Kildea, J. | bioRxiv Science

bioRxiv2025

In Silico Neutron Relative Biological Effectiveness Estimations For Pre-DNA Repair And Post-DNA Repair Endpoints

A comprehensive understanding of the energy-dependent stochastic risks associated with neutron exposure is crucial to develop robust radioprotection systems. However, the scarcity of experimental data presents significant challenges in this domain. Track-structure Monte Carlo simulations with DNA models have demonstrated their potential to further our fundamental understanding of neutron-induced stochastic risks. To date, most track-structure Monte Carlo studies on the relative biological effectiveness (RBE) of neutrons have focused on various types of DNA damage clusters defined using base pair distances. In this study, we extend these methodologies by incorporating the simulation of non-homologous end joining (NHEJ) DNA repair in order to evaluate the RBE of neutrons for misrepairs. To achieve this, we adapted our previously published Monte Carlo DNA damage simulation pipeline, which combines condensed-history and track-structure Monte Carlo methods, to support the standard DNA damage (SDD) data format. This adaptation enabled seamless integration of neutron-induced DNA damage results with the DNA Mechanistic Repair Simulator (DaMaRiS) toolkit. Additionally, we developed a clustering algorithm that reproduces pre-repair endpoints studied in prior works, as well as novel damage clusters based on Euclidean distances. The neutron RBE for misrepairs obtained in this study exhibits a qualitatively similar shape as the RBE obtained for previously reported pre-repair endpoints. However, it peaks higher, reaching a maximum RBE value of 23(1) at a neutron energy of 0.5 MeV. Furthermore, double-strand break (DSB) pairs with a separation distance of 11 nm better matched the RBE for misrepairs than did the DSB clusters defined with base pair distances that were used in previous in silico neutron RBE studies. These results suggest that there are some features to the spatial distribution of neutron-induced DNA damage that are best characterized either by explicit repair mechanism simulations or by cluster analysis based on Euclidean distances.

biophysics↗

bioRxiv2025

RadiSeq: a single- and bulk-cell whole-genome DNA sequencing simulator for radiation-damaged cell models

ObjectiveTo build and validate a simulation framework to perform single-cell and bulk-cell whole genome sequencing simulation of radiation-exposed Monte Carlo cell models to assist radiation genomics studies. ApproachSequencing the genomes of radiation-damaged cells can provide useful insight into radiation action for radiobiology research. However, carrying out post-irradiation sequencing experiments can often be challenging, expensive, and time-consuming. Although computational simulations have the potential to provide solutions to these experimental challenges, and aid in designing optimal experiments, the absence of tools currently limits such application. Monte Carlo toolkits exist to simulate radiation exposures of cell models but there are no tools to simulate single- and bulk-cell sequencing of cell models containing radiation-damaged DNA. Therefore, we aimed to develop a Monte Carlo simulation framework to address this gap by designing a tool capable of simulating sequencing processes for radiation-damaged cells. Main ResultsWe developed RadiSeq - a multi-threaded whole-genome DNA sequencing simulator written in C++. RadiSeq can be used to simulate Illumina sequencing of radiation-damaged cell models produced by Monte Carlo simulations. RadiSeq has been validated through comparative analysis, where simulated data were matched against experimentally obtained data, demonstrating reasonable agreement between the two. Additionally, it comes with numerous features designed to closely resemble actual whole-genome sequencing. RadiSeq is also highly customizable with a single input parameter file. SignificanceRadiSeq enables the research community to perform complex simulations of radiation-exposed DNA sequencing, supporting the optimization, planning, and validation of costly and time-intensive radiation biology experiments. This framework provides a powerful tool for advancing radiation genomics research.

bioinformatics↗