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TimeXNet: identifying active gene sub-networks using time-course gene expression profiles.

Ashwini Patil, Kenta Nakai

BMC Systems Biology

|December 19, 2014

Summary

This summary is machine-generated.

TimeXNet identifies active gene sub-networks with temporal paths from gene expression data. This tool predicts novel regulators and pathways, outperforming existing methods in analyzing cellular responses.

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Area of Science:

Systems Biology
Computational Biology
Bioinformatics

Background:

Time-course gene expression profiles reveal cellular state changes and molecular pathways.
Integrating these profiles with molecular networks offers insights into dynamic cellular responses.
Limited tools exist for predicting single active gene sub-networks from time-course expression data.

Purpose of the Study:

To introduce TimeXNet, a novel computational tool for identifying active gene sub-networks with temporal paths.
To leverage time-course gene expression data within a weighted gene regulatory and protein-protein interaction network.

Main Methods:

TimeXNet employs a specialized network flow optimization approach.
It identifies probable paths connecting genes with significant expression changes across consecutive time intervals.
The method was evaluated using mouse innate immune response and yeast osmotic stress response data.

Main Results:

TimeXNet successfully predicted novel regulators and associated pathways in active gene sub-networks.
It identified up to 50% more novel regulators compared to similar methods.
The tool predicted longer pathway overlaps (up to 7 edges) and demonstrated robustness against network noise.

Conclusions:

TimeXNet is a reliable tool for studying time-dependent cellular responses to stimuli.
It outperforms existing tools in identifying time-dependent active gene sub-networks.
The software is freely available and integrates with Cytoscape for visualization.