Creation and enhancement of a quantized hybrid computational model of cell cycle progression including DNA damage and repair mechanisms

This cell cycle network was also expanded to include interactions between cell cycle regulators and the p53 network, responsible for cell cycle arrest and the triggering of DNA damage repair. DNA damage included in this model was induced via radiation exposure, and thus can be incorporated at any point in the cell cycle, determining the specific repair mechanism response. <br><br>This hybrid model was then converted to calculate the absolute number of protein molecules as cells progress through the cell cycle.

This quantisation was achieved through the creation of a standard curve relating western blot band intensity, to the weight of the protein in the sample. After synchronising a population of Human Umbilical Vein Endothelial Cells (HUVEC), a time series was collected allowing for a direct comparison between the concentration of the cyclin proteins obtained from the samples, and the calculated concentrations from the model. <br><br>These results showed strong agreement between the computational data and the biological results allowing for the computational model to be converted to absolute protein concentration, with the maximum protein concentration obtained by cyclin B, on the order of 8E6 molecules per cell. <br><br>The expansion of the model to include the radiosensitisation due to the treatment with citrate capped gold nanoparticles was achieved by collecting in vitro data from gold nanoparticle treated HUVEC cells before irradiation, HUVEC’s were used as they contain fully intact cell cycle damage checkpoints and a functioning p53 gene.

Both the short and long term effects where investigated, showing a clear increase in damage yields at all radiation doses tested and a decrease in the long term survivability. This result was incorporated into the model providing a complete cell cycle progression model that includes DNA damage, repair, cell cycle arrest and radiosensitisation, giving a more complete understanding of the processes in a single model.