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Integrated Spatial and Temporal Stochastic Model for Computational Radiation Biology: A Case Study On Radiotherapy for Vascular Tumor

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R Liu

R Liu*, K Higley , Oregon State University, Corvallis, OR


SU-I-GPD-T-649 (Sunday, July 30, 2017) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose: This work was intended to develop an integrated spatial and temporal stochastic model to study the radiation effect on biological cells. The model could be used to create the computerized cell, to simulate the radiation transportation process in the cell, to quantify the DNA damage production in the cell, to quantify the cell state evolution dynamics, and to quantify the biological effect of the cell. The model was implemented based on multi-platform simulation. The developed model will contribute to the development of computational radiation biology.

Methods: Firstly, developing a set of simulation theory which includes five modules which serve the tools to simulate the major inter-steps of formation of radiation-induced effects. The five modules are Cell Maker, Transport Solver, Physical-Bio Translator, Cell Simulator, and Radiation Risk Analyzer. Secondly, a simulation package was developed based on two platforms which could implement radiation transport simulation and cell biology simulation. Thirdly, applying the package on a vascular tumor simulation.

Results: A three-dimensional vascular tumor growth process was specifically studied. Using ISTS model the cell response could be simulated, and with the cell biology simulation based on CompuCell3D, much more powerful simulation results could be obtained. At the specific time, electrons were used to irradiate the vascular cells, then the evolution process of tumors was tracked using our simulation package. Simulation results show tumor cells gradually died, which means the electron radiation successfully kills vascular cells and tumor cells die because of lacking nutrients.

Conclusion: An integrated spatial and temporal stochastic model was developed and was successfully implemented. We hope that this work will shed light on building a comprehensive mathematical modeling tool for computational radiation biology. A test shows that the developed model could be used to facilitate the investigation of radiation biology.

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