A quantitative model of the major pathways for radiation-induced DNA double-strand break repair

Evaluating the biological impact of shelters on astronaut health during different solar particle events: a Geant4-DNA simulation study

Mechanistic Monte Carlo simulations have proven invaluable in tackling complex challenges in radiobiology, for example for protecting astronauts from solar particle events (SPEs) during deep space missions which remains an underexplored area. In this study, the Geant4-DNA Monte Carlo code was used to assess the DNA damage caused by SPEs and evaluate the protective effectiveness of a multilayer shelter. By examining the February 1956 and October 1989 SPEs-two extreme cases-the results showed that the proposed shelter reduced DNA damage by up to 57.9% for the October 1989 SPE and 36.7% for the February 1956 SPE. Cell repair and survival modeling further revealed enhanced cell survival with the shelter, reducing lethal DNA damage by up to 64.3% and 88.2% for February 1956 and October 1989 SPEs, respectively. The results presented here highlight the crucial importance of developing effective radiation shielding to protect astronauts during solar storms and emphasizes the need to improve predictions of solar particle events to optimize shelter design.

Assessing the biological effects of boron neutron capture therapy through cellular DNA damage repair model

BACKGROUND

Boron neutron capture therapy (BNCT) is a targeted radiotherapy that relies on the 10B (n, α) 7Li reaction, which produces secondary particles with high linear energy transfer (LET), leading to a high relative biological effectiveness (RBE) in tumors. The biological effectiveness of BNCT is influenced by factors such as boron distribution and concentration, necessitating improved methods for assessing its radiobiological effects and clarifying the sensitivity of the differences in different factors to the biological effects.

PURPOSE

This paper introduces a method to evaluate the biological effects of BNCT using the cellular repair model. This method aims to overcome some of the limitations of current evaluation approaches. The primary goal is to provide guidance for clinical treatments and the development of boron drugs, as well as to investigate the impact of the synergistic effects of mixed radiation fields in BNCT on treatment outcomes.

METHODS

The approach involves three key steps: first, extending the radial energy deposition distribution of BNCT secondary particles using Geant4-DNA. This allows for the calculation of initial DNA double-strand breaks (DSBs) distributions for a given absorbed dose. Next, the obtained initial DSB distributions are used for DNA damage repair simulations to generate cell survival curves, then thereby quantifying RBE and compound biological effectiveness (CBE). The study also explores the synergistic effects of the mixed radiation fields in BNCT on assessing biological effects were also explored in depth.

RESULTS

The results showed that the RBE of boronophenylalanine (BPA) and sodium borocaptate (BSH) drugs at cell survival fraction 0.01 was 2.50 and 2.15, respectively. The CBE of the boron dose component was 3.60 and 0.73, respectively, and the RBE of the proton component was 3.21, demonstrating that BPA has a significantly higher biological impact than BSH due to superior cellular permeability. The proton dose significance in BNCT treatment is also underscored, necessitating consideration in both experimental and clinical contexts. The study demonstrates that synergistic effects between disparate radiation fields lead to increased misrepairs and enhanced biological impact. Additionally, the biological effect diminishes with rising boron concentration, emphasizing the need to account for intercellular DNA damage heterogeneity.

CONCLUSIONS

This methodology offers valuable insights for the development of new boron compounds and precise assessment of bio-weighted doses in clinical settings and can be adapted to other therapeutic modalities.

A clustering tool for generating biological geometries for computational modeling in radiobiology

Objective.To develop a computational tool that converts biological images into geometries compatible with computational software dedicated to the Monte Carlo simulation of radiation transport (TOPAS), and subsequent biological tissue responses (CompuCell3D). The depiction of individual biological entities from segmentation images is essential in computational radiobiological modeling for two reasons: image pixels or voxels representing a biological structure, like a cell, should behave as a single entity when simulating biological processes, and the action of radiation in tissues is described by the association of biological endpoints to physical quantities, as radiation dose, scored the entire group of voxels assembling a cell.Approach.The tool is capable of cropping and resizing the images and performing clustering of image voxels to create independent entities (clusters) by assigning a unique identifier to these voxels conforming to the same cluster. The clustering algorithm is based on the adjacency of voxels with image values above an intensity threshold to others already assigned to a cluster. The performance of the tool to generate geometries that reproduced original images was evaluated by the dice similarity coefficient (DSC), and by the number of individual entities in both geometries. A set of tests consisting of segmentation images of cultured neuroblastoma cells, two cell nucleus populations, and the vasculature of a mouse brain were used.Main results.The DSC was 1.0 in all images, indicating that original and generated geometries were identical, and the number of individual entities in both geometries agreed, proving the ability of the tool to cluster voxels effectively following user-defined specifications. The potential of this tool in computational radiobiological modeling, was shown by evaluating the spatial distribution of DNA double-strand-breaks after microbeam irradiation in a segmentation image of a cell culture.Significance.This tool enables the use of realistic biological geometries in computational radiobiological studies.

TOPAS-Tissue: A Framework for the Simulation of the Biological Response to Ionizing Radiation at the Multi-Cellular Level

This work aims to develop and validate a framework for the multiscale simulation of the biological response to ionizing radiation in a population of cells forming a tissue. We present TOPAS-Tissue, a framework to allow coupling two Monte Carlo (MC) codes: TOPAS with the TOPAS-nBio extension, capable of handling the track-structure simulation and subsequent chemistry, and CompuCell3D, an agent-based model simulator for biological and environmental behavior of a population of cells. We verified the implementation by simulating the experimental conditions for a clonogenic survival assay of a 2-D PC-3 cell culture model (10 cells in 10,000 µm2) irradiated by MV X-rays at several absorbed dose values from 0-8 Gy. The simulation considered cell growth and division, irradiation, DSB induction, DNA repair, and cellular response. The survival was obtained by counting the number of colonies, defined as a surviving primary (or seeded) cell with progeny, at 2.7 simulated days after irradiation. DNA repair was simulated with an MC implementation of the two-lesion kinetic model and the cell response with a p53 protein-pulse model. The simulated survival curve followed the theoretical linear-quadratic response with dose. The fitted coefficients α = 0.280 ± 0.025/Gy and β = 0.042 ± 0.006/Gy2 agreed with published experimental data within two standard deviations. TOPAS-Tissue extends previous works by simulating in an end-to-end way the effects of radiation in a cell population, from irradiation and DNA damage leading to the cell fate. In conclusion, TOPAS-Tissue offers an extensible all-in-one simulation framework that successfully couples Compucell3D and TOPAS for multiscale simulation of the biological response to radiation.

Dose-Dependent Shift in Relative Contribution of Homologous Recombination to DNA Repair after Low-LET Ionizing Radiation Exposure: Empirical Evidence and Numerical Simulation

Understanding the relative contributions of different repair pathways to radiation-induced DNA damage responses remains a challenging issue in terms of studying the radiation injury endpoints. The comparative manifestation of homologous recombination (HR) after irradiation with different doses greatly determines the overall effectiveness of recovery in a dividing cell after irradiation, since HR is an error-free mechanism intended to perform the repair of DNA double-strand breaks (DSB) during S/G2 phases of the cell cycle. In this article, we present experimentally observed evidence of dose-dependent shifts in the relative contributions of HR in human fibroblasts after X-ray exposure at doses in the range 20-1000 mGy, which is also supported by quantitative modeling of DNA DSB repair. Our findings indicate that the increase in the radiation dose leads to a dose-dependent decrease in the relative contribution of HR in the entire repair process.

The Effects of Galactic Cosmic Rays on the Central Nervous System: From Negative to Unexpectedly Positive Effects That Astronauts May Encounter

Galactic cosmic rays (GCR) pose a serious threat to astronauts’ health during deep space missions. The possible functional alterations of the central nervous system (CNS) under GCR exposure can be critical for mission success. Despite the obvious negative effects of ionizing radiation, a number of neutral or even positive effects of GCR irradiation on CNS functions were revealed in ground-based experiments with rodents and primates. This review is focused on the GCR exposure effects on emotional state and cognition, emphasizing positive effects and their potential mechanisms. We integrate these data with GCR effects on adult neurogenesis and pathological protein aggregation, forming a complete picture. We conclude that GCR exposure causes multidirectional effects on cognition, which may be associated with emotional state alterations. However, the irradiation in space-related doses either has no effect or has performance enhancing effects in solving high-level cognition tasks and tasks with a high level of motivation. We suppose the model of neurotransmission changes after irradiation, although the molecular mechanisms of this phenomenon are not fully understood.

Prediction of DNA rejoining kinetics and cell survival after proton irradiation for V79 cells using Geant4-DNA

PURPOSE

Track structure Monte Carlo (MC) codes have achieved successful outcomes in the quantitative investigation of radiation-induced initial DNA damage. The aim of the present study is to extend a Geant4-DNA radiobiological application by incorporating a feature allowing for the prediction of DNA rejoining kinetics and corresponding cell surviving fraction along time after irradiation, for a Chinese hamster V79 cell line, which is one of the most popular and widely investigated cell lines in radiobiology.

METHODS

We implemented the Two-Lesion Kinetics (TLK) model, originally proposed by Stewart, which allows for simulations to calculate residual DNA damage and surviving fraction along time via the number of initial DNA damage and its complexity as inputs.

RESULTS

By optimizing the model parameters of the TLK model in accordance to the experimental data on V79, we were able to predict both DNA rejoining kinetics at low linear energy transfers (LET) and cell surviving fraction.

CONCLUSION

This is the first study to demonstrate the implementation of both the cell surviving fraction and the DNA rejoining kinetics with the estimated initial DNA damage, in a realistic cell geometrical model simulated by full track structure MC simulations at DNA level and for various LET. These simulation and model make the link between mechanistic physical/chemical damage processes and these two specific biological endpoints.

Modeling of DNA Damage Repair and Cell Response in Relation to p53 System Exposed to Ionizing Radiation

Repair of DNA damage induced by ionizing radiation plays an important role in the cell response to ionizing radiation. Radiation-induced DNA damage also activates the p53 system, which determines the fate of cells. The kinetics of repair, which is affected by the cell itself and the complexity of DNA damage, influences the cell response and fate via affecting the p53 system. To mechanistically study the influences of the cell response to different LET radiations, we introduce a new repair module and a p53 system model with NASIC, a Monte Carlo track structure code. The factors determining the kinetics of the double-strand break (DSB) repair are modeled, including the chromosome environment and complexity of DSB. The kinetics of DSB repair is modeled considering the resection-dependent and resection-independent compartments. The p53 system is modeled by simulating the interactions among genes and proteins. With this model, the cell responses to low- and high-LET irradiation are simulated, respectively. It is found that the kinetics of DSB repair greatly affects the cell fate and later biological effects. A large number of DSBs and a slow repair process lead to severe biological consequences. High-LET radiation induces more complex DSBs, which can be repaired by slow processes, subsequently resulting in a longer cycle arrest and, furthermore, apoptosis and more secreting of TGFβ. The Monte Carlo track structure simulation with a more realistic repair module and the p53 system model developed in this study can expand the functions of the NASIC code in simulating mechanical radiobiological effects.

Review of the Geant4-DNA Simulation Toolkit for Radiobiological Applications at the Cellular and DNA Level

Simple Summary

A brief description of the methodologies to simulate ionizing radiation transport in biologically relevant matter is presented. Emphasis is given to the physical, chemical, and biological models of Geant4-DNA that enable mechanistic radiobiological modeling at the cellular and DNA level, important to improve the efficacy of existing and novel radiotherapeutic modalities for the treatment of cancer.

Abstract

The Geant4-DNA low energy extension of the Geant4 Monte Carlo (MC) toolkit is a continuously evolving MC simulation code permitting mechanistic studies of cellular radiobiological effects. Geant4-DNA considers the physical, chemical, and biological stages of the action of ionizing radiation (in the form of x- and γ-ray photons, electrons and β^±-rays, hadrons, α-particles, and a set of heavier ions) in living cells towards a variety of applications ranging from predicting radiotherapy outcomes to radiation protection both on earth and in space. In this work, we provide a brief, yet concise, overview of the progress that has been achieved so far concerning the different physical, physicochemical, chemical, and biological models implemented into Geant4-DNA, highlighting the latest developments. Specifically, the “dnadamage1” and “molecularDNA” applications which enable, for the first time within an open-source platform, quantitative predictions of early DNA damage in terms of single-strand-breaks (SSBs), double-strand-breaks (DSBs), and more complex clustered lesions for different DNA structures ranging from the nucleotide level to the entire genome. These developments are critically presented and discussed along with key benchmarking results. The Geant4-DNA toolkit, through its different set of models and functionalities, offers unique capabilities for elucidating the problem of radiation quality or the relative biological effectiveness (RBE) of different ionizing radiations which underlines nearly the whole spectrum of radiotherapeutic modalities, from external high-energy hadron beams to internal low-energy gamma and beta emitters that are used in brachytherapy sources and radiopharmaceuticals, respectively.

Performance Evaluation for Repair of HSGc-C5 Carcinoma Cell Using Geant4-DNA

Track-structure Monte Carlo simulations are useful tools to evaluate initial DNA damage induced by irradiation. In the previous study, we have developed a Gean4-DNA-based application to estimate the cell surviving fraction of V79 cells after irradiation, bridging the gap between the initial DNA damage and the DNA rejoining kinetics by means of the two-lesion kinetics (TLK) model. However, since the DNA repair performance depends on cell line, the same model parameters cannot be used for different cell lines. Thus, we extended the Geant4-DNA application with a TLK model for the evaluation of DNA damage repair performance in HSGc-C5 carcinoma cells which are typically used for evaluating proton/carbon radiation treatment effects. For this evaluation, we also performed experimental measurements for cell surviving fractions and DNA rejoining kinetics of the HSGc-C5 cells irradiated by 70 MeV protons at the cyclotron facility at the National Institutes for Quantum and Radiological Science and Technology (QST). Concerning fast- and slow-DNA rejoining, the TLK model parameters were adequately optimized with the simulated initial DNA damage. The optimized DNA rejoining speeds were reasonably agreed with the experimental DNA rejoining speeds. Using the optimized TLK model, the Geant4-DNA simulation is now able to predict cell survival and DNA-rejoining kinetics for HSGc-C5 cells.

A Geant4-DNA Evaluation of Radiation-Induced DNA Damage on a Human Fibroblast

Simple Summary

DNA damage caused by ionizing radiation in a human fibroblast cell evaluated by the Geant4-DNA Monte Carlo toolkit is presented. A validation study using a computational geometric human DNA model was then carried out, and the calculated DNA damage as a function of particle type and energy is presented. The results of this work showed a significant improvement on past work and were consistent with recent radiobiological experimental data, such as damage yields. This work and the developed methodology could impact a broad number of research fields in which the understanding of radiation effects is crucial, such as cancer radiotherapy, space science, and medical physics.

Abstract

Accurately modeling the radiobiological mechanisms responsible for the induction of DNA damage remains a major scientific challenge, particularly for understanding the effects of low doses of ionizing radiation on living beings, such as the induction of carcinogenesis. A computational approach based on the Monte Carlo technique to simulate track structures in a biological medium is currently the most reliable method for calculating the early effects induced by ionizing radiation on DNA, the primary cellular target of such effects. The Geant4-DNA Monte Carlo toolkit can simulate not only the physical, but also the physico-chemical and chemical stages of water radiolysis. These stages can be combined with simplified geometric models of biological targets, such as DNA, to assess direct and indirect early DNA damage. In this study, DNA damage induced in a human fibroblast cell was evaluated using Geant4-DNA as a function of incident particle type (gammas, protons, and alphas) and energy. The resulting double-strand break yields as a function of linear energy transfer closely reproduced recent experimental data. Other quantities, such as fragment length distribution, scavengeable damage fraction, and time evolution of damage within an analytical repair model also supported the plausibility of predicting DNA damage using Geant4-DNA.The complete simulation chain application “molecularDNA”, an example for users of Geant4-DNA, will soon be distributed through Geant4.

Models for Translational Proton Radiobiology-From Bench to Bedside and Back

The number of proton therapy centers worldwide are increasing steadily, with more than two million cancer patients treated so far. Despite this development, pending questions on proton radiobiology still call for basic and translational preclinical research. Open issues are the on-going discussion on an energy-dependent varying proton RBE (relative biological effectiveness), a better characterization of normal tissue side effects and combination treatments with drugs originally developed for photon therapy. At the same time, novel possibilities arise, such as radioimmunotherapy, and new proton therapy schemata, such as FLASH irradiation and proton mini-beams. The study of those aspects demands for radiobiological models at different stages along the translational chain, allowing the investigation of mechanisms from the molecular level to whole organisms. Focusing on the challenges and specifics of proton research, this review summarizes the different available models, ranging from in vitro systems to animal studies of increasing complexity as well as complementing in silico approaches.

The relation between microdosimetry and induction of direct damage to DNA by alpha particles

In radiopharmaceutical treatmentsα-particles are employed to treat tumor cells. However, the mechanism that drives the biological effect induced is not well known. Being ionizing radiation,α-particles can affect biological organisms by producing damage to the DNA, either directly or indirectly. Following the principle that microdosimetry theory accounts for the stochastic way in which radiation deposits energy in sub-cellular sized volumes via physical collisions, we postulate that microdosimetry represents a reasonable framework to characterize the statistical nature of direct damage induction byα-particles to DNA. We used the TOPAS-nBio Monte Carlo package to simulate direct damage produced by monoenergetic alpha particles to different DNA structures. In separate simulations, we obtained the frequency-mean lineal energy (yF) and dose-mean lineal energy (yD) of microdosimetric distributions sampled with spherical sites of different sizes. The total number of DNA strand breaks, double strand breaks (DSBs) and complex strand breaks per track were quantified and presented as a function of eitheryForyD.The probability of interaction between a track and the DNA depends on how the base pairs are compacted. To characterize this variability on compactness, spherical sites of different size were used to match these probabilities of interaction, correlating the size-dependent specific energy (z) with the damage induced. The total number of DNA strand breaks per track was found to linearly correlate withyFandzFwhen using what we defined an effective volume as microdosimetric site, while the yield of DSB per unit dose linearly correlated withyDorzD,being larger for compacted than for unfolded DNA structures. The yield of complex breaks per unit dose exhibited a quadratic behavior with respect toyDand a greater difference among DNA compactness levels. Microdosimetric quantities correlate with the direct damage imparted on DNA.

Kinetics model of DNA double-strand break repair in eukaryotes

This manuscript outlines the kinetics of two main repair pathways of DNA double-strand break (DSB) in eukaryotes: non-homologous end joining (NHEJ) and homologous recombination repair (HRR). In this review, we discuss the precise study of recruitment kinetics of repair proteins based on the latest technologies in the past two decades. Then we simulate the theoretical description of the DNA repair process by mathematical models. In our study, the consecutive reactions chain (CRC) model and continuous-time random walk (CTRW) model have been unified by us, so that we can obtain the function of the number of intermediates with time in the same framework of equations, overcome the incompatibility between the two models. On this basis, we propose a data fitting workflow using these both models. Finally, we give an overview of different real-time quantitative methods and the new mechanism complexity that can be found from the corresponding dynamic models.

A kinetic model of continuous radiation damage to populations of cells: comparison to the LQ model and application to molecular radiotherapy

The linear-quadratic (LQ) model to describe the survival of irradiated cells may be the most frequently used biomathematical model in radiotherapy. There has been an intense debate on the mechanistic origin of the LQ model. An interesting approach is that of obtaining LQ-like behavior from kinetic models, systems of differential equations that model the induction and repair of damage. Development of such kinetic models is particularly interesting for application to continuous dose rate therapies, such as molecular radiotherapy or brachytherapy. In this work, we present a simple kinetic model that describes the kinetics of populations of tumor cells, rather than lethal/sub-lethal lesions, which may be especially useful for application to continuous dose rate therapies, as in molecular radiotherapy. The multi-compartment model consists of a set of three differential equations. The model incorporates in an easy way different cross-interacting compartments of cells forming a tumor, and may be of especial interest for studying dynamics of treated tumors. In the fast dose delivery limit, the model can be analytically solved, obtaining a simple closed-form expression. Fitting of several surviving curves with both this solution and the LQ model shows that they produce similar fits, despite being functionally different. We have also investigated the operation of the model in the continuous dose rate scenario, firstly by fitting pre-clinical data of tumor response to ¹³¹I-CLR1404 therapy, and secondly by showing how damage repair and proliferation rates can cause a treatment to achieve control or not. Kinetic models like the one presented in this work may be of special interest when modeling response to molecular radiotherapy.

Fully integrated Monte Carlo simulation for evaluating radiation induced DNA damage and subsequent repair using Geant4-DNA

Ionising radiation induced DNA damage and subsequent biological responses to it depend on the radiation’s track-structure and its energy loss distribution pattern. To investigate the underlying biological mechanisms involved in such complex system, there is need of predicting biological response by integrated Monte Carlo (MC) simulations across physics, chemistry and biology. Hence, in this work, we have developed an application using the open source Geant4-DNA toolkit to propose a realistic “fully integrated” MC simulation to calculate both early DNA damage and subsequent biological responses with time. We had previously developed an application allowing simulations of radiation induced early DNA damage on a naked cell nucleus model. In the new version presented in this work, we have developed three additional important features: (1) modeling of a realistic cell geometry, (2) inclusion of a biological repair model, (3) refinement of DNA damage parameters for direct damage and indirect damage scoring. The simulation results are validated with experimental data in terms of Single Strand Break (SSB) yields for plasmid and Double Strand Break (DSB) yields for plasmid/human cell. In addition, the yields of indirect DSBs are compatible with the experimental scavengeable damage fraction. The simulation application also demonstrates agreement with experimental data of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}γ-H2AX yields for gamma ray irradiation. Using this application, it is now possible to predict biological response along time through track-structure MC simulations.

A Stochastic Model of DNA Double-Strand Breaks Repair Throughout the Cell Cycle

Cell cycle phase is a decisive factor in determining the repair pathway of DNA double-strand breaks (DSBs) by non-homologous end joining (NHEJ) or homologous recombination (HR). Recent experimental studies revealed that 53BP1 and BRCA1 are the key mediators of the DNA damage response (DDR) with antagonizing roles in choosing the appropriate DSB repair pathway in G1, S, and G2 phases. Here, we present a stochastic model of biochemical kinetics involved in detecting and repairing DNA DSBs induced by ionizing radiation during the cell cycle progression. A three-dimensional stochastic process is defined to monitor the cell cycle phase and DSBs repair at times after irradiation. To estimate the model parameters, a Metropolis Monte Carlo method is applied to perform maximum likelihood estimation utilizing the kinetics of γ-H2AX and RAD51 foci formation in G1, S, and G2 phases. The recruitment of DSB repair proteins is verified by comparing our model predictions with the corresponding experimental data on human cells after exposure to X and γ-radiation. Furthermore, the interaction between 53BP1 and BRCA1 is simulated for G1 and S/G2 phases determining the competition between NHEJ and HR pathways in repairing induced DSBs throughout the cell cycle. In accordance with recent biological data, the numerical results demonstrate that the maximum proportion of HR occurs in S phase cells and the high level of NHEJ takes place in G1 and G2 phases. Moreover, the stochastic realizations of the total yield of simple and complex DSBs ligation are compared for G1 and S/G2 damaged cells. Finally, the proposed stochastic model is validated when DSBs induced by different particle radiation such as iron, silicon, oxygen, proton, and carbon.

Enhancing low-dose risk assessment using mechanistic mathematical models of radiation effects

Mechanistic mathematical modeling of ionizing radiation (IR) effects has a long history spanning several decades. Models that mathematically represent current knowledge and hypotheses about how radiation damages cells and organs, leading to deleterious outcomes such as carcinogenesis, are particularly useful for estimating radiation risks at doses that are relevant for radiation protection, but are too low to provide a strong 'signal-to-noise ratio' in epidemiological or experimental studies with realistic sample sizes. Here, I discuss examples of models in several relevant areas, including radionuclide biokinetics, non-targeted IR effects, DNA double-strand break (DSB) rejoining and radiation carcinogenesis. I do not provide a detailed review of the vast modeling literature in these fields, but focus on concepts that we have implemented, such as using continuous probability distributions of exponential rates to model radionuclide biokinetics and DSB rejoining, and combining short and long time scales in carcinogenesis models. Improvements in models, including the ability to generate new hypotheses based on model predictions, may come from the introduction of additional novel concepts and from integrating multiple data types.

Mechanistic modelling supports entwined rather than exclusively competitive DNA double-strand break repair pathway

Following radiation induced DNA damage, several repair pathways are activated to help preserve genome integrity. Double Strand Breaks (DSBs), which are highly toxic, have specified repair pathways to address them. The main repair pathways used to resolve DSBs are Non-Homologous End Joining (NHEJ) and Homologous Recombination (HR). Cell cycle phase determines the availability of HR, but the repair choice between pathways in the G2 phases where both HR and NHEJ can operate is not clearly understood. This study compares several in silico models of repair choice to experimental data published in the literature, each model representing a different possible scenario describing how repair choice takes place. Competitive only scenarios, where initial protein recruitment determines repair choice, are unable to fit the literature data. In contrast, the scenario which uses a more entwined relationship between NHEJ and HR, incorporating protein co-localisation and RNF138-dependent removal of the Ku/DNA-PK complex, is better able to predict levels of repair similar to the experimental data. Furthermore, this study concludes that co-localisation of the Mre11-Rad50-Nbs1 (MRN) complexes, with initial NHEJ proteins must be modeled to accurately depict repair choice.

Modeling the interplay between DNA-PK, Artemis, and ATM in non-homologous end-joining repair in G1 phase of the cell cycle

Modeling a biological process equips us with more comprehensive insight into the process and a more advantageous experimental design. Non-homologous end joining (NHEJ) is a major double-strand break (DSB) repair pathway that occurs throughout the cell cycle. The objective of the current work is to model the fast and slow phases of NHEJ in G1 phase of the cell cycle following exposure to ionizing radiation (IR). The fast phase contains the major components of NHEJ; Ku70/80 complex, DNA-dependent protein kinase catalytic subunit (DNA-PKcs), and XLF/XRCC4/ligase IV complex (XXL). The slow phase in G1 phase of the cell cycle is associated with more complex lesions and involves ATM and Artemis proteins in addition to the major components. Parameters are mainly obtained from experimental data. The model is successful in predicting the kinetics of DSB foci in 13 normal, ATM-deficient, and Artemis-deficient mammalian fibroblast cell lines in G1 phase of the cell cycle after exposure to low doses of IR. The involvement of ATM provides the model with the potency to be connected to different signaling pathways. Ku70/80 concentration and DNA-binding rate as well as XXL concentration and enzymatic activity are introduced as the best targets for affecting NHEJ DSB repair process. On the basis of the current model, decreasing concentration and DNA binding rate of DNA-PKcs is more effective than inhibiting its activity towards the Artemis protein.

The effect of stochasticity on repair of DNA double strand breaks throughout non-homologous end joining pathway

DNA double strand breaks (DSBs) are the most lethal lesions of DNA induced by ionizing radiation, industrial chemicals and a wide variety of drugs used in chemotherapy. In the context of DNA damage response system modelling, uncertainty may arise in several ways such as number of induced DSBs, kinetic rates and measurement error in observable quantities. Therefore, using the stochastic approaches is imperative to gain further insight into the dynamic behaviour of DSBs repair process. In this article, a continuous-time Markov chain (CTMC) model of the non-homologous end joining (NHEJ) mechanism is formulated according to the DSB complexity. Additionally, a Metropolis Monte Carlo method is used to perform maximum likelihood estimation of the kinetic rate constants. Here, the effects of fluctuating kinetic rates and DSBs induction rate of the NHEJ mechanism are investigated. The stochastic realizations of the total yield of simple and complex DSBs ligation are simulated to compare their asymptotic dynamics. Furthermore, it has been proved that the total yield of DSBs has a normal distribution for sufficiently large number of DSBs. In order to estimate the expected duration of repairing DSBs, the probability distribution of DSBs lifetime is calculated based on the CTMC NHEJ model. Moreover, the variability of total yield of DSBs during constant low-dose radiation is evaluated in the presented model. The findings indicate that in stochastic NHEJ model, when there is no new DSBs induction through the repair process, all DSBs are eventually repaired. However, when DSBs are induced by constant low-dose radiation, a number of DSBs remains un-repaired.

Inhibition of Anaphase-Promoting Complex by Silence APC/CCdh1 to Enhance Radiosensitivity of Nasopharyngeal Carcinoma Cells

The aim of this study was to investigate the possibility of APC/C^Cdh1 as a potential therapeutic target in the radiosensitivity of nasopharyngeal carcinoma (NPC) cell CNE-1, and explain the role of APC subunits after silence of Cdh1 combined with radiotherapy. Transfection with Cdh1 shRNA significantly increased the radiosensitivity of CNE-1 cells and the radiation enhancement ratio (RER) of sh-Cdh1 cells was 1.76. Knockdown of Cdh1 in CNE-1 cells increased irradiation induced apoptosis and G2/M phase cell cycle arrest. The levels of CDC20 and CylinB1 increased and the levels of Ku70 and APC3 decreased after irradiation. APC/C^Cdh1 is involved in regulation of radiosensitivity in human NPC CNE-1 cells. Our study may provide a promising therapeutic strategy for NPC by targeting Cdh1. J. Cell. Biochem. 118: 3150-3157, 2017. © 2016 Wiley Periodicals, Inc.

Radiosensitivity and relative biological effectiveness based on a generalized target model

By considering both cellular repair effects and indirect effects of radiation, we have generalized the traditional target model, and made it have a linear-quadratic-linear characteristic. To assess the repair capacity-dependent radiosensitivity and relative biological effectiveness (RBE), the generalized target model was used to fit the survival of human normal embryonic lung fibroblast MRC-5 cells in the G0 and G1 phases after various types of radiations. The fitting results indicate that the generalized target model works well in the dose ranges considered. The resulting calculations qualitatively show that the parameter ratio (a/V) in the model could represent the cellular repair capacity. In particular, the significant linear correlations between radiosensitivity/RBE and cellular repair capacity are observed for different slopes of the linear regression curves. These results show that the radiosensitivity and RBE depend on the cellular repair capacity and can be regulated by linear energy transfer. These analyses suggest that the ratio a/V in the generalized target model can also be used for radiation damage assessment in radiotherapy.

Mechanistic Modelling and Bayesian Inference Elucidates the Variable Dynamics of Double-Strand Break Repair

DNA double-strand breaks are lesions that form during metabolism, DNA replication and exposure to mutagens. When a double-strand break occurs one of a number of repair mechanisms is recruited, all of which have differing propensities for mutational events. Despite DNA repair being of crucial importance, the relative contribution of these mechanisms and their regulatory interactions remain to be fully elucidated. Understanding these mutational processes will have a profound impact on our knowledge of genomic instability, with implications across health, disease and evolution. Here we present a new method to model the combined activation of non-homologous end joining, single strand annealing and alternative end joining, following exposure to ionising radiation. We use Bayesian statistics to integrate eight biological data sets of double-strand break repair curves under varying genetic knockouts and confirm that our model is predictive by re-simulating and comparing to additional data. Analysis of the model suggests that there are at least three disjoint modes of repair, which we assign as fast, slow and intermediate. Our results show that when multiple data sets are combined, the rate for intermediate repair is variable amongst genetic knockouts. Further analysis suggests that the ratio between slow and intermediate repair depends on the presence or absence of DNA-PKcs and Ku70, which implies that non-homologous end joining and alternative end joining are not independent. Finally, we consider the proportion of double-strand breaks within each mechanism as a time series and predict activity as a function of repair rate. We outline how our insights can be directly tested using imaging and sequencing techniques and conclude that there is evidence of variable dynamics in alternative repair pathways. Our approach is an important step towards providing a unifying theoretical framework for the dynamics of DNA repair processes.

Track structure modeling in liquid water: A review of the Geant4-DNA very low energy extension of the Geant4 Monte Carlo simulation toolkit

Understanding the fundamental mechanisms involved in the induction of biological damage by ionizing radiation remains a major challenge of today's radiobiology research. The Monte Carlo simulation of physical, physicochemical and chemical processes involved may provide a powerful tool for the simulation of early damage induction. The Geant4-DNA extension of the general purpose Monte Carlo Geant4 simulation toolkit aims to provide the scientific community with an open source access platform for the mechanistic simulation of such early damage. This paper presents the most recent review of the Geant4-DNA extension, as available to Geant4 users since June 2015 (release 10.2 Beta). In particular, the review includes the description of new physical models for the description of electron elastic and inelastic interactions in liquid water, as well as new examples dedicated to the simulation of physicochemical and chemical stages of water radiolysis. Several implementations of geometrical models of biological targets are presented as well, and the list of Geant4-DNA examples is described.

A generalized target theory and its applications

Different radiobiological models have been proposed to estimate the cell-killing effects, which are very important in radiotherapy and radiation risk assessment. However, most applied models have their own scopes of application. In this work, by generalizing the relationship between "hit" and "survival" in traditional target theory with Yager negation operator in Fuzzy mathematics, we propose a generalized target model of radiation-induced cell inactivation that takes into account both cellular repair effects and indirect effects of radiation. The simulation results of the model and the rethinking of "the number of targets in a cell" and "the number of hits per target" suggest that it is only necessary to investigate the generalized single-hit single-target (GSHST) in the present theoretical frame. Analysis shows that the GSHST model can be reduced to the linear quadratic model and multitarget model in the low-dose and high-dose regions, respectively. The fitting results show that the GSHST model agrees well with the usual experimental observations. In addition, the present model can be used to effectively predict cellular repair capacity, radiosensitivity, target size, especially the biologically effective dose for the treatment planning in clinical applications.

Induction and Processing of the Radiation-Induced Gamma-H2AX Signal and Its Link to the Underlying Pattern of DSB: A Combined Experimental and Modelling Study

We present here an analysis of DSB induction and processing after irradiation with X-rays in an extended dose range based on the use of the γH2AX assay. The study was performed by quantitative flow cytometry measurements, since the use of foci counting would result in reasonable accuracy only in a limited dose range of a few Gy. The experimental data are complemented by a theoretical analysis based on the GLOBLE model. In fact, original aim of the study was to test GLOBLE predictions against new experimental data, in order to contribute to the validation of the model. Specifically, the γH2AX signal kinetics has been investigated up to 24 h after exposure to increasing photon doses between 2 and 500 Gy. The prolonged persistence of the signal at high doses strongly suggests dose dependence in DSB processing after low LET irradiation. Importantly, in the framework of our modelling analysis, this is related to a gradually increased fraction of DSB clustering at the micrometre scale. The parallel study of γH2AX dose response curves shows the onset of a pronounced saturation in two cell lines at a dose of about 20 Gy. This dose is much lower than expected according to model predictions based on the values usually adopted for the DSB induction yield (≈ 30 DSB/Gy) and for the γH2AX foci extension of approximately 2 Mbp around the DSB. We show and discuss how theoretical predictions and experimental findings can be in principle reconciled by combining an increased DSB induction yield with the assumption of a larger genomic extension for the single phosphorylated regions. As an alternative approach, we also considered in our model the possibility of a 3D spreading-mechanism of the H2AX phosphorylation around the induced DSB, and applied it to the analysis of both the aspects considered. Our results are found to be supportive for the basic assumptions on which GLOBLE is built. Apart from giving new insights into the H2AX phosphorylation process, experiments performed at high doses are of relevance in the context of radiation therapy, where hypo-fractionated schemes become increasingly popular.