ARMADA: Using motif activity dynamics to infer gene regulatory networks from gene expression data

Area of Science:

• Systems Biology
• Computational Biology
• Genomics

Background:

• Gene expression data analysis is crucial for understanding genome-wide gene regulatory networks.
• High dimensionality of gene expression data poses challenges for traditional modeling and dimensionality reduction techniques.
• Existing methods often lack mechanistic interpretability.

Purpose of the Study:

• To extend the Motif Activity Response Analysis (MARA) methodology.
• To develop ARMADA for modeling regulator activity dynamics across time courses.
• To infer causal interactions driving these dynamics and provide mechanistic insights.

Main Methods:

• ARMADA models regulator activity dynamics over time.
• It infers causal interactions between regulators based on their activity dynamics.
• The method is implemented in the ISMARA webserver for accessible application.

Main Results:

• ARMADA successfully reproduces complex motif activity dynamics from gene expression time course data.
• It identifies key regulatory interactions driving observed dynamics, including known and novel interactions.
• Application to TNF-treated endothelial cells demonstrates its efficacy.

Conclusions:

• ARMADA offers a powerful approach for dissecting gene regulatory networks using time-course expression data.
• It facilitates hypothesis generation regarding regulator interactions in biological systems.
• The ISMARA webserver makes this advanced analysis accessible to researchers.