IDDRRA

IDDRRA models DNA damage and repair induced by ionizing radiation using Monte Carlo (MC) simulations to quantify energy deposition, single-strand breaks (SSB), double-strand breaks (DSB), and cluster damage sites (CDS).

Key Features:

• Monte Carlo (MC) simulations: Uses MC simulations to evaluate radiation–matter interactions and resultant molecular damage.
• Geant4-DNA toolkit: Integrates Geant4-DNA physics and chemistry models for high-fidelity radiation and chemical stage simulation.
• Particle support: Simulates irradiation with electrons (e-), positrons (e+), protons, and electron spectra.
• Python-based DNA design algorithm: Provides a Python algorithm to generate custom DNA structures for simulation.
• C++ classes and output: Implements C++ classes that produce simulation outputs in text format.
• Damage analysis: Analyzes energy deposition, single-strand breaks (SSB), double-strand breaks (DSB), and cluster damage sites (CDS).
• Probabilistic repair models: Incorporates probabilistic Monte Carlo-based models for SSB and DSB repair simulation.
• Benchmarks: Includes benchmarks across particles and DNA configurations for validation.
• Expandability and future work: Designed to accept new benchmarks and includes planned extension for quantification of indirect damage.

Scientific Applications:

• Quantification of radiation-induced DNA damage: Measures energy deposition and catalogs SSB, DSB, and CDS occurrences at molecular scale.
• Modeling repair processes: Simulates probabilistic SSB and DSB repair dynamics to study potential cellular responses.
• Comparative particle effects: Enables comparison of damage patterns induced by electrons, positrons, protons, and electron spectra.
• Benchmarking and validation: Provides benchmark cases for validating radiation biology simulation methodologies and configurations.

Methodology:

Employs Monte Carlo simulations using Geant4-DNA physics and chemistry models; simulates electrons (e-), positrons (e+), protons, and electron spectra; generates custom DNA structures via a Python algorithm; uses C++ classes to produce text-format outputs; analyzes energy deposition, SSB, DSB, and CDS; and implements probabilistic MC models for SSB/DSB repair and benchmark evaluations.