Mechanistic DNA damage simulations in Geant4-DNA Part 2: Electron and proton damage in a bacterial cell

Karamitros, Mathieu; Breton, Vincent; Brown, Jeremy M. C.; Sakata, D.; Sarramia, David; Incerti, S.

doi:10.1016/j.ejmp.2017.12.008

Cited by 68 publications

(95 citation statements)

References 57 publications

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“…An interesting future application field will be certainly related with similar studies performed with high-LET incident particles [36,37]. This preliminary work will be further extended with experimental data from other cancer cell lines irradiated at the CATANA beam line, in order to initiate a clinically relevant database and with the simulation and evaluation of cells damage at the DNA scale using computational approaches as reported in recent publications [38][39][40].…”

Section: Resultsmentioning

confidence: 93%

Radiobiological quantities in proton-therapy: Estimation and validation using Geant4-based Monte Carlo simulations

et al. 2019

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The Geant4 Monte Carlo simulation toolkit was used to reproduce radiobiological parameters measured by irradiating three different cancerous cell lines with monochromatic and clinical proton beams. Methods: The experimental setup adopted for irradiations was fully simulated with a dedicated open-source Geant4 application. Cells survival fractions was calculated coupling the Geant4 simulations with two analytical radiobiological models: one based on the LEM (Local Effect Model) approach and the other on a semi-empirical parameterisation. Results was evaluated and compared with experimental data. Results and conclusions: The results demonstrated the Geant4 ability to reproduce radiobiological quantities for different cell lines.

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Section: Resultsmentioning

confidence: 93%

Radiobiological quantities in proton-therapy: Estimation and validation using Geant4-based Monte Carlo simulations

et al. 2019

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“…Monte Carlo Damage Simulation (MCDS) [102][103][104] and DBSCAN [105] are two platforms that can be used for such a purpose. A study has already been done to compare DBSCAN with Geant4-DNA simulation results by Lampe et al [106,107].…”

Section: Monte Carlo Techniques For Radiobiological Modellingmentioning

confidence: 99%

Ionizing Radiation and Complex DNA Damage: Quantifying the Radiobiological Damage Using Monte Carlo Simulations

Chatzipapas

Papadimitroulas²,

Emfietzoglou

et al. 2020

Cancers

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Ionizing radiation is a common tool in medical procedures. Monte Carlo (MC) techniques are widely used when dosimetry is the matter of investigation. The scientific community has invested, over the last 20 years, a lot of effort into improving the knowledge of radiation biology. The present article aims to summarize the understanding of the field of DNA damage response (DDR) to ionizing radiation by providing an overview on MC simulation studies that try to explain several aspects of radiation biology. The need for accurate techniques for the quantification of DNA damage is crucial, as it becomes a clinical need to evaluate the outcome of various applications including both low- and high-energy radiation medical procedures. Understanding DNA repair processes would improve radiation therapy procedures. Monte Carlo simulations are a promising tool in radiobiology studies, as there are clear prospects for more advanced tools that could be used in multidisciplinary studies, in the fields of physics, medicine, biology and chemistry. Still, lot of effort is needed to evolve MC simulation tools and apply them in multiscale studies starting from small DNA segments and reaching a population of cells.

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“…In this study, we used 1 ns as the default value, which was also the choice in other works. 17,18 T c ¼ 2:5 ns, 19 10 ns 20 were also used previously. Thus, we reported the dependences of SSB and DSB yields on T c for five values, namely 1, 2.5, 5, 7.5, and 10 ns.…”

Section: B3 T C In the Chemical Stagementioning

confidence: 99%

A new open‐source GPU‐based microscopic Monte Carlo simulation tool for the calculations of DNA damages caused by ionizing radiation — Part II: sensitivity and uncertainty analysis

et al. 2020

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Purpose: Calculations of deoxyribonucleic acid (DNA) damages involve many parameters in the computation process. As these parameters are often subject to uncertainties, it is of central importance to comprehensively quantify their impacts on DNA single-strand break (SSB) and doublestrand break (DSB) yields. This has been a challenging task due to the required large number of simulations and the relatively low computational efficiency using CPU-based MC packages. In this study, we present comprehensive evaluations on sensitivities and uncertainties of DNA SSB and DSB yields on 12 parameters using our GPU-based MC tool, gMicroMC. Methods: We sampled one electron at a time in a water sphere containing a human lymphocyte nucleus and transport the electrons and generated radicals until 2 Gy dose was accumulated in the nucleus. We computed DNA damages caused by electron energy deposition events in the physical stage and the hydroxyl radicals at the end of the chemical stage. We repeated the computations by varying 12 parameters: (a) physics cross section, (b) cutoff energy for electron transport, (c)-(e) three branching ratios of hydroxyl radicals in the de-excitation of excited water molecules, (f) temporal length of the chemical stage, (g)-(h) reaction radii for direct and indirect damages, (i) threshold energy defining the threshold damage model to generate a physics damage, (j)-(k) minimum and maximum energy values defining the linear-probability damage model to generate a physics damage, and (l) probability to generate a damage by a radical. We quantified sensitivity of SSB and DSB yields with respect to these parameters for cases with 1.0 and 4.5 keV electrons. We further estimated uncertainty of SSB and DSB yields caused by uncertainties of these parameters. Results: Using a threshold of 10% uncertainty as a criterion, threshold energy in the threshold damage model, maximum energy in the linear-probability damage model, and probability for a radical to generate a damage were found to cause large uncertainties in both SSB and DSB yields. The scaling factor of the cross section, cutoff energy, physics reaction radius, and minimum energy in the linearprobability damage model were found to generate large uncertainties in DSB yields. Conclusions: We identified parameters that can generate large uncertainties in the calculations of SSB and DSB yields. Our study could serve as a guidance to reduce uncertainties of parameters and hence uncertainties of the simulation results.

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Mechanistic DNA damage simulations in Geant4-DNA Part 2: Electron and proton damage in a bacterial cell

Cited by 68 publications

References 57 publications

Radiobiological quantities in proton-therapy: Estimation and validation using Geant4-based Monte Carlo simulations

Radiobiological quantities in proton-therapy: Estimation and validation using Geant4-based Monte Carlo simulations

Ionizing Radiation and Complex DNA Damage: Quantifying the Radiobiological Damage Using Monte Carlo Simulations

A new open‐source GPU‐based microscopic Monte Carlo simulation tool for the calculations of DNA damages caused by ionizing radiation — Part II: sensitivity and uncertainty analysis

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