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

Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
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)...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
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...

You might also read

Related Articles

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

Sort by
Same author

Post-booster longitudinal plasma proteomic changes following BNT162b2 COVID-19 vaccination in Qatar.

Frontiers in immunology·2026
Same author

A new serological autoantibody signature associated with multiple sclerosis.

Neurobiology of disease·2025
Same author

Modeling long-term dynamics of carbon dioxide and oxygen in high-altitude wetlands.

Journal of environmental management·2025
Same author

A multi-omics approach reveals dysregulated TNF-related signaling pathways in circulating NK and T cell subsets of young children with autism.

Genes and immunity·2025
Same author

Proteomics analysis of extracellular vesicles for biomarkers of autism spectrum disorder.

Frontiers in molecular biosciences·2024
Same author

Deletion of TRPC6, an Autism Risk Gene, Induces Hyperexcitability in Cortical Neurons Derived from Human Pluripotent Stem Cells.

Molecular neurobiology·2023

Related Experiment Video

Updated: Jul 7, 2026

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Gene network inference via structural equation modeling in genetical genomics experiments.

Bing Liu1, Alberto de la Fuente, Ina Hoeschele

  • 1Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061-0477, USA.

Genetics
|February 5, 2008
PubMed
Summary

This study introduces a novel method for gene network inference in systems genetics. The approach constructs an encompassing directed network and uses structural equation modeling to identify gene regulatory relationships.

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Related Experiment Videos

Last Updated: Jul 7, 2026

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Genetics
  • Systems Biology
  • Bioinformatics

Background:

  • Gene network inference is crucial for understanding complex biological systems.
  • Genetical genomics and systems genetics experiments generate large datasets for network analysis.

Purpose of the Study:

  • To develop a method for gene network inference in genetical genomics experiments.
  • To construct an encompassing directed network (EDN) integrating various regulatory relationships.
  • To apply structural equation modeling (SEM) for inferring cyclic gene networks.

Main Methods:

  • Expression quantitative trait locus (eQTL) mapping integrating cis-, cis-trans-, and trans-regulation.
  • Identification of regulator-target pairs using local structural models.
  • Construction of an encompassing directed network (EDN).
  • Application of structural equation modeling (SEM) with penalized likelihood ratio and Occam's window for network inference.

Main Results:

  • The proposed SEM algorithm successfully infers networks with hundreds of genes and eQTL.
  • The method is capable of modeling cyclic networks, as demonstrated by its application to simulated and yeast data.
  • The EDN framework effectively integrates diverse genetic regulation information.

Conclusions:

  • The developed SEM-based approach provides a robust method for gene network inference in systems genetics.
  • The integration of eQTL mapping and network modeling offers a powerful tool for dissecting genetic architectures.
  • This method advances our ability to understand complex gene regulatory networks in biological systems.