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...
Epistasis01:39

Epistasis

In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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,...
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
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...
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...

You might also read

Related Articles

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

Sort by
Same author

Single-cell eQTL mapping reveals convergent glial-neuronal risk architecture in Parkinson's disease.

bioRxiv : the preprint server for biology·2026
Same author

Chemically Defined Non-human Glycans Comprising Galactose-α1-3-Galactose (α-Gal) Epitopes Glycoengineered into the Fragment Antigen-Binding (Fab) Domain of Cetuximab Differentially Affect Human Anti-α-Gal Immunoglobulin E (IgE) Binding.

ACS pharmacology & translational science·2026
Same author

Experimental and computational methods for allelic imbalance analysis from single-nucleus RNA-seq data.

Genome biology·2026
Same author

Integrating Long-Read Structural Variant Analysis with single-nucleus RNA-seq to Elucidate Gene Expression Effects in Disease.

bioRxiv : the preprint server for biology·2026
Same author

Postoperative outcomes of antireflux surgery in lung transplant recipients.

Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract·2025
Same author

The Interplay of Pollution, Child Opportunity, and High Health Care Utilization in Children With Asthma in San Diego County.

Pediatric emergency care·2025
Same journal

Another 10 years of PLOS Computational Biology: A data-driven reflection on trends in genomics research.

PLoS computational biology·2026
Same journal

Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.

PLoS computational biology·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Jun 1, 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

Quantitative epistasis analysis and pathway inference from genetic interaction data.

Hilary Phenix1, Katy Morin, Cory Batenchuk

  • 1Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada.

Plos Computational Biology
|May 19, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new quantitative model and experimental method to infer gene order and network architecture from genetic interactions. The approach accurately predicts relationships in complex biological pathways by analyzing fitness and expression data.

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

Mapping Dysfunctional Protein-Protein Interactions in Disease
09:39

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Related Experiment Videos

Last Updated: Jun 1, 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

Mapping Dysfunctional Protein-Protein Interactions in Disease
09:39

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Area of Science:

  • Systems Biology
  • Quantitative Genetics
  • Molecular Biology

Background:

  • Inferring regulatory and metabolic network models from quantitative genetic interaction data is a significant challenge in systems biology.
  • Understanding gene order and pathway architecture is crucial for deciphering complex biological systems.

Purpose of the Study:

  • To present a novel quantitative model for interpreting epistasis within signaling pathways.
  • To develop an experimental method for determining pathway architecture and gene order.
  • To extract quantitative parameters for enhanced genetic network models.

Main Methods:

  • Developed a quantitative model for interpreting epistasis in response to external signals.
  • Applied the model to quantify effects of single and double gene deletions on fitness and reporter gene expression.
  • Systematically analyzed the galactose utilization pathway in Saccharomyces cerevisiae.

Main Results:

  • Fitness trait analysis revealed metabolic enzyme order and intermediate accumulation effects.
  • Expression trait analysis identified transcriptional regulatory gene order, regulatory signals, and their strengths.
  • Combined analysis of both traits accurately inferred ~80% of known relationships with no false positives.

Conclusions:

  • The novel method accurately infers gene order and pathway architecture from quantitative genetic data.
  • Applicable to diverse biological systems where combinatorial loss-of-function mutations can be quantified.
  • Provides a new level of quantitative information for genetic network models.