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 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...
Real Time RT-PCR02:57

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...
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)...
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...
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...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...

You might also read

Related Articles

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

Sort by
Same author

Prolonged Prescription Rates of Z-Hypnotics Among Individuals with Chronic Musculoskeletal Pain and Insomnia: The HUNT Study.

Nature and science of sleep·2026
Same author

Long-Term Outcomes of Patients Seeking Specialized Health Care for Persisting Postconcussion Symptoms After a Mild Head Injury.

Archives of physical medicine and rehabilitation·2026
Same author

Traumatic Axonal Injury on Early Magnetic Resonance Imaging and Associations with Long-Term Outcome in Children with Moderate and Severe Traumatic Brain Injury.

Journal of neurotrauma·2026
Same author

Roadmap for light interaction with biophotonic surfaces and their diverse applications.

Journal of biomedical optics·2026
Same author

Dynamic cerebral autoregulation in infants undergoing major non-cardiac surgery.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism·2026
Same author

Neonatal Outcomes Following a Preconception Lifestyle Intervention in People at Risk of Gestational Diabetes: Secondary Findings from the BEFORE THE BEGINNING Randomized Controlled Trial.

Nutrients·2025
Same journal

Balanced mediated pathway detection in genomic data.

Statistical applications in genetics and molecular biology·2026
Same journal

Annealed variational mixtures for disease subtyping and biomarker discovery.

Statistical applications in genetics and molecular biology·2026
Same journal

Performance of the permutation test approach with base calling errors for detecting changes in variant allele frequencies in ctDNA for a single patient.

Statistical applications in genetics and molecular biology·2026
Same journal

BLOG: Bayesian longitudinal omics with group constraints.

Statistical applications in genetics and molecular biology·2026
Same journal

AI-driven risk prediction and categorization in cystic fibrosis leveraging AttentiveLSTM and Fox Wolf Optimizer.

Statistical applications in genetics and molecular biology·2026
Same journal

Perfect collinearity not created equal: measuring and visualizing the severity of multi-collinearity of modern omics data.

Statistical applications in genetics and molecular biology·2026
See all related articles

Related Experiment Video

Updated: Jun 15, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

A Bayesian hierarchical model for quantitative real-time PCR data.

Turid Follestad1, Tommy S Jørstad, Sten E Erlandsen

  • 1Norwegian University of Science and Technology. turid.follestad@sintef.no

Statistical Applications in Genetics and Molecular Biology
|March 4, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian model for quantitative real-time polymerase chain reaction (PCR) to accurately measure DNA copy numbers. The novel approach enhances precision in biological sample analysis using advanced statistical methods.

More Related Videos

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow
12:53

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow

Published on: June 14, 2017

qPCRTag Analysis - A High Throughput, Real Time PCR Assay for Sc2.0 Genotyping
07:00

qPCRTag Analysis - A High Throughput, Real Time PCR Assay for Sc2.0 Genotyping

Published on: May 25, 2015

Related Experiment Videos

Last Updated: Jun 15, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow
12:53

Bidirectional Retroviral Integration Site PCR Methodology and Quantitative Data Analysis Workflow

Published on: June 14, 2017

qPCRTag Analysis - A High Throughput, Real Time PCR Assay for Sc2.0 Genotyping
07:00

qPCRTag Analysis - A High Throughput, Real Time PCR Assay for Sc2.0 Genotyping

Published on: May 25, 2015

Area of Science:

  • Biostatistics
  • Molecular Biology
  • Genetics

Background:

  • Quantitative real-time polymerase chain reaction (qPCR) is crucial for DNA quantification.
  • Accurate relative quantification of DNA copy number across samples is challenging.
  • Existing methods may not fully account for reaction kinetics and inherent randomness.

Purpose of the Study:

  • To develop a Bayesian hierarchical model for quantitative real-time PCR data.
  • To enable accurate relative quantification of DNA copy number in biological samples.
  • To incorporate reaction efficiency dependent on target DNA abundance.

Main Methods:

  • A hidden Markov model framework for fluorescence intensity data.
  • Modeling reaction efficiency based on DNA abundance and reaction kinetics.
  • Bayesian inference using Markov chain Monte Carlo (MCMC) for posterior distributions.
  • Incorporation of process randomness and measurement error.

Main Results:

  • The developed Bayesian model provides a robust framework for qPCR data analysis.
  • The model successfully estimates DNA copy number with consideration for reaction kinetics.
  • Application to simulated and experimental data demonstrates the method's validity.

Conclusions:

  • The Bayesian hierarchical model offers an improved approach for relative DNA quantification in qPCR.
  • This method enhances the accuracy and reliability of results from biological samples.
  • The model's ability to handle inherent variability is a key advantage.