Figure 7
Source publication
Computational simulations offer a powerful tool for quantitatively investigating radiation interactions with biological tissue and can help bridge the gap between physics, chemistry and biology. The TOPAS collaboration is tackling this challenge by extending the current Monte Carlo tool to allow for sub-cellular in silico simulations in a new exten...
Context in source publication
Context 1
... order to create a fractal globule with the same properties as that found in real DNA, a continuous 3D Hilbert space filling curve was used. A recursive function was used to generate the 3D Hilbert curve to fill a cube volume with a basic building block of an open cube formed with seven base components (cylinders) as shown in figure 7. The fractal pattern is made by recursively converting each line to a smaller version of the original starting pattern. ...
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Background
Monte Carlo particle simulation has become the primary tool for designing low‐energy miniature x‐ray tubes due to the difficulties of physically prototyping these devices and characterizing their radiation fields. Accurate simulation of electronic interactions within their targets is necessary for modeling both photon production and heat...
Purpose:
To provide Monte Carlo calculated beam quality correction factors (kQ) for monoenergetic proton beams using TOPAS, a toolkit based on the Monte Carlo code Geant4.
Materials and methods:
Monte Carlo simulations of six plane-parallel and four cylindrical ionization chambers were carried out. The latest ICRU 90 recommendations on key data...
A single crystal chemical vapor deposition (scCVD) diamond membrane-based microdosimetric system was used to perform simultaneous measurements of dose profile and microdosimetric spectra with the Y1 proton passive scattering beamline of the Center of Proton Therapy, Institute Curie in Orsay, France. To qualify the performance of the set-up in clini...
The TOPAS Monte Carlo (MC) system is used in radiation therapy and medical imaging research, having played a significant role in making Monte Carlo simulations widely available for proton therapy related research. While TOPAS provides detailed simulations of patient scale properties, the fundamental unit of the biological response to radiation is a...
Objective. A novel x-ray field produced by an ultrathin conical target is described in the literature. However, the optimal design for an associated collimator remains ambiguous. Current optimization methods using Monte Carlo calculations restrict the efficiency and robustness of the design process. A more generic optimization method that reduces p...
Citations
... Here, M TOPAS 25,26 is a software package for the Geant4 27,28 kernel of the All-Particle MC toolkit, which is a userfriendly tool for MC simulations in medical physics, eliminating the need for complex programming languages and offering great convenience. TOPAS-nBio 29,30 is an extension of the TOPAS toolkit, based on the Geant4-DNA kernel, 31 which allows an explicit modeling of the interactions of each particle. This extension provides a multiscale simulation capability, ranging from the macroscopic description of the radiation field to the microscopic description of biological outcomes. ...
Background
The current radiobiological model employed for boron neutron capture therapy (BNCT) treatment planning, which relies on microdosimetry, fails to provide an accurate representation the biological effects of BNCT. The precision in calculating the relative biological effectiveness (RBE) and compound biological effectiveness (CBE) plays a pivotal role in determining the therapeutic efficacy of BNCT. Therefore, this study focuses on how to improve the accuracy of the biological effects of BNCT.
Purpose
The purpose of this study is to propose new radiation biology models based on nanodosimetry to accurately assess RBE and CBE for BNCT.
Methods
Nanodosimetry, rooted in ionization cluster size distributions (ICSD), introduces a novel approach to characterize radiation quality by effectively delineating RBE through the ion track structure at the nanoscale. In the context of prior research, this study presents a computational model for the nanoscale assessment of RBE and CBE. We establish a simplified model of DNA chromatin fiber using the Monte Carlo code TOPAS‐nBio to evaluate the applicability of ICSD to BNCT and compute nanodosimetric parameters.
Results
Our investigation reveals that both homogeneous and heterogeneous nanodosimetric parameters, as well as the corresponding biological model coefficients α and β, along with RBE values, exhibit variations in response to varying intracellular ¹⁰B concentrations. Notably, the nanodosimetric parameter M1C2$M_1^{{{\mathrm{C}}}_2}$ effectively captures the fluctuations in model coefficients α and RBE.
Conclusion
Our model facilitates a nanoscale analysis of BNCT, enabling predictions of nanodosimetric quantities for secondary ions as well as RBE, CBE, and other essential biological metrics related to the distribution of boron. This contribution significantly enhances the precision of RBE calculations and holds substantial promise for future applications in treatment planning.
... Geant4-DNA has been designed for modeling of biological damage induced by ionizing radiation at the DNA scale. TOPAS-nBio has been carefully validated and evaluated in radiobiological studies simulating DNA damages and water radiolysis for gamma, proton and alpha particle irradiations [32][33][34][35][36][37][38][39][40][41][42]. ...
Aim
Over recent years, [²²⁵Ac]Ac-PSMA and [¹⁷⁷Lu]Lu-PSMA radiopharmaceutical therapy have evolved as a promising treatment option for advanced prostate cancer. Especially for alpha particle emitter treatments, there is still a need for improving dosimetry, which requires accurate values of relative biological effectiveness (RBE). To achieve that, consideration of DNA damages in the cell nucleus and knowledge of the energy deposition in the location of the DNA at the nanometer scale are required. Monte Carlo particle track structure simulations provide access to interactions at this level. The aim of this study was to estimate the RBE of ²²⁵Ac compared to ¹⁷⁷Lu. The initial damage distribution after radionuclide decay and the residual damage after DNA repair were considered.
Methods
This study employed the TOol for PArtcile Simulation (TOPAS) based on the Geant4 simulation toolkit. Simulation of the nuclear DNA and damage scoring were performed using the TOPAS-nBio extension of TOPAS. DNA repair was modeled utilizing the Python-based program MEDRAS (Mechanistic DNA Repair and Survival). Five different cell geometries of equal volume and two radionuclide internalization assumptions as well as two cell arrangement scenarios were investigated. The radionuclide activity (number of source points) was adopted based on SPECT images of patients undergoing the above-mentioned therapies.
Results
Based on the simulated dose–effect curves, the RBE of ²²⁵Ac compared to ¹⁷⁷Lu was determined in a wide range of absorbed doses to the nucleus. In the case of spherical geometry, 3D cell arrangement and full radionuclide internalization, the RBE based on the initial damage had a constant value of approximately 2.14. Accounting for damage repair resulted in RBE values ranging between 9.38 and 1.46 for ²²⁵Ac absorbed doses to the nucleus between 0 and 50 Gy, respectively.
Conclusion
In this work, the consideration of DNA repair of the damage from [²²⁵Ac]Ac-PSMA and [¹⁷⁷Lu]Lu-PSMA revealed a dose dependency of the RBE. Hence, this work suggested that DNA repair is an important aspect to understand response to different radiation qualities.
... 73 As has already been indicated, for those without Cþþ expertise, all these GEANT4-DNA capabilities for low energy physics, chemistry and radiobiology are accessible through the user-friendly TOPAS-nBio toolkit. [74][75][76][77] Figure 2(a) shows a TOPAS simulation of the simulated tracks of 50 keV electrons traversing a spherical cell model containing mitochondria and gold nanoparticles. Similar models have been used to investigate the nano-scale and radiobiological interactions underpinning dose enhancement with nano-particles Journal of Radiotherapy in Practice and more recently high dose-rate FLASH radiotherapy delivery, 78,79 both using TOPAS-nBio. ...
Introduction
This is the second of two papers giving an overview of the use of Monte-Carlo techniques for radiotherapy applications.
Methods
The first paper gave an introduction and introduced some of the codes that are available to the user wishing to model the different aspects of radiotherapy treatment. It also aims to serve as a useful companion to a curated collection of papers on Monte-Carlo that have been published in this journal.
Results and Conclusions
This paper focuses on the application of Monte-Carlo to specific problems in radiotherapy. These include radiotherapy and imaging beam production, brachytherapy, phantom and patient dosimetry, detector modelling and track structure calculations for micro-dosimetry, nano-dosimetry and radiobiology.
... To simulate radiation interactions on an event-by-event basis, several Monte Carlo track structure codes have been developed [16,17,18,19,20,21]; among them is the GEANT4-DNA package [22,23], which is a part of the open-source GEANT4 [24] Monte Carlo simulation toolkit. These software packages are important tools in radiation dosimetry due to the event-byevent simulation of particle interactions with the traversed medium, and have contributed significantly to the understanding of particle interaction processes [25,26] and the radiationinduced biological effects [26,27,28,29,30,31,32]. By generating detailed spatial pattern of energy deposition points, Monte Carlo track structure codes can simulate chemical species produced in water radiolysis [33]. ...
In e-aq dosimetry, absorbed radiation dose to water is measured by monitoring the concentration of radiation-induced hydrated electrons (e-aq). However, to obtain accurate dose, the radiation chemical yield of e-aq, G(e-aq), is needed for the radiation quality/setup under investigation. The aim of this study was to investigate the time-evolution of the G-values for the main generated reactive species during water radiolysis using GEANT4-DNA. The effects of cluster size and linear energy transfer (LET) on G(e-aq) were examined. Validity of GEANT4-DNA for calculation of G(e-aq) for clinically relevant energies was studied. Three scenarios were investigated with different phantom sizes and incoming electron energies (1 keV to 1 MeV). The time evolution of G(e-aq) was in good agreement with published data and did not change with decreasing phantom size. The time-evolution of the G-value increases with increasing LET for all radiolytic species. The particle tracks formed with high-energy electrons are separated and the resulting reactive species develop independently in time. With decreasing energy, the mean separation distance between reactive species decreases. The particle tracks might not initially overlap but will overlap shortly thereafter due to diffusion of reactive species, increasing the probability of e-aq recombination with other species. This also explains the decrease of G(e-aq) with cluster size and LET. Finally, if all factors are kept constant, as the incoming electron energy increases to clinically relevant energies, G(e-aq) remains similar to its value at 1 MeV, hence GEANT4-DNA can be used for clinically relevant energies.
... However, in contrast to modeling, biological experiments are most often performed at the cell population scale (Chatzipapas et al 2020). Therefore, the validation of Monte Carlo codes from these experimental data can only be performed based on mean results (McNamara et al 2017), occulting in the stochastic nature of DNA damage induction. Thus, the variability of intercellular responses was not represented by these simulation codes. ...
Objective:
The mechanisms of radiation-induced DNA damage can be understood via the fundamental acquisition of knowledge through a combination of experiments and modeling. Currently, most biological experiments are performed by irradiating an entire cell population, whereas modeling of radiation-induced effects is usually performed via Monte Carlo simulations with track structure codes coupled to realistic DNA geometries of a single-cell nucleus. However, the difference in scale between the two methods hinders a direct comparison because the dose distribution in the cell population is not necessarily uniform owing to the stochastic nature of the energy deposition. Thus, this study proposed the MINAS TIRITH tool to model the distribution of radiation-induced DNA damage in a cell population.
Approach:
The proposed method is based on precomputed databases of microdosimetric parameters and DNA damage distributions generated using the Geant4-DNA Monte Carlo Toolkit. First, a specific energy z was assigned to each cell of an irradiated population for a particular absorbed dose D_abs, following microdosimetric formalism. Then, each cell was assigned a realistic number of DNA damage events according to the specific energy z, respecting the stochastic character of its occurrence.
Main results:
This study validated the MINAS TIRITH tool by comparing its results with those obtained using the Geant4-DNA track structure code and a Geant4-DNA based simulation chain for DNA damage calculation. The different elements of comparison indicated consistency between MINAS TIRITH and the Monte Carlo simulation in case of the dose distribution in the population and the calculation of the amount of DNA damage.
Significance:
MINAS TIRITH is a new approach for the calculation of radiation-induced DNA damage at the cell population level that facilitates reasonable simulation times compared to those obtained with track structure codes. Moreover, this tool enables a more direct comparison between modeling and biological experimentation.
... At the subcellular scale, Monte Carlo simulations have proven to be useful for the quantification and assessment of radio-induced damage, particularly to the DNA molecule. Several tools are currently available, such as PARTRAC [9], KURBUC [10], and RITRACKS [11] Geant4-DNA [12][13][14][15], gMicroMC [16,17], and TOPAS-nBIO [18,19], MPEXS-DNA [20], IDDRRA [21] that either are based on Geant4-DNA or include some of its features. A review of such tools can be found in [1]. ...
Purpose
The scientific community shows great interest in the study of DNA damage induction, DNA damage repair, and the biological effects on cells and cellular systems after exposure to ionizing radiation. Several in silico methods have been proposed so far to study these mechanisms using Monte Carlo simulations. This study outlines a Geant4‐DNA example application, named “molecularDNA”, publicly released in the 11.1 version of Geant4 (December 2022).
Methods
It was developed for novice Geant4 users and requires only a basic understanding of scripting languages to get started. The example includes two different DNA‐scale geometries of biological targets, namely “cylinders” and “human cell”. This public version is based on a previous prototype and includes new features, such as: the adoption of a new approach for the modeling of the chemical stage, the use of the standard DNA damage format to describe radiation‐induced DNA damage, and upgraded computational tools to estimate DNA damage response.
Results
Simulation data in terms of single‐strand break and double‐strand break yields were produced using each of the available geometries. The results were compared with the literature, to validate the example, producing less than 5% difference in all cases. Conclusion: “molecularDNA” is a prototype tool that can be applied in a wide variety of radiobiology studies, providing the scientific community with an open‐access base for DNA damage quantification calculations. New DNA and cell geometries for the “molecularDNA” example will be included in future versions of Geant4‐DNA.
... Geant4-DNA combined the G4DNACPA100ElasticModel with the G4DNAChampionElasticModel into a new physics constructor G4EmDNAPhysics_option8 (option8) [56]. Similarly, Lund et al., have developed a physics constructor called G4EmDNAPhysics_hybrid2and4(hereafter "op-tion2and4") to extract the best features of option2 and option4 [57,58] in TOPAS-nBio [59,60], which is an extension to the TOPAS [61] Monte Carlo application based on Geant4. All their work was aimed at developing a recently advanced elastic model that would perform better than any of Geant4's electromagnetic (EM) models. ...
... The proportional model, which was also used in PARTRAC [19] and TOPAS-nBio [59,60], assigned a probability of strand breaks of 0 for total energy deposition below 5 eV (based on DNA induction experiments using low-energy photons and electrons [78]), of 1 above 37.5 eV, within a linear interpolation between. Thus, 21.25 eV was the energy deposition value at the probability of 0.5 in Formula (4). ...
Monte Carlo simulations can quantify various types of DNA damage to evaluate the biological effects of ionizing radiation at the nanometer scale. This work presents a study simulating the DNA target response after proton irradiation. A chromatin fiber model and new physics constructors with the ELastic Scattering of Electrons and Positrons by neutral Atoms (ELSEPA) model were used to describe the DNA geometry and the physical stage of water radiolysis with the Geant4-DNA toolkit, respectively. Three key parameters (the energy threshold model for strand breaks, the physics model and the maximum distance to distinguish DSB clusters) of scoring DNA damage were studied to investigate the impact on the uncertainties of DNA damage. On the basis of comparison of our results with experimental data and published findings, we were able to accurately predict the yield of various types of DNA damage. Our results indicated that the difference in physics constructor can cause up to 56.4% in the DNA double-strand break (DSB) yields. The DSB yields were quite sensitive to the energy threshold for strand breaks (SB) and the maximum distance to classify the DSB clusters, which were even more than 100 times and four times than the default configurations, respectively.
... Ultimately, our aim is to make the first quantitative analysis via simulation of the heuristic arguments proposed in Ref. [36]. To achieve this, we perform Monte Carlo simulations based on the widely-used particle interaction software Geant4 [85,86] using basic DNA-strand models constructed with the Geant4 front-end software TOPAS [87] and the TOPAS-nBio radiobiology toolkit [88]. ...
... Simulations were performed using the TOPAS-nBio radiobiology extension [88] of TOPAS version 3.3 [87], which uses Geant4 version 10.6 [85,86]. The interaction types simulated consist of multiple scatters, coupled transportation, and ionisation. ...
We present the first proof-of-concept simulations of detectors using biomaterials to detect particle interactions. The essential idea behind a “DNA detector” involves the attachment of a forest of precisely-sequenced single or double-stranded nucleic acids from a thin holding layer made of a high-density material. Incoming particles break a series of strands along a roughly co-linear chain of interaction sites and the severed segments then fall to a collection area. Since the sequences of base pairs in nucleic acid molecules can be precisely amplified and measured using polymerase chain reaction (PCR), the original spatial position of each broken strand inside the detector can be reconstructed with nm precision. Motivated by the potential use as a low-energy directional particle tracker, we perform the first Monte Carlo simulations of particle interactions inside a DNA detector. We compare the track topology as a function of incoming direction, energy, and particle type for a range of ionising particles. While particle identification and energy reconstruction might be challenging without a significant scale-up, the excellent potential angular and spatial resolution ( $$\lesssim 25^\circ $$ ≲ 25 ∘ axial resolution for keV-scale particles and nm-scale track segments) are clear advantages of this concept. We conclude that a DNA detector could be a cost-effective, portable, and powerful new particle detection technology. We outline the outstanding experimental challenges, and suggest directions for future laboratory tests.
... This is achieved by taking into account the contributions of the imaginary and complementary set of probabilities to the set R and that we have called accordingly the set M. This extension proved that it was effective and consequently we were successful to create an original paradigm dealing with prognostic and stochastic sciences in which we were able to express deterministically in C all the nondeterministic processes happening in the 'real' world R. This innovative paradigm was coined by the term "The Complex Probability Paradigm" and was started and established in my seventeen earlier publications and research works [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61]. ...
... We can see that by taking into consideration the set of imaginary probabilities we added three new and original axioms and consequently the system of axioms defined by Kolmogorov was hence expanded to encompass the set of imaginary numbers [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61]. ...
... In fact, if z represents one particle in a macrosystem from the uniform distribution U, then Z U represents all the particles in the whole macrosystem from the uniform distribution U that means that Z U represents the whole random distribution in the complex probability plane C. So, in this context, it follows directly that a Bernoulli distribution can be understood as a simplified system with two random particles (section 6-1), whereas the general case is a random system with N random particles (section 6-2). Afterward, I will prove an important property at the foundation of statistical mechanics and physics using this new powerful concept (section 6-3) [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61]. ...
In applied mathematics, the name Monte Carlo is given to the method of solving problems by means of experiments with random numbers. This name, after the casino at Monaco, was first applied around 1944 to the method of solving deterministic problems by reformulating them in terms of a problem with random elements which could then be solved by large-scale sampling. But, by extension, the term has come to mean any simulation that uses random numbers. In the twentieth century and present time, Monte Carlo methods have become among the fundamental techniques of simulation in modern science. This was accomplished after a long history of efforts done by prominent and distinguished mathematicians and scientists. This book is an illustration of the use of Monte Carlo methods when applied to solve specific problems in mathematics, engineering, physics, statistics, or science in general.
... Furthermore, with regard to biological modeling, the code inherits the chemical parameters provided by the Geant4-DNA toolkit and also includes mechanistic DNA repair models to perform water radiolysis simulations. With this, it is possible to develop complete modeling from the initial physical events to the final observed biological result [47]. ...
Monte Carlo simulations have been applied to determine and study different parameters that are challenged in experimental measurements, due to its capability in simulating the radiation transport with a probability distribution to interact with electrosferic electrons and some cases with the nucleus from an arbitrary material, which such particle track or history can carry out physical quantities providing data from a studied or investigating quantities. For this reason, simulation codes, based on Monte Carlo, have been proposed. The codes currently available are MNCP, EGSnrc, Geant, FLUKA, PENELOPE, as well as GAMOS and TOPAS. These simulation codes have become a tool for dose and dose distributions, essentially, but also for other applications such as design clinical, tool for commissioning of an accelerator linear, shielding, radiation protection, some radiobiologic aspect, treatment planning systems, prediction of data from results of simulation scenarios. In this chapter will be present some applications for radiotherapy procedures with use, specifically, megavoltage x-rays and electrons beams, in scenarios with homogeneous and anatomical phantoms for determining dose, dose distribution, as well dosimetric parameters through the PENELOPE and TOPAS code.
