Radiobiological Modeling with Monte Carlo Tools - Simulating Cellular Responses to Ionizing Radiation

Abstract

As the prevalence of cancer continues to rise in a rapidly aging population, the integration of advancements in computational capabilities with oncological practices presents promising opportunities for enhancing cancer treatment management. In silico modeling has emerged as a key approach for studying the radiobiological aspects of cancer, providing novel pathways for understanding cellular mechanisms and potential future improvements in clinical radiotherapy. This review examines significant advancements and ongoing challenges in simulating the complex interactions of ionizing radiation with cancer cells. We explore the utility and limitations of current in silico models, including agent-based models and hybrid approaches that integrate cellular behavior with radiobiological effects using Monte Carlo tools. The paper highlights key developments that have enabled more accurate simulations of DNA damage, various repair processes, and the influence of the microenvironment on cellular radiosensitivity. Looking ahead, we address the need for further refinement of these models and their integration with experimental data to enhance predictive accuracy and potential clinical applications. The capacity of these models to potentiate personalized cancer therapy is emphasized, highlighting the ongoing shift towards more comprehensive and sophisticated computational approaches.

Keywords: Monte Carlo tools; biomathematical model; cancer hallmarks; in silico modeling; radiobiology.

Figure 1.

Progressive Levels of DNA Geometry Available in TOPAS-NBio to Study Radiation Effects—from Simple Linear Strands to Dense Chromatin Structures—Demonstrating the Trade-off Between Biological Realism and Computational Feasibility.

Figure 2.

Conceptual Map Linking In Silico Modeling Tools To Radiobiological Processes And Clinical Applications, Highlighting Iterative Data-Driven Refinement.