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Related Experiment Video

Updated: Sep 19, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

Robust signalling entropy estimation for biological process characterisation.

Ana Stolnicu¹, Nensi Ikonomi¹, Peter Eckhardt-Bellmann¹

¹Institute of Medical Systems Biology, Ulm University, 89069 Ulm, Germany.

Briefings in Bioinformatics

|June 18, 2025

Summary

This summary is machine-generated.

Signalling entropy quantifies cellular pathway uncertainty. This study reveals how protein network topology and data correction methods impact entropy calculations, crucial for understanding biological complexity and disease.

Keywords:

correction methods false-positive interactions protein interaction networks signalling entropy

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Last Updated: Sep 19, 2025

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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

Area of Science:

Systems biology
Computational biology
Bioinformatics

Background:

Signalling entropy measures uncertainty in cellular signalling pathways, reflecting protein interaction complexity.
It offers insights into cell fate, drug resistance, and disease progression.
Accurate quantification relies on integrating expression data with protein interaction networks, which can be compromised by experimental noise.

Purpose of the Study:

To investigate the impact of protein interaction network topology on signalling entropy calculations.
To systematically evaluate various data correction strategies for improving entropy estimation.
To identify optimal approaches for different data types and biological contexts.

Main Methods:

Analysis of signalling entropy using distinct protein interaction network topologies.
Systematic evaluation of different data correction strategies.
Assessment of the influence of network structure and correction methods on entropy values.

Main Results:

Different protein interaction network topologies significantly alter signalling entropy calculations.
Various data correction strategies show distinct benefits and drawbacks.
Identification of the most effective correction approaches for specific data types and biological scenarios.

Conclusions:

Understanding the influence of network topology and correction methods is vital for reliable signalling entropy estimation.
Optimized entropy calculations enhance comprehension of biological processes and disease mechanisms.
This work provides a protocol to improve the reliability of signalling entropy analysis.