Toward a Conceptual Multiscale Framework for Predictive Radiobiology: Integrating Genomic Damage, Network Rewiring,...

Area of Science:

• Radiobiology
• Systems Biology
• Genomics

Background:

• Traditional radiobiology focused on DNA damage and linear dose-response, but radiation effects are context-dependent and involve more than genomic alterations.
• Low-dose and chronic radiation exposures induce complex regulatory processes beyond direct mutational burden.
• Radiation responses involve molecular damage, tissue remodeling, and organism-level outcomes.

Purpose of the Study:

• Propose a conceptual multiscale framework for predictive radiobiology.
• Integrate genomic damage, post-transcriptional regulation, network rewiring, and tissue microenvironmental interactions.
• Define "predictive radiobiology" as the integrative prediction of radiation-induced outcomes.

Main Methods:

• Review of current understanding of radiation-induced signaling pathways.
• Integration of multi-omics, single-cell analysis, spatial biology, and organotypic models.
• Application of artificial intelligence and systems-level computational approaches.

Main Results:

• Radiation signaling extends beyond DNA double-strand breaks to include RNA-binding protein regulation, adaptive network responses, and extracellular matrix interactions.
• Radiation responses are governed by interconnected molecular and tissue-level processes.
• AI and computational approaches offer new ways to model non-linear radiation effects.

Conclusions:

• A multiscale framework is necessary for a comprehensive understanding of radiation biology.
• Integrative and multiscale approaches are crucial for improving mechanistic understanding and predictive modeling in radiobiology.
• Addressing limitations like data integration and translational gaps is essential for advancing the field.