Effects of differing underlying assumptions in in silico models on predictions of DNA damage and repair
Authors
Warmenhoven , John WHenthorn, Nicholas T
McNamara, Aimee L
Ingram, Samuel P
Merchant, Michael J
Kirkby, Karen J
Schuemann, J
Paganetti, H
Prise, KM
McMahon, S J
Affiliation
The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom.Issue Date
2023
Metadata
Show full item recordAbstract
The induction and repair of DNA double-strand breaks (DSBs) are critical factors in the treatment of cancer by radiotherapy. To investigate the relationship between incident radiation and cell death through DSB induction many in silico models have been developed. These models produce and use custom formats of data, specific to the investigative aims of the researchers, and often focus on particular pairings of damage and repair models. In this work we use a standard format for reporting DNA damage to evaluate combinations of different, independently developed, models. We demonstrate the capacity of such inter-comparison to determine the sensitivity of models to both known and implicit assumptions. Specifically, we report on the impact of differences in assumptions regarding patterns of DNA damage induction on predicted initial DSB yield, and the subsequent effects this has on derived DNA repair models. The observed differences highlight the importance of considering initial DNA damage on the scale of nanometres rather than micrometres. We show that the differences in DNA damage models result in subsequent repair models assuming significantly different rates of random DSB end diffusion to compensate. This in turn leads to disagreement on the mechanisms responsible for different biological endpoints, particularly when different damage and repair models are combined, demonstrating the importance of inter-model comparisons to explore underlying model assumptions.Citation
Warmenhoven JW, Henthorn NT, McNamara AL, Ingram SP, Merchant MJ, Kirkby KJ, et al. Effects of Differing Underlying Assumptions in In Silico Models on Predictions of DNA Damage and Repair. Radiation research. 2023 Dec 1;200(6):509-22. PubMed PMID: 38014593. Epub 2023/11/28. eng.Journal
Radiation ResearchDOI
10.1667/rade-21-00147.1PubMed ID
38014593Additional Links
https://dx.doi.org/10.1667/rade-21-00147.1Type
ArticleLanguage
enae974a485f413a2113503eed53cd6c53
10.1667/rade-21-00147.1