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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 Networks

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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.
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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.

Genome Size and the Evolution of New Genes

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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.

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Updated: Jun 12, 2026

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

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

Published on: October 19, 2021

A novel gene network inference algorithm using predictive minimum description length approach.

Vijender Chaitankar¹, Preetam Ghosh, Edward J Perkins

¹School of Computing, University of Southern Mississippi, MS 39402, USA. vchaitan@orca.st.usm.edu

BMC Systems Biology

|June 5, 2010

Summary

This summary is machine-generated.

This study introduces a new algorithm for gene regulatory network inference using mutual information and the predictive minimum description length (PMDL) principle. The algorithm improves precision by automatically determining optimal thresholds, eliminating the need for manual parameter tuning.

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Novel Sequence Discovery by Subtractive Genomics

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Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Gene regulatory network (GRN) inference is crucial for understanding cellular mechanisms.
Information theory models offer a computationally efficient approach for large-scale GRN inference.
A key challenge is determining appropriate thresholds for regulatory relationships, often requiring difficult parameter tuning.

Purpose of the Study:

To develop a novel algorithm for GRN inference from DNA microarray data.
To address the limitation of user-specified fine-tuning parameters in existing information theory models.
To improve the accuracy and reliability of GRN inference.

Main Methods:

Incorporation of mutual information (MI) and conditional mutual information (CMI) to identify gene-gene regulatory links.
Application of the predictive minimum description length (PMDL) principle to automatically determine optimal MI thresholds.
Evaluation using synthetic and yeast Saccharomyces cerevisiae time-series microarray data.

Main Results:

The proposed algorithm significantly improved precision in GRN inference compared to existing methods.
Fewer false regulatory edges were identified, enhancing network accuracy.
Performance was analyzed across varying data sizes, revealing saturation at larger datasets.

Conclusions:

The developed algorithm effectively uses the PMDL principle to infer GRNs without a fine-tuning parameter.
The PMDL principle successfully determined MI thresholds, leading to improved GRN inference precision.
An optimal CMI threshold was identified through sensitivity analysis, and algorithm performance stabilizes with increased data size.