From radiation dose to cellular dynamics: A discrete model for simulating cancer therapy

Radiation therapy is one of the most common cancer treatments, and dose optimization and targeting of radiation are crucial since both cancerous and healthy cells are affected. Different mathematical and computational approaches have been developed for this task. The most common mathematical approach, dating back to the late 1970s, is the linear-quadratic (LQ) model for the survival probability given the radiation dose. Most simulation models consider tissue as a continuum rather than consisting of discrete cells. While reasonable for large-scale models (e.g., human organs), continuum approaches necessarily neglect cellular-scale effects, which may play a role in growth, morphology, and metastasis of tumors. Here we propose a method for modeling the effect of radiation on cells based on the mechanobiological CellSim3D simulation model for growth, division, and proliferation of cells. To model the effect of a radiation beam, we incorporate a Monte Carlo procedure into CellSim3D with the LQ model by introducing a survival probability at each beam delivery. Effective removal of dead cells by phagocytosis was also implemented. Systems with two types of cells were simulated: stiff slowly proliferating healthy cells and soft rapidly proliferating cancer cells. For model verification, the results were compared to prostate cancer (PC-3 cell line) data for different doses, and we found good agreement. In addition, we simulated proliferating systems and analyzed the probability density of the contact forces. We determined the state of the system with respect to the jamming transition and found very good agreement with experiments.

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Publication Details

Journal
Physical review. E
Published
2025-11-13
DOI
https://doi.org/10.1103/fn8b-s7x3
Citations
1
Primary Topic
Mathematical Biology Tumor Growth
Type
article
Field-Weighted Citation Impact
1.37
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article

From radiation dose to cellular dynamics: A discrete model for simulating cancer therapy

Karl J. Friston
1 citations
Physical review. E
Mathematical Biology Tumor Growth
1.37
article

From radiation dose to cellular dynamics: A discrete model for simulating cancer therapy

Karl J. Friston
article en
1 citations

Abstract

Radiation therapy is one of the most common cancer treatments, and dose optimization and targeting of radiation are crucial since both cancerous and healthy cells are affected. Different mathematical and computational approaches have been developed for this task. The most common mathematical approach, dating back to the late 1970s, is the linear-quadratic (LQ) model for the survival probability given the radiation dose. Most simulation models consider tissue as a continuum rather than consisting of discrete cells. While reasonable for large-scale models (e.g., human organs), continuum approaches necessarily neglect cellular-scale effects, which may play a role in growth, morphology, and metastasis of tumors. Here we propose a method for modeling the effect of radiation on cells based on the mechanobiological CellSim3D simulation model for growth, division, and proliferation of cells. To model the effect of a radiation beam, we incorporate a Monte Carlo procedure into CellSim3D with the LQ model by introducing a survival probability at each beam delivery. Effective removal of dead cells by phagocytosis was also implemented. Systems with two types of cells were simulated: stiff slowly proliferating healthy cells and soft rapidly proliferating cancer cells. For model verification, the results were compared to prostate cancer (PC-3 cell line) data for different doses, and we found good agreement. In addition, we simulated proliferating systems and analyzed the probability density of the contact forces. We determined the state of the system with respect to the jamming transition and found very good agreement with experiments.

Physical review. EVol. 112(6)
Western University (CA), University of Eastern Finland (FI), University of Trento (IT), Hebrew University of Jerusalem (IL), Office of Advanced Scientific Computing Research (US), Istituto Nazionale di Fisica Nucleare, Trento Institute for Fundamental Physics And Applications (IT), Intelligent Systems Research (United States) (US), Finland University (FI)
Openalex Percentile: Top 15%
Mathematical Biology Tumor Growth
1.37
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