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Related Concept Videos

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,...
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...
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...
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...
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...

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Related Experiment Video

Updated: Jun 12, 2026

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Automated identification of pathways from quantitative genetic interaction data.

Alexis Battle1, Martin C Jonikas, Peter Walter

  • 1Department of Computer Science, Stanford University, Stanford, CA 94305-9010, USA.

Molecular Systems Biology
|June 10, 2010
PubMed
Summary

This study introduces a new Bayesian method to reconstruct biological pathways from gene interaction data. The approach accurately maps known pathways and identifies novel gene relationships, like SGT2 in tail-anchored biogenesis.

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Area of Science:

  • Systems Biology
  • Genetics
  • Computational Biology

Background:

  • High-throughput quantitative genetic interaction (GI) data reveals gene functional dependencies.
  • Analytical tools for interpreting complex GI data are needed to fully leverage these datasets.

Purpose of the Study:

  • To develop a novel Bayesian learning method for reconstructing biological pathway structures.
  • To automatically analyze quantitative phenotypes of double knockout organisms for pathway inference.

Main Methods:

  • Utilized a Bayesian learning approach.
  • Applied the method to quantitative genetic interaction data for endoplasmic reticulum (ER) genes.
  • Used the unfolded protein response as a quantitative phenotype.

Main Results:

  • Successfully reconstructed known functional pathways, including N-linked glycosylation and ER-associated protein degradation.
  • Identified novel gene relationships, such as placing SGT2 within the tail-anchored biogenesis pathway.
  • Experimentally validated the novel finding regarding SGT2.

Conclusions:

  • The developed Bayesian method effectively reconstructs detailed biological pathway structures from quantitative GI data.
  • The approach is adaptable for future high-throughput quantitative GI datasets and broader biological applications.