Network analysis to understand gene regulation: across spatial scales and toward multi-level models

Area of Science:

• Systems Biology
• Bioinformatics
• Genomics

Background:

• Network theory is applied to biological systems, using spatially embedded networks (e.g., chromatin, tissue) and abstract interaction networks (e.g., protein interactomes, drug targets).
• Current network models in epigenomics often lack integration and realism, hindering a comprehensive understanding of complex biological processes such as gene regulation.

Purpose of the Study:

• To review recent network theory applications in epigenomics.
• To advocate for expanded network formalisms for more integrated and realistic biological models.
• To highlight the utility of network formalisms in cancer research.

Main Methods:

• Review of current network theory approaches in epigenomics.
• Discussion of the need for integrating different network types (intracellular and tissue).
• Exploration of extending network models beyond transcriptional regulation to multiomics.

Main Results:

• Current network models are often siloed, limiting their ability to represent complex biological systems.
• Integrating intracellular chromatin networks with tissue context is crucial for understanding cell interactions.
• Expanding network formalisms to encompass multiomics data is essential for a holistic view of biological regulation.

Conclusions:

• Advancing network theory in biology requires more comprehensive and integrated modeling approaches.
• Future models must incorporate multi-level data (e.g., chromatin, tissue, multiomics) for greater realism.
• Network formalisms offer significant potential for applications in cancer research and understanding complex biological processes.