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...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).Mechanisms of Genetic VariationThe original sources of genetic variation are mutations,...
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...

You might also read

Related Articles

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

Sort by
Same author

An Optimization Method of Production-Distribution in Multi-Value-Chain.

Sensors (Basel, Switzerland)·2023
Same author

LILRB2-containing small extracellular vesicles from glioblastoma promote tumor progression by promoting the formation and expansion of myeloid-derived suppressor cells.

Cancer immunology, immunotherapy : CII·2023
Same author

Pure endoscopic minimally invasive surgery with a non‑expandable tubular retractor for intradural extramedullary spinal tumors.

Experimental and therapeutic medicine·2023
Same author

Stable Expression of dmiR-283 in the Brain Promises Positive Effects in Endurance Exercise on Sleep-Wake Behavior in Aging <i>Drosophila</i>.

International journal of molecular sciences·2023
Same author

Endoscopic endonasal transsphenoidal approach for craniopharyngioma: A case report.

Experimental and therapeutic medicine·2023
Same author

MorphoSim: an efficient and scalable phase-field framework for accurately simulating multicellular morphologies.

NPJ systems biology and applications·2023

Related Experiment Video

Updated: Jun 4, 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

Modular analysis of the probabilistic genetic interaction network.

Lin Hou1, Lin Wang, Minping Qian

  • 1School of Mathematical Sciences, Peking University, Beijing 100871, China.

Bioinformatics (Oxford, England)
|February 1, 2011
PubMed
Summary

This study introduces a Bayesian approach for analyzing Epistatic Miniarray Profiles (EMAP) data, creating probabilistic genetic interaction networks. This method effectively identifies biologically significant gene modules, outperforming existing algorithms.

More Related Videos

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

Related Experiment Videos

Last Updated: Jun 4, 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

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

Area of Science:

  • Systems Biology
  • Computational Biology
  • Genetics

Background:

  • Epistatic Miniarray Profiles (EMAP) enables large-scale genetic interaction network mapping.
  • Current EMAP data analysis relies on discrete networks and arbitrary thresholds, limiting quantitative insights.
  • A probabilistic network approach is needed to fully exploit EMAP quantitative data.

Purpose of the Study:

  • To develop a probabilistic framework for genetic interaction networks using mixture modeling.
  • To implement a Bayesian approach for identifying densely interacting modules within probabilistic networks.
  • To improve the analysis of quantitative genetic interaction data from EMAP.

Main Methods:

  • Applied mixture modeling to construct a probabilistic genetic interaction network from EMAP data.
  • Utilized a Bayesian approach to identify modules of interacting genes within the probabilistic network.
  • Compared the Bayesian method against hierarchical clustering and Markov clustering algorithms.

Main Results:

  • Mixture modeling effectively served as a soft-thresholding technique for EMAP measures.
  • The Bayesian approach identified 27 modules in a Saccharomyces cerevisiae early secretory pathway dataset.
  • 14 of the identified modules were significantly enriched by known functional gene sets, demonstrating biological relevance.
  • The Bayesian method outperformed hierarchical clustering and Markov clustering in recovering significant biological modules.

Conclusions:

  • The developed Bayesian approach provides a robust method for analyzing EMAP data.
  • This probabilistic network analysis enhances the identification of biologically meaningful gene modules.
  • The findings suggest a superior performance of the Bayesian method for genetic interaction network module discovery.