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

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.