Modeling the Physics of Damage Cluster Formation in a Cellular Environment (Final Report)

摘要

Modern tools of radiobiology are leading to many new discoveries regarding how cells and tissues respond to radiation exposure. We can now irradiate single cells and observe responses in adjacent cells. We can also measure clusters of radiation damage produced in DNA. Our primary objective has been to understand the underling physics associated with these new biological responses. The primary tools available to describe the initial spatial pattern of damage formed by the absorption of ionizing radiation are based on Monte Carlo simulation of the structure of charged particle tracks. Although many Monte Carlo codes exist and considerable progress is being made in the incorporation of detailed macromolecular target structures into these codes, much of the interaction physics is still based on gas phase measurements and/or untested theoretical calculations that focus on water as the transport medium. Our objectives were threefold, (1) to expand the applicability of Monte Carlo track structure simulation to tissue-like material beyond the current focus on water, (2) to incorporate the most recent experimental information on electron interactions in biologically relevant material, and (3) to compare recent measurements of electron emissions induced by charged particles in thin foils with Monte Carlo predictions. We addressed these research objectives in three ways. First we applied theoretical techniques, similar to those used to derive data for water, to obtain cross sections for other condensed phase materials. This served two purposes. One was to provide testability of the theoretical technique by comparison to existing experimental data for electron transport (similar data does not exist for water), and the other was to expand the target database for use in modeling tissue. Second, we carefully reviewed published data, and ongoing experiments, for electron interaction cross-sections in biologically relevant condensed phase material. Results for low-energy electron interactions in biomolecules are particularly relevant. And third, we worked with Monte Carlo Modelers to incorporate these data into their codes for testing the sensitivity of results to the different input data and for direct tests of modeling results. We were particularly interested in how the molecular make up of the media influences the sensitivity of the Monte Carlo models of electron transport and the quality of the interaction cross sections used as the input database. This approach helps link the underling physics to the observed biological responses.