iDDN: determining trans-omics network structure and rewiring with integrative differential dependency networks

Area of Science:

• Systems biology
• Genomics
• Computational biology

Background:

• Gene networks drive disease progression, offering targets for therapeutic interventions.
• Differential network analysis identifies rewiring in regulatory structures under varying conditions.
• Existing tools often rely on incomplete single-omics data, limiting analysis of complex regulatory mechanisms.

Purpose of the Study:

• To introduce the integrative Differential Dependency Networks (iDDN) tool for robust multi-omics differential network inference.
• To extend the Differential Dependency Networks (DDN) framework with biologically informed designs for enhanced accuracy.
• To facilitate the identification of trans-omics regulatory circuitry and differentially wired molecules.

Main Methods:

• Developed the integrative DDN (iDDN) tool, extending the DDN framework.
• Incorporated biologically principled designs for multi-omics data integration.
• Performed comparative evaluations using realistic simulations and case studies.

Main Results:

• iDDN enables robust differential network inferences from multi-omics data.
• The tool accurately identifies study-specific, trans-omics regulatory circuitry.
• iDDN helps pinpoint networks of differentially wired molecules linked to phenotypic transitions.

Conclusions:

• iDDN provides a powerful approach for analyzing complex gene regulatory networks across multiple omics layers.
• The tool enhances the identification of molecular targets for disease intervention by revealing critical regulatory elements.
• iDDN facilitates a deeper understanding of disease mechanisms and progression pathways.