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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,...
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...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...

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Related Experiment Video

Updated: Jun 17, 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

AOP Network Box: An Integrative Framework for Bridging Adverse Outcome Pathways and Biological Networks.

Minju Na1, Dongsup Kim1

  • 1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.

Chemical Research in Toxicology
|June 16, 2026
PubMed
Summary
This summary is machine-generated.

AOP Network Box integrates diverse data to map chemical exposures to adverse outcomes. This tool aids in discovering new links between chemicals, biological molecules, and events for better understanding toxicity mechanisms.

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Related Experiment Videos

Last Updated: Jun 17, 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

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

  • Toxicology
  • Computational Biology
  • Bioinformatics

Background:

  • Adverse outcome pathways (AOPs) model mechanisms linking chemical exposures to adverse outcomes via molecular initiating events (MIEs) and key events (KEs).
  • Existing data gaps hinder comprehensive understanding of AOPs, including chemical associations and biological molecule-event relationships.

Purpose of the Study:

  • To develop a novel network-based platform, AOP Network Box, for integrating diverse biological and chemical data.
  • To identify AOPs associated with specific chemicals and uncover novel relationships among chemicals, biological molecules, and events.

Main Methods:

  • Developed AOP Network Box, a multilayered network integrating chemical-protein interaction (CPI), protein-protein interaction (PPI), Gene Ontology (GO), and AOP network data.
  • Leveraged integrated network data to identify AOPs and discover unknown associations.

Main Results:

  • The AOP Network Box successfully integrates multiple data types, creating a comprehensive resource for AOP research.
  • The platform facilitates the identification of chemicals linked to known AOPs and reveals previously undiscovered relationships between chemicals, biological molecules, and events.
  • Enabled deeper insights into the mechanistic basis of chemical-induced adverse outcomes.

Conclusions:

  • AOP Network Box provides a powerful, unified framework for exploring AOPs and understanding chemical toxicity mechanisms.
  • The integrated network approach enhances the discovery of novel chemical-biological relationships, advancing toxicological research.
  • The freely accessible web platform supports broader scientific inquiry into adverse outcome pathways.