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High-throughput Fluorometric Measurement of Potential Soil Extracellular Enzyme Activities

High-throughput Fluorometric Measurement of Potential Soil Extracellular Enzyme Activities

Published on: November 15, 2013

[Not Available].

Ryoji Yanashima¹, Noriyuki Kitagawa, Yoshiya Matsubara

¹Institute for Advanced Biosciences, Keio University Japan.

Frontiers in Neuroinformatics

|June 23, 2009

Summary

This study introduces a new network analysis method using k-shortest paths to better understand biological signaling pathways, especially in diseases like Alzheimer's. The approach identifies crucial pathways missed by traditional methods.

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Area of Science:

Systems Biology
Bioinformatics
Network Science

Background:

Scale-free and small-world network models are used to represent functional units in biological networks.
Traditional network analysis of signaling pathways often overlooks the impact of alternative routes and inactive proteins.

Purpose of the Study:

To develop and apply a novel network analysis method for signaling pathways.
To compare network properties of original and restricted signaling cascades using gene expression data.
To identify key pathways in late-onset Alzheimer's disease.

Main Methods:

Utilized Python modules for network analysis.
Compared original and restricted signaling cascades as directed graphs.
Implemented k-shortest paths and k-cycles analysis.

Keywords:

Alzheimer's disease graph theory hippocampal CA1 k-shortest path analysis network analysis network robustness python signal transduction

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Last Updated: Jun 22, 2026

High-throughput Fluorometric Measurement of Potential Soil Extracellular Enzyme Activities

High-throughput Fluorometric Measurement of Potential Soil Extracellular Enzyme Activities

Published on: November 15, 2013

Analyzed microarray gene expression profiles from Alzheimer's disease patients.

Main Results:

No significant differences were found using traditional models when inactive proteins were removed.
The k-shortest paths and k-cycles method identified relevant pathways, including feedback and feedforward loops.
This new technique yielded results consistent with in vivo findings and revealed pathways missed by shortest path or degree analysis.

Conclusions:

The developed module offers a comprehensive approach to analyze biomolecular networks.
The k-shortest paths method enhances the identification of functional pathways in disease contexts.
This approach provides a more robust analysis of signaling cascades compared to traditional methods.