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Protein Networks

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 Interfaces

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...

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Updated: Jun 18, 2025

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

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Published on: October 19, 2021

Inferring Interaction Networks from Transcriptomic Data: Methods and Applications.

Vikram Singh¹, Vikram Singh²

¹Centre for Computational Biology and Bioinformatics, Central University of Himachal Pradesh, Dharamshala, Himachal Pradesh, India.

Methods in Molecular Biology (Clifton, N.J.)

|July 27, 2024

Summary

This summary is machine-generated.

This study explores methods for analyzing transcriptomic data to build gene regulatory and protein-protein interaction networks. These networks reveal complex biological mechanisms and identify key genes for further research.

Keywords:

Bayesian model Coexpression networks Gaussian model Gene regulatory networks Interologous networks Protein interaction networks Transcriptome

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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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Published on: March 5, 2022

Area of Science:

Molecular Biology
Systems Biology
Bioinformatics

Background:

Transcriptomic data offers a comprehensive view of gene expression dynamics.
Understanding gene expression is crucial for inferring biomolecular interactions and cellular processes.
Gene regulatory networks and protein-protein interaction networks are key to deciphering regulatory mechanisms.

Purpose of the Study:

To investigate methodologies for extracting insights from transcriptomic data.
To review approaches for constructing and analyzing biomolecular interaction networks.
To provide a framework for identifying key genes and functional modules within these networks.

Main Methods:

Association-based methods (correlation of expression vectors).
Probabilistic models (Bayesian and Gaussian models).
Interologous methods and network topology analysis.

Main Results:

Diverse methodologies can distill significant biological insights from transcriptomic data.
Network topology and functional analysis are crucial for evaluating interaction significance.
Strategies for identifying functional modules and prioritizing key genes were reviewed.

Conclusions:

Transcriptomic data analysis enables the construction of complex molecular networks.
Network-based techniques offer adaptable analyses across various biological domains.
This framework facilitates the uncovering of intricate regulatory mechanisms.