lobular carcinoma with a glycogen-rich clear cell pattern and elevated tumor mutation burden.

Frontiers in oncology·2026
Same author

Dual knockout of Fas and TCRα in Jurkat reporter cells enables highly sensitive identification of antigen-specific TCRs.

Biochemical and biophysical research communications·2026
Same author

Functional and structural analysis of KK-LC-1-specific T cell receptors from patients with lung Cancer for immunotherapy.

Cellular immunology·2026
Same author

Deep learning models for image classification of lymphoma: a pilot study in canine.

The Journal of veterinary medical science·2025

Related Experiment Video

Updated: May 9, 2026

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Multi-omics approach for estimating metabolic networks using low-order partial correlations.

Mitsunori Kayano1, Seiya Imoto, Rui Yamaguchi

  • 1Department of Animal and Food Hygiene, Obihiro University of Agriculture and Veterinary Medicine, Hokkaido, Japan. kayano@obihiro.ac.jp

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|August 1, 2013
PubMed
Summary

We developed a new statistical method (MF-PCor) to estimate metabolic networks using multi-omics data. This approach improves accuracy by integrating transcriptome and proteome data, revealing complex metabolic interactions.

More Related Videos

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Related Experiment Videos

Last Updated: May 9, 2026

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Area of Science:

  • Systems Biology
  • Metabolomics
  • Bioinformatics

Background:

  • Metabolome analysis aims to elucidate metabolic pathways and regulatory systems.
  • Multi-omics data (RNA, proteins, metabolites) offer rich insights but integrated analysis methods are lacking.

Purpose of the Study:

  • To develop a robust statistical method for estimating metabolic networks from multi-omics data.
  • To improve the accuracy of metabolic network reconstruction by integrating transcriptome and proteome data.

Main Methods:

  • Developed a novel statistical method, maximum of low-order partial correlations (MF-PCor), utilizing robust correlation coefficients.
  • Incorporated enzyme transcript and protein data to enhance metabolic network estimation.
  • Validated the method through numerical experiments with synthetic and real data, comparing it against correlation networks (Cor) and Gaussian graphical models (GGM).

Main Results:

  • MF-PCor demonstrated superior performance compared to Cor and GGM in estimating metabolic networks.
  • Integration of enzyme transcript and protein data significantly improved the accuracy of network reconstruction.
  • Analysis of real data identified key metabolites, enzymes, and genes involved in specific metabolic reactions difficult to detect with metabolite data alone.

Conclusions:

  • The MF-PCor method provides a powerful approach for systems-level understanding of metabolism using multi-omics data.
  • This method enhances the identification of complex metabolic interactions and regulatory mechanisms.