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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,...
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...
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: May 19, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

A network-based approach for predicting missing pathway interactions.

Saket Navlakha1, Anthony Gitter, Ziv Bar-Joseph

  • 1School of Computer Science and Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

Plos Computational Biology
|August 24, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational framework to predict missing protein interactions in cellular signaling pathways. By focusing on specific cellular responses, the method identifies crucial connections to improve understanding of biological networks.

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

Published on: July 1, 2020

Related Experiment Videos

Last Updated: May 19, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

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

Area of Science:

  • Systems Biology
  • Computational Biology
  • Molecular Biology

Background:

  • Cellular signaling pathways are crucial for biological responses, encoded within large-scale protein interaction networks.
  • Identifying all true protein interactions is challenging, hindering the understanding of signal flow and causal relationships.
  • Existing methods for predicting protein interactions are often too general for specific pathway analysis.

Purpose of the Study:

  • To develop a novel framework for predicting missing protein interactions within specific signaling pathways.
  • To enhance connectivity between upstream protein sources and downstream transcription factor targets.
  • To improve the accuracy of pathway-consistent interaction prediction.

Main Methods:

  • Developed a computational framework to predict new protein interactions by minimizing distances between pathway sources and targets.
  • Employed algorithms to identify and add a minimal set of shortcut edges to protein interaction networks.
  • Focused on proteins involved in specific cellular responses to ensure pathway consistency.

Main Results:

  • Applied the framework to the osmotic stress response pathway in yeast (S. cerevisiae).
  • Identified several previously unknown interactions, with some supported by existing literature.
  • Experimental validation supported a novel, unreported interaction, demonstrating the framework's efficacy.

Conclusions:

  • The developed framework effectively predicts pathway-consistent protein interactions.
  • This approach aids in elucidating complex signaling cascades and causal relationships.
  • The generalizable framework has potential applications in edge prediction across various biological domains.