What question did this study set out to answer?

This research aims to propose a conceptual framework for understanding radiation responses by integrating various biological scales. It seeks to highlight the complexity of radiation-induced biological effects beyond genomic damage.

June 12, 2026Open Access

Toward a Conceptual Multiscale Framework for Predictive Radiobiology: Integrating Genomic Damage, Network Rewiring, and Tissue Microenvironment

Key Points

This research aims to propose a conceptual framework for understanding radiation responses by integrating various biological scales. It seeks to highlight the complexity of radiation-induced biological effects beyond genomic damage.
Narrative review of existing literature on radiation-induced biological responses.
Integration of concepts from multi-omics, single-cell analysis, and three-dimensional organotypic models.
Discussion of artificial intelligence and systems-level computational approaches in modeling radiation effects.
Identified that radiation responses are context-dependent and involve complex interactions beyond DNA damage.
Highlighted the roles of RNA-binding proteins and extracellular matrix in tissue remodeling and signaling.
Addressed challenges like data integration and reproducibility in applying these findings clinically.

Abstract

Radiation-induced biological responses emerge through complex interactions across multiple biological scales, ranging from molecular damage to tissue remodeling and organism-level outcomes. Although traditional radiobiology has primarily focused on DNA damage and linear dose–response relationships, increasing evidence suggests that radiation responses are highly context-dependent and cannot be fully explained by genomic alterations alone. In particular, low-dose and chronic radiation exposures often induce biological effects that involve dynamic regulatory processes beyond direct mutational burden. The narrative review proposes a conceptual multiscale framework for predictive radiobiology that integrates genomic damage, post-transcriptional regulation, network rewiring, and tissue microenvironmental interactions. Within this framework, “predictive radiobiology” refers to the integrative prediction of radiation-induced outcomes, including radiosensitivity, tissue remodeling, fibrosis progression, therapeutic response, and long-term carcinogenic risk. We discuss how radiation-induced signaling extends beyond DNA double-strand breaks to include RNA-binding protein-mediated regulation, adaptive network responses, and extracellular matrix-dependent cellular plasticity. Recent advances in multi-omics, single-cell analysis, spatial biology, and three-dimensional organotypic models have revealed that radiation responses are governed by interconnected molecular and tissue-level processes. Furthermore, artificial intelligence and systems-level computational approaches provide new opportunities for modeling non-linear and context-dependent radiation effects across biological scales. We further discuss current limitations, including data integration challenges, reproducibility issues, and the translational gap between experimental models and clinical applications. Collectively, this conceptual framework highlights the need for integrative and multiscale approaches to improve mechanistic understanding and predictive modeling in modern radiobiology.

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