Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Konstantinos Chatzipapas
 + 3973 word(s) 3973 2021-05-28 05:05:53 |
2 format correct
Bruce Ren
 -21 word(s) 3952 2021-06-11 11:19:50 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Chatzipapas, K. Ionizing Radiation and Complex DDR. Encyclopedia. Available online: https://encyclopedia.pub/entry/10773 (accessed on 18 November 2024).
Chatzipapas K. Ionizing Radiation and Complex DDR. Encyclopedia. Available at: https://encyclopedia.pub/entry/10773. Accessed November 18, 2024.
Chatzipapas, Konstantinos. "Ionizing Radiation and Complex DDR" Encyclopedia, https://encyclopedia.pub/entry/10773 (accessed November 18, 2024).
Chatzipapas, K. (2021, June 11). Ionizing Radiation and Complex DDR. In Encyclopedia. https://encyclopedia.pub/entry/10773
Chatzipapas, Konstantinos. "Ionizing Radiation and Complex DDR." Encyclopedia. Web. 11 June, 2021.
Ionizing Radiation and Complex DDR
Edit
This entry is adapted from the peer-reviewed paper 10.3390/cancers12040799

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 30 years, a lot of effort into improving the knowledge of radiation biology. 

radiobiology nanoscale simulations Monte Carlo method ionizing radiation complex DNA damage biological response

1. Introduction

Ionizing radiation (IR) is used in several medical procedures either for therapeutic and/or for imaging/diagnostic procedures. When used for therapeutic purposes, e.g., to effectively treat cancer, the main goal is to successfully irradiate malignant cells while ensuring healthy tissues absorb the lowest possible dose. Following this technique, malignant cells are killed while healthy cells remain healthy, avoiding possible genetic aberrations or cell death [1][2][3][4]. To achieve this objective, it is essential to accurately predict the consequences following IR interaction with biological matter. Accidental, natural, and occupational exposure to IR must not been overlooked because of their possible great biological impact.
The scientific community has invested a lot of effort in understanding the response mechanisms initiated and generally involved in IR-induced DNA damage. Particularly, the exact radiochemical mechanisms that produce single strand breaks (SSBs), double strand breaks (DSBs), and clustered/complex DNA damage (CDD) which can consist of multiple DSBs and/or closely spaced (within 10–20 base pairs) non-DSB lesions, such as oxidized bases and abasic sites [5][6], as well as the DNA damage response (DDR) and repair pathways activated through the whole procedure have been investigated. The great variation of pathways that actively participate in ‘response-to-radiation’ prompts extensive investigation. It has to be mentioned that the aforementioned processes could lead to oncogenic transformations, but the achievement of wider knowledge will guide the scientific community to the biological optimization of radiation therapy for cancer treatment [7][8][9][10][11].
The present review article aims to improve the understanding of the field of IR-DDR in general by providing an overview of Monte Carlo simulation studies that try to explain several aspects of cancer treatment using IR. Some mathematical studies on the investigation of DDR have also been included.
Since the early ‘90s, the scientific community has been investigating the interaction mechanisms of IR on cells and DNA. Figure 1 depicts the growth of the number of published scientific studies in the field of radiobiology using Monte Carlo techniques. A large increase in the number of studies is observed from 2010, coinciding with the great evolution in computer science and advanced computational tools for Monte Carlo simulations on the molecular and cellular level. According to Scopus (www.scopus.com), the relevant documents in “DNA damage” and “Monte Carlo simulations” total almost 380 over the last 30 years (Figure 1), showing the emerging field of computational radiobiology. This review aims to summarize the studies that were performed on the DNA modelling for Monte Carlo simulations, on the DDR, and on the DNA repair mechanisms.
Cancers 12 00799 g001 550
Figure 1. Number of published articles, over the last 30 years, on “Monte Carlo simulations” for the investigation of “DNA damage”.

2. An Overview of the Methods Used in Nanoscale Simulations

Monte Carlo techniques take advantage of random number generators and computers to simulate complex systems that are not easy to approach in an analytical way. Monte Carlo methods originate from innovative work by Ulam, von Neumann, Metropolis and Fermi in the 1930s and 1940s [12][13], and are described nicely in Andrieu et al. [14]. At later stages, MC techniques were used by Eckhardt [15] and since then they have been applied in many disciplines. They are used in board games, weather forecasting, electrical and telecommunication engineering, quantum physics, and computational biology [16]. In medical physics, they are used to simulate a range of medical applications that exploit IR, for imaging purposes or for dosimetry applications on the macro- and nano-scale [17].
We should mention the fact that to be able to have absolutely comparable information on the output of each MC code, data shall be given on alternative approaches, definitely including particle types and energies for which the code is applicable, underlying cross sections, assumptions on radiochemistry, whether indirect damage is included at all, and how DNA and chromatin structures are modelled. At present, information provided on distinct codes is of a very heterogeneous nature. For instance, simulations on the effects of gold nanoparticles are mentioned with Geant4-DNA only, but these have been performed with other codes as well, cf. the recent review by Li et al Phys Med 2020 [18].
Pioneers to MC simulations of DNA energy deposition and strand breaks were Paretzke H.G., Goodhead D.T., Nikjoo H. and Tomita H. In 1989, Nikjoo et al. [19] published a study (based on [20]) where they used virtual cylindrical geometrical models to imitate biological models of DNA segments, nucleosomes, and chromatin fibers. To determine the energy deposition in a target volume by ultra-soft X-rays they used the already published, in 1988, mathematical model for track structure analysis by Goodhead et al. [21]. Tomita et al. [22] went a step further and used monoenergetic electrons and new DNA structure models (one turn of double-strand DNA, nucleosome, solenoid) to study physical and chemical stages of the radiation interaction. They studied both direct and indirect effects of radiation on the DNA.

2.1. Particle Track Structure Codes

Monte Carlo track structure (MCTS) codes are widely used for simulating the transportation of ionizing particles in biological matter at small scales (nm-μm). Thus, they offer a valuable theoretical tool for mechanistic radiation effect studies, especially in quantifying DNA damage under different radiation qualities, which still presents a major challenge in radiobiology research [23]. This is not possible with most general-purpose MC radiation transport codes such as MCNP [24], EGS [25], FLUKA [26], PENELOPE [27], and PHITS [28], which are widely used for dosimetry at the tissue and organ level (~mm-cm scale). The limitation of these codes stems from the use of so-called condensed-history physics models [29]. The essence of these models is the grouping of a large number of interactions along artificial steps, which limits their resolution to about 10–100 μm (at best), and prohibits their application at low energies (below about 1–10 keV) [30].
To reach a spatial resolution at the DNA level, it is necessary to adopt a more detailed description of particle transport, as shown in Figure 2. This is achieved by simulating all elastic and inelastic collisions one-by-one, until all particles slow down to thermalization energies or, in practice, the ionizing threshold of the medium (which is in around 10 eV for tissue-like materials). The aim is to generate a three-dimensional map of the radiation track based on the spatial distribution of energy-transfer points in the medium [31]. This functionality has provided a unique theoretical tool for investigating differences between sparse (or low linear energy transfer (LET)) and dense (or high-LET) ionization radiations at the nanometer level, which is not possible by other means [32]. MCTS codes have been the workhorse of theoretical microdosimetry, enabling systematic calculations of lineal energy spectra (the stochastic analog of LET), proximity functions, ionization cluster distributions, etc., which are used for explaining and predicting the quality factor or the relative biological effectiveness of different ionizing radiations [33][34][35][36][37][38][39]. In addition, quantitative estimates of the early “direct” DNA damage can be obtained by combining the spatial distribution of energy-transfer points (above a certain threshold) with the geometric structure of DNA [40][41][42][43][44][45]. The main drawback of MCTS codes is that they are computer intensive and require much more detailed physics models than currently available ones [46]. In particular, track-structure models for low-energy transport must strongly rely on theory since experimental data are scarce, and they are highly material-specific which limits their application range [47]. Thus, MCTS simulations are usually application-specific and are developed for a single media, most commonly water.
Cancers 12 00799 g002 550
Figure 2. Particle transport example. (a) Track structure of an electron (10 KeV) in water as simulated in Geant4-DNA. The red line, which starts from the bottom right edge, is the route followed by the primary electron, while in yellow the interactions with the water medium are presented. The red branches that are separated from the main route represent secondary electrons. (b) Schematic representation of the way that energy is deposited on DNA molecules. This is a stylish representation, which has the aim of helping the reader understand the way that the superposition of energy deposition in water is transformed to single or double strand breaks. The blue edges imitate DNA bases.
MCTS codes for water have been used for over 40 years in radiation biophysics, and more than a dozen such codes have been reported in the literature [23], with the most widely used being NOREC [48], PARTRAC [20], KURBUC [49], and RITRACKS [50]. Although most of the early MCTS codes were limited to electrons, nowadays most MCTS codes can also simulate more heavily charged particles like protons, alphas, and some ions. NOREC (NIST-modified Oak Ridge Electron Code) is an upgraded version of the OREC code developed in the ‘70s at the Oak Ridge National Laboratory (ORNL) by Ritchie and co-workers [51]. OREC was the first MCTS code to implement a realistic physics model of the liquid-phase of water based on an empirical dielectric response function deduced from optical measurements at ORNL [52]. This major achievement allowed the calculation of ionization and excitation cross sections for condensed-phase water from first principles. OREC was also one of the first MCTS codes to extend the simulations to the chemical stage with a detailed radiolysis model that was extensively benchmarked against experimental data [53]. In the early 2000s, following an upgrade of the elastic scattering model based on NIST’s ELAST database, OREC was renamed to NOREC [48]. NOREC can simulate full slowing-down electron tracks over the energy range from 7.4 eV to 1 MeV. PARTRAC (PARticle TRACks) is an evolution of the MOCA (MOnte-CArlo) code developed in the 1970s by Paretzke and co-workers at the National Research Center for Environment and Health (GSF) of Germany. MOCA was one of the first MCTS codes for electron transport in biological matter represented by unit-density gaseous water [20]. PARTRAC has evolved to a comprehensive MCTS code that extends to the chemical and biological stage, including multi-scale models for the structure of the whole genomic DNA with atomic resolution, which are supplemented with DNA damage and repair models [54]. It has also adopted more realistic physics models for the biological medium based on the empirical dielectric response function of liquid water developed by Dingfelder and co-workers [55]. PARTRAC can transport electrons (10 eV–10 MeV), protons (1 keV–1 GeV), alpha-particles (1 keV–1 GeV), and light ions (from <1 MeV/u up to 1 GeV/u) [56], and it can also take into account water radiolysis, DNA repair and chromosomal aberrations [57][58][59]. KURBUC (Kyushu University and Radiation Biophysics Unit Code) was initially developed at the Medical Research Council (MRC) of UK in the early 1990s by Nikjoo and co-workers using mostly empirical and parameterized physics models for electron transport in unit-density gaseous water mediums in the energy range from 10 eV to 10 MeV [60]. It was later extended to protons (1 keV–1 MeV), alpha particles (1 keV/u–2 MeV/u), neutrons (thermal to 100 MeV), and carbon ions (1 keV/u–10 MeV/u). In its latest version, KURBUCliq [49], a more realistic physics model for biological matter, has been implemented using the dielectric response function developed by Emfietzoglou, Cucinotta, and Nikjoo (known as the ECN model) [61] which is based on the latest experimental data for liquid water. KURBUCliq can also perform simulations of the chemical and biological stage including sophisticated mathematical models of DNA damage and repair pathways along with atomistic DNA structure models [62][63][64]. RITRACKS (Relativistic Ion TRACKS) is a NASA-funded MCTS code that extends the transport of ionizing particles in unit-density gaseous water mediums up to the very highest energies (100 MeV for electrons and 10 GeV/u for ions), which are of interest to space radiation protection. Molecular cross sections specific to the DNA bases (in the gas phase) are also available towards a more realistic approximation of direct ionizations in DNA. A distinct feature is the implementation of time-dependent track effects in order to simulate dose-rate effects. It also includes modeling of water radiolysis and simple DNA structures for radiation damage studies [65]. Recently, the RITRACKS code has been used and the results advocate for the hypothesis that not only a single particle may induce many non-DSB clustered damages, but also that there may be a large number of them in a chromatin fiber [66]. The TRAX code performs simulations for track-structure applications in various media [67]. MC4 performs simulations down to very low energies, including models for gaseous and liquid water, and is known for its upgraded models for the liquid phase [68][69]. PHITS has been developed to simulate the track structure of electrons in liquid water in a wide incident energy spectrum from 1 meV to 1 MeV [70].
The aforementioned MCTS codes are not publicly available. Therefore, in recent years, there have been efforts to implement track-structure models into some general-purpose MC codes to enable simulations at both the macro and microscopic scale. The most notable examples are the Monte Carlo N-Particle version 6 (MCNP6) [71], PENELOPE (modification to PENELOPE/penEasy, resulting in the LionTrack code) [72][73][74], and the Geant4-DNA package of Geant4 [75][76][77]. Apart from MCNP6, which uses a simple interpolation of high-energy atomic models down to the eV energy range, thus neglecting molecular aggregation and condensed-phase effects, the Geant4-DNA and modified PENELOPE/penEasy extensions are based on elaborate physics models specifically developed for liquid water. The code PENELOPE is known for its electron models and extends accurately to low energies, having applications to micro- and nano-dosimetry [78][79], however, the low-energy modification of the PENELOPE code is not publicly distributed. Therefore, since 2007, Geant4 (release 9.1) is the only open access general-purpose radiation transport MC code offering, through its Geant4-DNA extension, track-structure functionalities in liquid water down to the eV energy range for radiobiological applications at the cellular, sub-cellular, and DNA level [75][76]. Due to the prominent role of low-energy electrons (called “track-ends”) in radiation-induced DNA damage, a distinct feature of Geant4-DNA is the availability of different physics models for the interaction of low-energy electrons with liquid water [39][80][81][82]. These models, which adopt either the dielectric response function approach or other hybrid approaches, undergo continuous development and refinement. Recently, track-structure models for gold nanoparticles have also been developed and will be incorporated in the upcoming Geant4-DNA releases [83][84][85]. It also includes modelling of the chemical stage [86] and offers various representations of DNA target structures to be used in DNA damage studies [87][88][89]. Geant4-DNA can transport electrons (7.4 eV–1 MeV), protons (100 eV–100 MeV), alpha particles (1 keV–400 MeV), and ions (0.5 MeV/u–106 MeV/u). It is noteworthy that the TOPAS-nBio software [90], which extends the TOPAS MC code [91] to the (sub) cellular and DNA scale for radiobiological studies [92], is based on the Geant4-DNA package.
The aforementioned MCTS codes have great potential to expand the knowledge of mechanisms that are involved in the response of biological matter to IR, but they also have limitations. The limitations vary from the lack of experimental information on the processes involved, which are also under research, up to the broad uncertainty of used parameters or the large fluctuations of parameters that are exploited to describe cell sensitivity. Another obstacle is that not all MCTS codes are benchmarked, especially with respect to nanodosimetric calculations. MCTS codes are very sensitive to the physics cross sections that are used for particle transportation as they are the core of the computation. These cross sections vary depending on how they are produced (analytically, extrapolation, or experimentally) and therefore they introduce a wide uncertainty to the simulation of track structures. This undoubtedly influences the results of MCTS simulations, e.g., the number of interactions within a certain volume, which is very critical when it comes to nanodosimetry (somewhat less for microdosimety). Thus, there is an urgent need for benchmarking MCTS codes with some efforts being done recently [93]. The computational time of the simulation for a population of cells is also a big concern. MCTS codes are also not capable enough to reliably simulate the very low energy secondary electrons below about 10 eV, which are considered as potential lethal sources for the cell since they can induce harmful clustered lesions in the DNA [94][95].

2.2. Monte Carlo Techniques for Radiobiological Modelling

Currently, there are two methods used to estimate DNA damage. The first method is to estimate potential DSBs by superimposing DNA geometry to the radiation track structure [42][44][96][97][98]. The other method is to use clustering algorithms based on probabilistic models to estimate DSB yield [99][100][101]. The first method is more direct way to estimate DNA damage accurately, but it is also a time-consuming procedure. The second method can also calculate with reasonable accuracy the DNA DSB, and it reduces the computational time. 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].
The RADAMOL [108][109] toolbox includes the simulation of chemical and physico-chemical stages produced when ionizing particles interact with water. To simulate scavengeable and unscavengeable DNA damage, it also includes RADACK and DIRADACK. PARTRAC [55] is focused on radiation biology and can simulate both physics and chemistry interactions produced by the irradiation of biological materials. PARTRAC also includes mathematical models of biology to study the procedure of recombination of DNA segments after irradiation [110] and has integrated information on DNA and chromatin structure over multiple levels and the susceptibility of DNA damage, as well as enzymatic reactions that aim to restore the integrity of the cellular DNA. PARTRAC, in its follow-up chemistry component, can account for inter-track effects [111], a feature that Geant4-DNA also implanted in its code. Geant4-DNA has been often compared with PARTRAC, for validation purposes [112][113], while PARTRAC publications influenced the design of Geant4-DNA code.
A recent addition on simulating radiation chemistry and track structures at the subcellular scale is the MPEXS-DNA, GPU-based Monte Carlo simulator [114]. MPEXS-DNA uses the speed of GPU processing for the physical, physicochemical, chemical and biological stages of the simulation process of IR interaction with biological matter. It is based on the Geant4-DNA package and can also simulate chemical reactions and the diffusion process for molecular species, and the distribution of molecular species can also be determined. In this study [114], the authors confirmed that the simulation results, which were obtained using MPEXS-DNA, were consistent with existing experimental and simulation data. Compared with simulations that were performed in Geant4-DNA with a single CPU core, MPEXSDNA performed the same simulations thousands times faster, keeping the accuracy at the same level.
The Monte Carlo Damage Simulation (MCDS) [102][103] algorithm, as a cell-level model, is a quasi-phenomenological concept used to simulate DNA damage yields (SSBs, DSBs and sites of multiple base damage, average number of lesions per DNA damage cluster, as well as cluster length in base pairs). It is a fast algorithm compared to common track-structure simulations (execution time ranges from seconds to some minutes on a typical PC). It utilizes monoenergetic electrons, protons, α-particles and other charged particles with atomic numbers up to Z = 26 (i.e., 56Fe), with kinetic energies up to a few GeV [102][103][104]. It also has the ability to simulate damage induction for random mixtures of charged particles. For photons and neutrons, MCDS can provide the distribution of secondary charged particles produced via the interaction of neutral particles with a target region of interest [115]. Considering the spectrum of energy used for the particles involved in MCDS, the minimum allowed kinetic energy limit depends on the particle type: e.g., for electrons, damage induction can be simulated from some tens of eV, while for bigger particles the corresponding limit increases with increasing atomic number. This code can also simulate the effects of oxygen on the induction of clustered DNA lesions. Because of the fact that MCDS provides simulations tacitly for both direct and indirect DNA damage mechanisms, it uses an extraneous free radical scavenger (DMSO) and thus imitates diminutions in the total amount of strand breaks and base damages because of the scavenger’s presence [102]. It must be mentioned at this point that MCDS simulates the so called “initial” levels of DNA damage induced and not the processing or repair.

2.3. DNA Modelling

An important parameter to simulate DDR is to model DNA molecules. There are two ways to model DNA molecules. One way is to use simulation techniques and the other one is to use geometry design techniques. There are also fixed DNA geometries that can be downloaded by the Proteins Data Bank (PDB) [116]. PDB (http://www.rcsb.org/) is a big database that contains high detailed DNA molecules, but it does not contain every DNA molecule. In Geant4-DNA, there is an example (PDB4DNA [117]) that includes a model by this database. In Figure 3, two different DNA molecules that were provided by the PDB database are presented in several styles. The stylish representation has been used to make the reader understand the geometry of the DNA molecule. The “simulation” representation is the one used by the MC simulation algorithms and they represent each different ingredient of the DNA molecule with spheres of different colors. Bases are shown in blue while the backbone is shown in grey and red. The spheres are the space where energy is deposited. The first one is a simple and small molecule (Figure 3a,b) (linear, with 20 base pairs length), while the second one (Figure 3c,d) is a more complex DNA molecule (5000 base pairs). Simulation software usually exploits the geometry shown in Figure 3b,d to calculate energy deposition.
Cancers 12 00799 g003 550
Figure 3. 3D view of two different DNA molecules by the Proteins Data Bank (PDB) database. Scale bars have been added accordingly. Both scale bars represent a distance of 30 nm. (a) Simple small DNA, stylish view, (b) simple small DNA, “simulation” view, (c) complex DNA, stylish view, (d) complex DNA, “simulation” view.
A DNA simulation tool is MacroMoleculeBuilder (MMB) [118], which has been implemented using the SimTK simulation toolkit [119]. SimTK provides the user with several simulation tools, including Simbody (https://simtk.org/projects/simbody/) and Molmodel (https://simtk.org/projects/molmodel). These algorithms allow the implementation of specified joint constrains using coordinates, which allow the user to control the flexibility of the generated molecule. MMB can simulate the interactions between bases, which are exploited to produce the geometry of any combination of base pairs. An analytical classification of base pairs can be found in the Leontis–Stombaugh–Westhof catalog [120]. The catalog contains several combinations of force and torque that tend to produce an attachment frame around the first residue’s base, with a body frame on the second residue’s base.
Furthermore, Howell et al. proposed a simulation algorithm for the modelling of B-DNA [121]. They exploited coarse-grained (CG) simulations based on the bead-rod model reported by Wang et al. [122] that originated in the wormlike chain model [123]. In this model, the DNA molecules are designed using N beads connected by N−1 inextensible rods of length l, with a total length L. The final model has a length of L = (N−1)l. For energy compensation, the model employs a bending penalty between adjacent beads and an excluded volume repulsion between beads. This algorithm can generate several different structures where the only common thing is their ingredients. This model can only support homogeneous DNA bending and cannot be extended to more complex configurations that include DNA kinking and stretching [124].
Cumberworth et al. [125] introduced several sampling methods that create a model which can simulate computationally feasible DNA origami self-assembly. The model can contain structural parameters relevant to the process of construction. There are several different methods, using different sets of parameters, to produce similar designs. For large-scale systems, the results can be highly different as there is a connection between the variables of the origami design.
The above-described DNA simulation models have not yet been used in MC simulations. Recently, DnaFabric [126] has been presented and used in Geant4-DNA. The purpose of DnaFabric is to generate, edit, display, and export complex DNA geometrical models. It is based on a hierarchical geometry description designed to adapt to optimization techniques and adaptable level of detail (LODs).

References

  1. Cornforth, M.; Bedford, J. A quantitative comparison of potentially lethal damage repair and the rejoining of interphase chromosome breaks in low passage normal human fibroblasts. Radiat. Res. 1987, 111, 385–405.
  2. Bedford, J. Sublethal damage, potentially lethal damage, and chromosomal aberrations in mammalian cells exposed to ionizing radiations. Int. J. Radiat. Oncol. Biol. Phys. 1991, 21, 1457–1469.
  3. Hlatky, L.; Sachs, R.; Vazquez, M.; Cornforth, M. Radiation-induced chromosome aberrations: Insights gained from biophysical modeling. Bioessays 2002, 24, 714–723.
  4. Hall, E.J.; Giaccia, A.J. Radiobiology for the Radiobiologist; Lippincott Williams & Wilkins: Philadelfia, PA, USA, 2006.
  5. Georgakilas, A.G.; O’Neill, P.; Stewart, R.D. Induction and Repair of Clustered DNA Lesions: What Do We Know So Far? Radiat. Res. 2013, 180, 100–109.
  6. Nikitaki, Z.; Hellweg, C.E.; Georgakilas, A.G.; Ravanat, J.-L. Stress-induced DNA damage biomarkers: Applications and limitations. Front. Chem. 2015, 3, 35.
  7. Vitti, E.T.; Parsons, J.L. The Radiobiological Effects of Proton Beam Therapy: Impact on DNA Damage and Repair. Cancers (Basel) 2019, 11, 946.
  8. Obe, G.; Pfeiffer, P.; Savage, J.R.K.; Johannes, C.; Goedecke, W.; Jeppesen, P.; Natarajan, A.T.; Martı́nez-López, W.; Folle, G.A.; Drets, M.E. Chromosomal aberrations: Formation, identification and distribution. Mutat. Res. /Fundam. Mol. Mech. Mutagenesis 2002, 504, 17–36.
  9. Rothkamm, K.; Lobrich, M. Misrepair of radiation-induced DNA double-strand breaks and its relevance for tumorigenesis and cancer treatment (review). Int. J. Oncol. 2002, 21, 433–440.
  10. Worku, M.; Fersht, N.; Martindale, C.; Funes, J.M.; Short, S.C. Sequential Transformation of Mesenchymal Stem Cells is Associated with Increased Radiosensitivity and Reduced DNA Repair Capacity. Radiat. Res. 2013, 179, 698–706.
  11. Byrne, M.; Wray, J.; Reinert, B.; Wu, Y.; Nickoloff, J.; Lee, S.-H.; Hromas, R.; Williamson, E. Mechanisms of oncogenic chromosomal translocations. Ann. N. Y. Acad. Sci. 2014, 1310, 89–97.
  12. Metropolis, N.; Ulam, S. The Monte Carlo method. J. Am. Stat. Assoc. 1949, 44, 335–341.
  13. Metropolis, N.; Rosenbluth, A.W.; Rosenbluth, M.N.; Teller, A.H.; Teller, E. Equations of state calculations by fast computing machines. J. Chem. Phys. 1953, 21, 1087–1091.
  14. Andrieu, C.; de Freitas, N.; Doucet, A.; Jordan, M.I. An Introduction to MCMC for Machine Learning. Mach. Learn. 2003, 50, 5–43.
  15. Eckhardt, R. Stan Ulam, John Von Neumann, and the Monte Carlo method. Los Alamos Sci. Spec. Issue 1987, 131–136.
  16. Bouzy, B.; Helmstetter, B. Advances in Computer Games 10: Many Games, Many Challenges; al, H.v.d.H.e., Ed.; Kluwer: Boston, MA, USA, 2003; pp. 159–174.
  17. El Naqa, I.; Pater, P.; Seuntjens, J. Monte Carlo role in radiobiological modelling of radiotherapy outcomes. Phys. Med. Biol. 2012, 57, R75–R97.
  18. Li, W.B.; Belchior, A.; Beuve, M.; Chen, Y.Z.; Di Maria, S.; Friedland, W.; Gervais, B.; Heide, B.; Hocine, N.; Ipatov, A.; et al. Intercomparison of dose enhancement ratio and secondary electron spectra for gold nanoparticles irradiated by X-rays calculated using multiple Monte Carlo simulation codes. Phys. Med. 2020, 69, 147–163.
  19. Nikjoo, H.; Goodhead, D.T.; Charlton, D.E.; Paretzke, H.G. Energy deposition in small cylindrical targets by ultrasoft X-rays. Phys. Med. Biol. 1989, 34, 691–705.
  20. Paretzke, H. Ch 3: Radiation track structure theory. In Kinetics of Non-homogeneous Processes; Freeman, G.R., Ed.; Wiley: New York, NY, USA, 1987; pp. 89–170.
  21. Goodhead, D.T.; Nikjoo, H. Track Structure Analysis of Ultrasoft X-rays Compared to High- and Low-LET Radiations. Int. J. Radiat. Biol. 1989, 55, 513–529.
  22. Tomita, H.; Kai, M.; Kusama, T.; Aoki, Y.; Ito, A. Monte Carlo simulation of DNA strand breaks induced by monoenergetic electrons using higher-order structure models of DNA. Int. J. Radiat. Biol. 1994, 66, 669–682.
  23. Nikjoo, H.; Uehara, S.; Emfietzoglou, D.; Cucinotta, F.A. Track-structure codes in radiation research. Radiat. Meas. 2006, 41, 1052–1074.
  24. MCNP: A General Monte Carlo code for Neutron and Photon Transport Version 3A Revision 2; LA--7396-M-Rev2; Los Alamos National Lab, N. M.: New Mexico, NM, USA, 1986; p. 591.
  25. Kawrakow, I.; Mainegra-Hing, E.; Rogers, D.; Tessier, F.; Walters, P. The EGSnrc Code System: Monte Carlo Simulation of Electron and Photon Transport; NRCC PIRS-701: Ottawa, ON, Canada, 2006.
  26. Ferrari, A.; Sala, P.R.; Fasso, A.; Ranft, J. FLUKA: A Multi-Particle Transport Code; SLAC-R-773; TRN: US0601448 United States 10.2172/877507. Available online: (accessed on 22 March 2020).
  27. Baró, J.; Sempau, J.; Fernández-Varea, J.M.; Salvat, F. PENELOPE: An algorithm for Monte Carlo simulation of the penetration and energy loss of electrons and positrons in matter. Nucl. Instrum. Methods Phys. Res. Sect. B: Beam Interact. Mater. At. 1995, 100, 31–46.
  28. Sato, T.; Matsuda, N.; Hashimoto, S.; Iwamoto, Y.; Noda, S.; Nakashima, H.; Fukahori, T.; Chiba, S.; Niita, K.; Iwase, H.; et al. Overview of the PHITS code and its application to medical physics. Prog. Nucl. Sci. Technol. 2014, 4, 879–882.
  29. Nahum, A.E. Condensed-history Monte-Carlo simulation for charged particles: What can it do for us? Radiat. Environ. Biophys. 1999, 38, 163–173.
  30. Nikjoo, H.; Emfietzoglou, D.; Liamsuwan, T.; Taleei, R.; Liljequist, D.; Uehara, S. Radiation track, DNA damage and response—a review. Rep. Prog. Phys. 2016, 79, 116601.
  31. Dingfelder, M. Track-structure simulations for charged particles. Health Phys 2012, 103, 590–595.
  32. Booz, J.; Braby, L.; Coyne, J.; Kliauga, P.; Lindborg, L.; Menzel, H.-G.; Parmentier, N. Parmentier, Report 36, Journal of the International Commission on Radiation Units and Measurements, Volume os19, Issue 1, December 31, 1983. Available online: (accessed on 26 March 2020).
  33. Report 40, Journal of the International Commission on Radiation Units and Measurements, Volume os21, Issue 1, April 4, 1986, Page NP. Available online: (accessed on 26 March 2020).
  34. Lazarakis, P.; Incerti, S.; Ivanchenko, V.; Kyriakou, I.; Emfietzoglou, D.; Corde, S.; Rosenfeld, A.B.; Lerch, M.; Tehei, M.; Guatelli, S. Investigation of track structure and condensed history physics models for applications in radiation dosimetry on a micro and nano scale in Geant4. Biomed. Phys. Eng. Express 2018, 4, 024001.
  35. Kyriakou, I.; Emfietzoglou, D.; Ivanchenko, V.; Bordage, M.C.; Guatelli, S.; Lazarakis, P.; Tran, H.N.; Incerti, S. Microdosimetry of electrons in liquid water using the low-energy models of Geant4. J. Appl. Phys. 2017, 122, 024303.
  36. Emfietzoglou, D.; Papamichael, G.; Nikjoo, H. Monte Carlo Electron Track Structure Calculations in Liquid Water Using a New Model Dielectric Response Function. Radiat. Res. 2017, 188, 355–368.
  37. Famulari, G.; Pater, P.; Enger, S.A. Microdosimetry calculations for monoenergetic electrons using Geant4-DNA combined with a weighted track sampling algorithm. Phys. Med. Biol. 2017, 62, 5495–5508.
  38. Incerti, S.; Kyriakou, I.; Bordage, M.C.; Guatelli, S.; Ivanchenko, V.; Emfietzoglou, D. Track structure simulations of proximity functions in liquid water using the Geant4-DNA toolkit. J. Appl. Phys. 2019, 125, 104301.
  39. Kyriakou, I.; Ivanchenko, V.; Sakata, D.; Bordage, M.C.; Guatelli, S.; Incerti, S.; Emfietzoglou, D. Influence of track structure and condensed history physics models of Geant4 to nanoscale electron transport in liquid water. Phys. Med. Eur. J. Med. Phys. 2019, 58, 149–154.
  40. Friedland, W.; Schmitt, E.; Kundrát, P.; Dingfelder, M.; Baiocco, G.; Barbieri, S.; Ottolenghi, A. Comprehensive track-structure based evaluation of DNA damage by light ions from radiotherapy-relevant energies down to stopping. Sci. Rep. 2017, 7, 45161.
  41. Watanabe, R.; Rahmanian, S.; Nikjoo, H. Spectrum of Radiation-Induced Clustered Non-DSB Damage—A Monte Carlo Track Structure Modeling and Calculations. Radiat. Res. 2015, 183, 525–540.
  42. Chatzipapas, K.P.; Papadimitroulas, P.; Obeidat, M.; McConnell, K.A.; Kirby, N.; Loudos, G.; Papanikolaou, N.; Kagadis, G.C. Quantification of DNA double-strand breaks using Geant4-DNA. Med. Phys. 2019, 46, 405–413.
  43. Schuemann, J.; McNamara, A.L.; Warmenhoven, J.W.; Henthorn, N.T.; Kirkby, K.J.; Merchant, M.J.; Ingram, S.; Paganetti, H.; Held, K.D.; Ramos-Mendez, J.; et al. A New Standard DNA Damage (SDD) Data Format. Radiat. Res. 2019, 191, 76–92.
  44. Pater, P.; Seuntjens, J.; El Naqa, I.; Bernal, M.A. On the consistency of Monte Carlo track structure DNA damage simulations. Med. Phys. 2014, 41, 121708.
  45. Liu, W.; Tan, Z.; Zhang, L.; Champion, C. Investigation on the correlation between energy deposition and clustered DNA damage induced by low-energy electrons. Radiat. Environ. Biophys. 2018, 57, 179–187.
  46. Emfietzoglou, D.; Nikjoo, H. Accurate Electron Inelastic Cross Sections and Stopping Powers for Liquid Water over the 0.1-10 keV Range Based on an Improved Dielectric Description of the Bethe Surface. Radiat. Res. 2007, 167, 110–120.
  47. Emfietzoglou, D.; Nikjoo, H. The Effect of Model Approximations on Single-Collision Distributions of Low-Energy Electrons in Liquid Water. Radiat. Res. 2005, 163, 98–111.
  48. Semenenko, V.A.; Turner, J.E.; Borak, T.B. NOREC, a Monte Carlo code for simulating electron tracks in liquid water. Radiat. Environ. Biophys. 2003, 42, 213–217.
  49. Liamsuwan, T.; Emfietzoglou, D.; Uehara, S.; Nikjoo, H. Microdosimetry of low-energy electrons. Int. J. Radiat. Biol. 2012, 88, 899–907.
  50. Plante, I.; Cucinotta, F.A. Ionization and excitation cross sections for the interaction of HZE particles in liquid water and application to Monte Carlo simulation of radiation tracks. New J. Phys. 2008, 10, 125020.
  51. Ritchie, R.H.; Hamm, R.N.; Turner, J.E.; Wright, H.A.; Bolch, W.E. Radiation Interactions and Energy Transport in the Condensed Phase. In Physical and Chemical Mechanisms in Molecular Radiation Biology; Glass, W.A., Varma, M.N., Eds.; Springer US: Boston, MA, USA, 1991; pp. 99–135.
  52. Dingfelder, M.; Ritchie, R.H.; Turner, J.E.; Friedland, W.; Paretzke, H.G.; Hamm, R.N. Comparisons of calculations with PARTRAC and NOREC: Transport of electrons in liquid water. Radiat. Res. 2008, 169, 584–594.
  53. Aydogan, B.; Marshall, D.T.; Swarts, S.G.; Turner, J.E.; Boone, A.J.; Richards, N.G.; Bolch, W.E. Site-Specific OH Attack to the Sugar Moiety of DNA: A Comparison of Experimental Data and Computational Simulation. Radiat. Res. 2002, 157, 38–44.
  54. Friedland, W.; Paretzke, H.G.; Ballarini, F.; Ottolenghi, A.; Kreth, G.; Cremer, C. First steps towards systems radiation biology studies concerned with DNA and chromosome structure within living cells. Radiat. Environ. Biophys. 2008, 47, 49–61.
  55. Friedland, W.; Dingfelder, M.; Kundrát, P.; Jacob, P. Track structures, DNA targets and radiation effects in the biophysical Monte Carlo simulation code PARTRAC. Mutat. Res. /Fundam. Mol. Mech. Mutagenesis 2011, 711, 28–40.
  56. Schmitt, E.; Friedland, W.; Kundrát, P.; Dingfelder, M.; Ottolenghi, A. Cross-section scaling for track structure simulations of low-energy ions in liquid water. Radiat. Prot. Dosim. 2015, 166, 15–18.
  57. Dingfelder, M.; Travia, A.; McLawhorn, R.A.; Shinpaugh, J.L.; Toburen, L.H. Electron Emission from Foils and Biological Materials after Proton Impact. Radiat Phys. Chem. Oxf. Engl. 1993 2008, 77, 1213–1217.
  58. Friedland, W.; Jacob, P.; Kundrát, P. Stochastic Simulation of DNA Double-Strand Break Repair by Non-homologous End Joining Based on Track Structure Calculations. Radiat. Res. 2010, 173, 677–688.
  59. Friedland, W.; Kundrát, P. Track structure based modelling of chromosome aberrations after photon and alpha-particle irradiation. Mutat. Res. /Genet. Toxicol. Environ. Mutagenesis 2013, 756, 213–223.
  60. Uehara, S.; Nikjoo, H.; Goodhead, D.T. Cross-sections for water vapour for the Monte Carlo electron track structure code from 10 eV to the MeV region. Phys. Med. Biol. 1993, 38, 1841–1858.
  61. Emfietzoglou, D.; Cucinotta, F.A.; Nikjoo, H. A Complete Dielectric Response Model for Liquid Water: A Solution of the Bethe Ridge Problem. Radiat. Res. 2005, 164, 202–211.
  62. Taleei, R.; Nikjoo, H. Repair of the double-strand breaks induced by low energy electrons: A modelling approach. Int. J. Radiat. Biol. 2012, 88, 948–953.
  63. Taleei, R.; Nikjoo, H. The Non-homologous End-Joining (NHEJ) Pathway for the Repair of DNA Double-Strand Breaks: I. A Mathematical Model. Radiat. Res. 2013, 179, 530–539.
  64. Rahmanian, S.; Taleei, R.; Nikjoo, H. Radiation induced base excision repair (BER): A mechanistic mathematical approach. DNA Repair 2014, 22, 89–103.
  65. Plante, I.; Ponomarev, A.L.; Cucinotta, F.A. Calculation of the energy deposition in nanovolumes by protons and HZE particles: Geometric patterns of initial distributions of DNA repair foci. Phys. Med. Biol. 2013, 58, 6393–6405.
  66. Nikitaki, Z.; Nikolov, V.; Mavragani, I.V.; Plante, I.; Emfietzoglou, D.; Iliakis, G.; Georgakilas, A.G. Non-DSB clustered DNA lesions. Does theory colocalize with the experiment? Radiat. Phys. Chem. 2016, 128, 26–35.
  67. Wälzlein, C.; Scifoni, E.; Krämer, M.; Durante, M. Simulations of dose enhancement for heavy atom nanoparticles irradiated by protons. Phys. Med. Biol. 2014, 59, 1441–1458.
  68. Emfietzoglou, D.; Papamichael, G.; Kostarelos, K.; Moscovitch, M. A Monte Carlo track structure code for electrons (~10 eV-10 keV) and protons (~0.3-10 MeV) in water: Partitioning of energy and collision events. Phys. Med. Biol. 2000, 45, 3171–3194.
  69. Emfietzoglou, D.; Papamichael, G.; Karava, K.; Androulidakis, I.; Pathak, A.; Phillips, G.W.; Moscovitch, M.; Kostarelos, K. A Monte-Carlo code for the detailed simulation of electron and light-ion tracks in condensed matter. Radiat. Prot. Dosim. 2006, 119, 491–496.
  70. Matsuya, Y.; Kai, T.; Yoshii, Y.; Yachi, Y.; Naijo, S.; Date, H.; Sato, T. Modeling of yield estimation for DNA strand breaks based on Monte Carlo simulations of electron track structure in liquid water. J. Appl. Phys. 2019, 126, 124701.
  71. Goorley, T.; James, M.; Booth, T.; Brown, F.; Bull, J.; Cox, L.J.; Durkee, J.; Elson, J.; Fensin, M.; Forster, R.A.; et al. Features of MCNP6. Ann. Nucl. Energy 2016, 87, 772–783.
  72. Fernández-Varea, J.M.; González-Muñoz, G.; Galassi, M.E.; Wiklund, K.; Lind, B.K.; Ahnesjö, A.; Tilly, N. Limitations (and merits) of PENELOPE as a track-structure code. Int. J. Radiat. Biol. 2012, 88, 66–70.
  73. Bäckström, G.; Galassi, M.E.; Tilly, N.; Ahnesjö, A.; Fernández-Varea, J.M. Track structure of protons and other light ions in liquid water: Applications of the LIonTrack code at the nanometer scale. Med. Phys. 2013, 40, 064101.
  74. Villegas, F.; Tilly, N.; Ahnesjö, A. Monte Carlo calculated microdosimetric spread for cell nucleus-sized targets exposed to brachytherapy125I and192Ir sources and60Co cell irradiation. Phys. Med. Biol. 2013, 58, 6149–6162.
  75. Bernal, M.A.; Bordage, M.C.; Brown, J.M.C.; Davídková, M.; Delage, E.; El Bitar, Z.; Enger, S.A.; Francis, Z.; Guatelli, S.; Ivanchenko, V.N.; et al. Track structure modeling in liquid water: A review of the Geant4-DNA very low energy extension of the Geant4 Monte Carlo simulation toolkit. Phys. Med. 2015, 31, 861–874.
  76. Incerti, S.; Kyriakou, I.; Bernal, M.A.; Bordage, M.C.; Francis, Z.; Guatelli, S.; Ivanchenko, V.; Karamitros, M.; Lampe, N.; Lee, S.B.; et al. Geant4-DNA example applications for track structure simulations in liquid water: A report from the Geant4-DNA Project. Med. Phys. 2018, 45, e722–e739.
  77. Agostinelli, S.; Allison, J.; Amako, K.; Apostolakis, J.; Araujo, H.; Arce, P.; Asai, M.; Axen, D.; Banerjee, S.; Barrand, G.; et al. Geant4—a simulation toolkit. Nucl. Instrum. Methods Phys. Res. Sect. A: Accel. Spectrometers Detect. Assoc. Equip. 2003, 506, 250–303.
  78. Bernal, M.A.; Liendo, J.A. An investigation on the capabilities of the PENELOPE MC code in nanodosimetry. Med. Phys. 2009, 36, 620–625.
  79. Villegas, F.; Bäckström, G.; Tilly, N.; Ahnesjö, A. Energy deposition clustering as a functional radiation quality descriptor for modeling relative biological effectiveness. Med. Phys. 2016, 43, 6322–6335.
  80. Incerti, S.; Ivanchenko, A.; Karamitros, M.; Mantero, A.; Moretto, P.; Tran, H.N.; Mascialino, B.; Champion, C.; Ivanchenko, V.N.; Bernal, M.A.; et al. Comparison of GEANT4 very low energy cross section models with experimental data in water. Med. Phys. 2010, 37, 4692–4708.
  81. Kyriakou, I.; Sefl, M.; Nourry, V.; Incerti, S. The impact of new Geant4-DNA cross section models on electron track structure simulations in liquid water. J. Appl. Phys. 2016, 119, 194902.
  82. Bordage, M.C.; Bordes, J.; Edel, S.; Terrissol, M.; Franceries, X.; Bardies, M.; Lampe, N. Implementation of new physics models for low energy electrons inImplementation of new physics models for low energy electrons in liquid water in Geant4-DNA. Phys. Med. 2016, 32, 1833–1840.
  83. Sakata, D.; Kyriakou, I.; Okada, S.; Tran, H.N.; Lampe, N.; Guatelli, S.; Bordage, M.-C.; Ivanchenko, V.; Murakami, K.; Sasaki, T.; et al. Geant4-DNA track-structure simulations for gold nanoparticles: The importance of electron discrete models in nanometer volumes. Med. Phys. 2018, 45, 2230–2242.
  84. Sakata, D.; Incerti, S.; Bordage, M.C.; Lampe, N.; Okada, S.; Emfietzoglou, D.; Kyriakou, I.; Murakami, K.; Sasaki, T.; Tran, H.; et al. An implementation of discrete electron transport models for gold in the Geant4 simulation toolkit. J. Appl. Phys. 2016, 120, 244901.
  85. Sakata, D.; Kyriakou, I.; Tran, H.N.; Bordage, M.-C.; Rosenfeld, A.; Ivanchenko, V.; Incerti, S.; Emfietzoglou, D.; Guatelli, S. Electron track structure simulations in a gold nanoparticle using Geant4-DNA. Phys. Med. 2019, 63, 98–104.
  86. Shin, W.-G.; Ramos-Mendez, J.; Faddegon, B.; Tran, H.N.; Villagrasa, C.; Perrot, Y.; Okada, S.; Karamitros, M.; Emfietzoglou, D.; Kyriakou, I.; et al. Evaluation of the influence of physical and chemical parameters on water radiolysis simulations under MeV electron irradiation using Geant4-DNA. J. Appl. Phys. 2019, 126, 114301.
  87. Meylan, S.; Incerti, S.; Karamitros, M.; Tang, N.; Bueno, M.; Clairand, I.; Villagrasa, C. Simulation of early DNA damage after the irradiation of a fibroblast cell nucleus using Geant4-DNA. Sci. Rep. 2017, 7, 11923.
  88. Sakata, D.; Lampe, N.; Karamitros, M.; Kyriakou, I.; Belov, O.; Bernal, M.A.; Bolst, D.; Bordage, M.-C.; Breton, V.; Brown, J.M.C.; et al. Evaluation of early radiation DNA damage in a fractal cell nucleus model using Geant4-DNA. Phys. Med. 2019, 62, 152–157.
  89. Tang, N.; Bueno, M.; Meylan, S.; Incerti, S.; Tran, H.N.; Vaurijoux, A.; Gruel, G.; Villagrasa, C. Influence of chromatin compaction on simulated early radiation-induced DNA damage using Geant4-DNA. Med. Phys. 2019, 46, 1501–1511.
  90. Schuemann, J.; McNamara, A.L.; Ramos-Méndez, J.; Perl, J.; Held, K.D.; Paganetti, H.; Incerti, S.; Faddegon, B. TOPAS-nBio: An Extension to the TOPAS Simulation Toolkit for Cellular and Sub-cellular Radiobiology. Radiat. Res. 2019, 191, 125–138.
  91. Perl, J.; Shin, J.; Schumann, J.; Faddegon, B.; Paganetti, H. TOPAS: An innovative proton Monte Carlo platform for research and clinical applications. Med. Phys. 2012, 39, 6818–6837.
  92. McNamara, A.; Geng, C.; Turner, R.; Mendez, J.R.; Perl, J.; Held, K.; Faddegon, B.; Paganetti, H.; Schuemann, J. Validation of the radiobiology toolkit TOPAS-nBio in simple DNA geometries. Phys. Med. : Pm : Int. J. Devoted Appl. Phys. Med. Biol. Off. J. Ital. Assoc. Biomed. Phys. (Aifb) 2017, 33, 207–215.
  93. Villagrasa, C.; Bordage, M.-C.; Bueno, M.; Bug, M.; Chiriotti, S.; Gargioni, E.; Heide, B.; Nettelbeck, H.; Parisi, A.; Rabus, H. assessing the contribution of cross-Sections to the uncertainty Of monte carlo calculations in micro- And nanodosimetry. Radiat. Prot. Dosim. 2018, 183, 11–16.
  94. Zheng, Y.; Sanche, L. Clustered DNA Damages induced by 0.5 to 30 eV Electrons. Int. J. Mol. Sci. 2019, 20, 3749.
  95. Rezaee, M.; Hunting, D.J.; Sanche, L. Correlation between energy deposition and molecular damage from Auger electrons: A case study of ultra-low energy (5–18 eV) electron interactions with DNA. Med. Phys. 2014, 41, 072502.
  96. Elsässer, T.; Scholz, M. Cluster Effects within the Local Effect Model. Radiat. Res. 2007, 167, 319–329.
  97. Abolfath, R.M.; Carlson, D.J.; Chen, Z.J.; Nath, R. A molecular dynamics simulation of DNA damage induction by ionizing radiation. Phys. Med. Biol. 2013, 58, 7143–7157.
  98. Liang, Y.; Yang, G.; Liu, F.; Wang, Y. Monte Carlo simulation of ionizing radiation induced DNA strand breaks utilizing coarse grained high-order chromatin structures. Phys. Med. Biol. 2015, 61, 445–460.
  99. Liu, R.; Zhao, T.; Zhao, X.; Reynoso, F.J. Modeling gold nanoparticle radiosensitization using a clustering algorithm to quantitate DNA double-strand breaks with mixed-physics Monte Carlo simulation. Med. Phys. 2019, 46, 5314–5325.
  100. Francis, Z.; Villagrasa, C.; Clairand, I. Simulation of DNA damage clustering after proton irradiation using an adapted DBSCAN algorithm. Comput. Methods Programs Biomed. 2011, 101, 265–270.
  101. Dos Santos, M.; Villagrasa, C.; Clairand, I.; Incerti, S. Influence of the DNA density on the number of clustered damages created by protons of different energies. Nucl. Instrum. Methods Phys. Res. Sect. B: Beam Interact. Mater. At. 2013, 298, 47–54.
  102. Semenenko, V.A.; Stewart, R.D. A Fast Monte Carlo Algorithm to Simulate the Spectrum of DNA Damages Formed by Ionizing Radiation. Radiat. Res. 2004, 161, 451–457.
  103. Semenenko, V.A.; Stewart, R.D. Fast Monte Carlo simulation of DNA damage formed by electrons and light ions. Phys. Med. Biol. 2006, 51, 1693–1706.
  104. Stewart, R.; Yu, V.; Georgakilas, A.; Koumenis, C.; Park, J.; Carlson, D. Effects of radiation quality and oxygen on clustered DNA lesions and cell death. Radiat. Res. 2011, 176, 587–602.
  105. Liu, R.; Zhao, T.; Swat, M.H.; Reynoso, F.J.; Higley, K.A. Development of computational model for cell dose and DNA damage quantification of multicellular system. Int. J. Radiat. Biol. 2019, 95, 1484–1497.
  106. Lampe, N.; Karamitros, M.; Breton, V.; Brown, J.M.C.; Kyriakou, I.; Sakata, D.; Sarramia, D.; Incerti, S. Mechanistic DNA damage simulations in Geant4-DNA part 1: A parameter study in a simplified geometry. Phys. Med. Eur. J. Med. Phys. 2018, 48, 135–145.
  107. Lampe, N.; Karamitros, M.; Breton, V.; Brown, J.M.C.; Sakata, D.; Sarramia, D.; Incerti, S. Mechanistic DNA damage simulations in Geant4-DNA Part 2: Electron and proton damage in a bacterial cell. Phys. Med. Eur. J. Med. Phys. 2018, 48, 146–155.
  108. Štepán, V.; Davídková, M. Impact of oxygen concentration on yields of DNA damages caused by ionizing radiation. J. Phys. Conf. Ser. 2008, 101, 012015.
  109. Štěpán, V.; Davídková, M. RADAMOL tool: Role of radiation quality and charge transfer in damage distribution along DNA oligomer. Eur. Phys. J. D 2014, 68, 240.
  110. Alloni, D.; Campa, A.; Friedland, W.; Mariotti, L.; Ottolenghi, A. Track structure, radiation quality and initial radiobiological events: Considerations based on the PARTRAC code experience. Int. J. Radiat. Biol. 2012, 88, 77–86.
  111. Kreipl, M.S.; Friedland, W.; Paretzke, H.G. Interaction of ion tracks in spatial and temporal proximity. Radiat. Environ. Biophys. 2009, 48, 349.
  112. Incerti, S.; Champion, C.; Tran, H.N.; Karamitros, M.; Bernal, M.; Francis, Z.; Ivanchenko, V.; Mantero, A. Energy deposition in small-scale targets of liquid water using the very low energy electromagnetic physics processes of the Geant4 toolkit. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. At. 2013, 306, 158–164.
  113. Karamitros, M.; Incerti, S.; Mantero, A. Modeling radiation chemistry in the Geant4 toolkit. Prog. Nucl. Sci. Technol. 2011, 2, 503–508.
  114. Okada, S.; Murakami, K.; Incerti, S.; Amako, K.; Sasaki, T. MPEXS-DNA, a new GPU-based Monte Carlo simulator for track structures and radiation chemistry at subcellular scale. Med. Phys. 2019, 46, 1483–1500.
  115. Hsiao, Y.; Stewart, R.D. Monte Carlo simulation of DNA damage induction by x-rays and selected radioisotopes. Phys. Med. Biol. 2007, 53, 233–244.
  116. Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The Protein Data Bank. Nucleic Acids Res. 2000, 28, 235–242.
  117. Delage, E.; Pham, Q.T.; Karamitros, M.; Payno, H.; Stepan, V.; Incerti, S.; Maigne, L.; Perrot, Y. PDB4DNA: Implementation of DNA geometry from the Protein Data Bank (PDB) description for Geant4-DNA Monte-Carlo simulations. Comput. Phys. Commun. 2015, 192, 282–288.
  118. Flores, S.C.; Sherman, M.A.; Bruns, C.M.; Eastman, P.; Altman, R.B. Fast flexible modeling of RNA structure using internal coordinates. Ieee/Acm Trans Comput Biol Bioinform 2011, 8, 1247–1257.
  119. Sherman, M. Simbody Home Page. Available online: (accessed on 22 March 2020).
  120. Leontis, N.B.; Stombaugh, J.; Westhof, E. The non-Watson-Crick base pairs and their associated isostericity matrices. Nucleic Acids Res. 2002, 30, 3497–3531.
  121. Howell, S.C.; Qiu, X.; Curtis, J.E. Monte Carlo simulation algorithm for B-DNA. J. Comput. Chem. 2016, 37, 2553–2563.
  122. Wang, Y.; Tree, D.R.; Dorfman, K.D. Simulation of DNA Extension in Nanochannels. Macromolecules 2011, 44, 6594–6604.
  123. Wang, J.; Gao, H. A generalized bead-rod model for Brownian dynamics simulations of wormlike chains under strong confinement. J. Chem. Phys. 2005, 123, 084906.
  124. Peters, J.P., 3rd; Maher, L.J. DNA curvature and flexibility in vitro and in vivo. Q. Rev. Biophys. 2010, 43, 23–63.
  125. Cumberworth, A.; Reinhardt, A.; Frenkel, D. Lattice models and Monte Carlo methods for simulating DNA origami self-assembly. J. Chem. Phys. 2018, 149, 234905.
  126. Meylan, S.; Vimont, U.; Incerti, S.; Clairand, I.; Villagrasa, C. Geant4-DNA simulations using complex DNA geometries generated by the DnaFabric tool. Comput. Phys. Commun. 2016, 204, 159–169.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Oncology
Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Konstantinos Chatzipapas
View Times: 710
Revisions: 2 times (View History)
Update Date: 23 Jun 2021
Table of Contents
    1000/1000
    Hot Most Recent
    ScholarVision Creations
    Feedback