Influence maximization in time bounded network identifies transcription factors regulating perturbed pathways.

Area of Science:

• * Computational Biology
• * Systems Biology
• * Bioinformatics

Background:

• * Understanding dynamic biological processes requires identifying perturbed pathways and their regulators in response to environmental changes.
• * Current time-series analysis methods struggle to simultaneously identify perturbed pathways and their regulators.
• * Existing methods often require separate tools for gene set analysis and pathway interpretation, and lack time-series specific considerations for gene-gene interactions.

Purpose of the Study:

• * To propose a novel time-series analysis method, TimeTP, for identifying transcription factors (TFs) that regulate pathway perturbation.
• * To narrow the focus to perturbed sub-pathways and leverage gene regulatory and protein-protein interaction networks to pinpoint TFs.
• * To develop a method that can simultaneously identify perturbed pathways and their regulators from time-series data.

Main Methods:

• * TimeTP identifies perturbed sub-pathways by tracking expression changes over time.
• * It maps the starting points of perturbed sub-pathways to biological networks.
• * The influence maximization technique is employed to determine the most influential TFs regulating these pathways.

Main Results:

• * TimeTP successfully identified significant sub-pathways and their regulators in a PIK3CA knock-in dataset.
• * The method pinpointed regulators relevant to the PIP3 signaling pathway.
• * Results were visually summarized using a TF-PATHWAY MAP IN TIME CLOCK.

Conclusions:

• * TimeTP offers a powerful approach for simultaneously identifying perturbed pathways and their regulators in time-series data.
• * The method effectively utilizes network information to pinpoint key transcription factors driving biological responses.
• * TimeTP provides a valuable tool for dissecting dynamic biological processes and signaling pathways.