Developing an Agent-Based Mathematical Model for Simulating Post-Irradiation Cellular Response: A Crucial Component of a Digital Twin Framework for Personalized Radiation Treatment

TL;DR

This work addresses the challenge of linking rapid physical energy deposition from ionizing radiation to long-timescale cellular outcomes in a way suitable for digital-twin frameworks. It introduces the Physical–Bio Translator, an agent-based, cell-state–driven model that uses a state-energy formalism $E = \alpha N + \beta C$ and Gaussian overlap $\langle \phi_i|\phi_j\rangle$ to compute transition probabilities between Healthy, Arrested, and Dead states across cell-cycle phases, including saturation for lethal transitions. Bystander signaling is incorporated via a diffusion–reaction equation that couples to state-energy increments, enabling simulation of direct and indirect radiation effects on a monolayer of cells, with outputs such as cell-phase distributions, state compositions, and survival curves. The platform demonstrates qualitative agreement with expected radiobiological features (phase- and dose-dependent effects, hyper-radiosensitivity at low doses under certain conditions) and serves as a modular, extensible step toward multiscale digital twins for personalized radiation therapy. Future work will focus on parameter calibration against experimental data, uncertainty quantification, and extension to tissue- and patient-level heterogeneity to enhance predictive capability for treatment planning.

Abstract

In this study, we present the Physical-Bio Translator, an agent-based simulation model designed to simulate cellular responses following irradiation. This simulation framework is based on a novel cell-state transition model that accurately reflects the characteristics of irradiated cells. To validate the Physical-Bio Translator, we performed simulations of cell phase evolution, cell phenotype evolution, and cell survival. The results indicate that the Physical-Bio Translator effectively replicates experimental cell irradiation outcomes, suggesting that digital cell irradiation experiments can be conducted via computer simulation, offering a more sophisticated model for radiation biology. This work lays the foundation for developing a robust and versatile digital twin at multicellular or tissue scales, aiming to comprehensively study and predict patient responses to radiation therapy.

Figures (7)

• Figure 1: The proposed three cell states, $S_1$, $S_2$ and $S_3$. The green bar corresponds to the healthy state, the pink one corresponds to the arrested state, and the red bar corresponds to the dead state. The black arrows indicate the possible cell state transition routes. Each cell state has a corresponding cell state energy distribution which is proposed as a Gaussian distribution.
• Figure 2: Cell state transition diagram
• Figure 3: Illustration of neighborhood-based space availability. The current cell occupies $P(i,j)$; daughter cells can be placed in any of the four adjacent vacant sites.
• Figure 4: Monolayer cell culture irradiated by a 1 MeV electron plane source. Each cell is modeled as a 40 µ m water sphere with a 10 µ m nucleus centered inside. The red lines with yellow points illustrate representative electron tracks generated by the Geant4-DNA physics processes.
• Figure 5: The cell phase distribution of 1000 cells under different dose irradiation.