Abstract
We present a nanodosimetric model for predicting the yield of double strand breaks (DSBs) and non-DSB clustered damages induced in irradiated DNA. The model uses experimental ionization cluster size distributions measured in a gas model by an ion counting nanodosimeter or, alternatively, distributions simulated by a Monte Carlo track structure code developed to simulate the nanodosimeter. The model is based on a straightforward combinatorial approach translating ionizations, as measured or simulated in a sensitive gas volume, to lesions in a DNA segment of one-two helical turns considered equivalent to the sensitive volume of the nanodosimeter. The two model parameters, corresponding to the probability that a single ion detected by the nanodosimeter corresponds to a single strand break or a single lesion (strand break or base damage) in the equivalent DNA segment, were tuned by fitting the model-predicted yields to previously measured double-strand break and double-strand lesion yields in plasmid DNA irradiated with protons and helium nuclei. Model predictions were also compared to both yield data simulated by the PARTRAC code for protons of a wide range of different energies and experimental DSB and non-DSB clustered DNA damage yield data from the literature. The applicability and limitations of this model in predicting the LET dependence of clustered DNA damage yields are discussed. © 2010 Institute of Physics and Engineering in Medicine.
| Original language | English |
|---|---|
| Pages (from-to) | 761-781 |
| Number of pages | 21 |
| Journal | Physics in Medicine and Biology |
| Volume | 55 |
| Issue number | 3 |
| DOIs | |
| State | Published - Feb 7 2010 |
ASJC Scopus Subject Areas
- Radiological and Ultrasound Technology
- Radiology Nuclear Medicine and imaging
Keywords
- Radiometry/instrumentation
- Reproducibility of Results
- DNA Damage/radiation effects
- Helium/adverse effects
- Probability
- DNA/radiation effects
- Plasmids/radiation effects
- Algorithms
- DNA Breaks, Double-Stranded/radiation effects
- Nanotechnology/instrumentation
- Protons/adverse effects
- Computer Simulation
- Models, Genetic
- Software
- Monte Carlo Method
- Saccharomyces cerevisiae
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