Using State Variables to Model the Response of Tumour Cells to Radiation and Heat: A Novel Multi-Hit-Repair Approach

Author:

Scheidegger Stephan1ORCID,Fuchs Hans U.1ORCID,Zaugg Kathrin2,Bodis Stephan34,Füchslin Rudolf M.15

Affiliation:

1. ZHAW School of Engineering, Zurich University of Applied Science, 8401 Winterthur, Switzerland

2. University Hospital Bern, Switzerland

3. Radio-Onkologie-Zentrum KSA-KSB, 5001 Aarau, Switzerland

4. Medical Faculty, University of Zurich, 8006 Zurich, Switzerland

5. European Centre of Living Technology, 30124 Venice, Italy

Abstract

In order to overcome the limitations of the linear-quadratic model and include synergistic effects of heat and radiation, a novel radiobiological model is proposed. The model is based on a chain of cell populations which are characterized by the number of radiation induced damages (hits). Cells can shift downward along the chain by collecting hits and upward by a repair process. The repair process is governed by a repair probability which depends upon state variables used for a simplistic description of the impact of heat and radiation upon repair proteins. Based on the parameters used, populations up to 4-5 hits are relevant for the calculation of the survival. The model describes intuitively the mathematical behaviour of apoptotic and nonapoptotic cell death. Linear-quadratic-linear behaviour of the logarithmic cell survival, fractionation, and (with one exception) the dose rate dependencies are described correctly. The model covers the time gap dependence of the synergistic cell killing due to combined application of heat and radiation, but further validation of the proposed approach based on experimental data is needed. However, the model offers a work bench for testing different biological concepts of damage induction, repair, and statistical approaches for calculating the variables of state.

Publisher

Hindawi Limited

Subject

Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modelling and Simulation,General Medicine

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