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Biologically-Based Multistage Modeling of Radiation Effects

William Hazelton

hazelton@fhcrc.org
Fred Hutchinson Cancer Institute
Seattle, WA 98105



Why this Project?
Risks associated with exposure to low doses of ionizing radiation are highly uncertain. Recently considerably research effort has been devoted to improve our understanding of biological processes that control low dose radiation response. Unexpected findings including bystander effects, cell killing, mutation, and genomic instability have been characterized. Bystander effects have shown that biological effects occur not only in cells with irradiated nuclei but in cells where only the cytoplasm was hit, as well as in “non-hit” neighbors of irradiated cells. Early adaptive response can be induced that reduces the response to a high dose of radiation following a low dose. At very low doses some cells appear to be hypersensitive to radiation.


Project Goals:

A multistage cancer model will be meshed with models of new biological phenomena: genomic instability, adaptive response, bystander effects, and low-dose hypersensitivity to help understand the impact of these phenomena on radiation risk. The model will also consider the influence of the cell cycle on these observations.


Experimental Approach:

These phenomena will be incorporated into models of carcinogenesis that include multiple events leading to the initiation of cells and malignant conversion of cells. These multistage clonal expansion (MSCE) models can easily be extended to incorporate genomic instability, bystander effects, adaptive response and low-dose hypersensitivity. Collaboration with experimentalists and other modelers to focus on important features of these phenomena while avoiding unnecessary details will be an important part of this project.


Expected Outcomes:

A hierarchy of biological archetypes will be useful in determining parameters of these detailed models including cell cultures, 3D human cell clusters, cultured human tissues, and populations. Mechanistic models of these phenomena will be optimized using data from in vitro experiments. Combining these submodels will make it possible to incorporate them as components of in vivo models. Such modeling will make it possible to better relate in vitro mechanistic studies to in vivo observations.

 

 



                   
                   
                   
 

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