The identification of potential regulators of the mammalian heat shock response has broader implications than just providing a better understanding of the cellular response to thermal stress. The heat shock response also acts as a surrogate of the general protein quality control system within the cell which plays a significant role in aging and many protein folding diseases as well as the responses to other physical and chemical stressors. In this study, a broad-based functional genomics approach was taken to identify potential regulators of the mammalian heat shock response. The primary VE-822 screen identified a large number of potential modifiers that were subjected to a secondary screen for hyper-activation of the response under severe heat stress. The secondary screen was used to rank the potential modifiers and gene expression microarray analysis was used to identify which genes were expressed in the experimental cell line. A subset of eight genes were chosen for validation using siRNA knockdown. Of the eight genes, only PRKCI showed a statistically significant reduction in the heat shock response for both siRNA duplexes when compared with controls. Tag provides the driving force for tumor initiation by blocking the activities of Rb and p53 tumor suppressors. This idea has been borne out by the analysis of mice bearing germline deletion of individual ERM proteins, where abnormalities are largely restricted to tissues expressing only one family member. The variation of mutation effects with fitness, together with the fact that error rates can be easily modified as a consequence of mutations producing genotypes with variable capacity to cause errors, suggest that mutation rates are a character subjected to the action of natural selection. Stable environments would favour low mutation rates, constrained only by the costs of error-repair mechanisms. In contrast to this, environments subjected to frequent changes would select for increased mutation rates that permit faster adaptation to the new conditions. However, the optimization of the mutation rate is not only determined by its impact on adaptation but also by the consequences that the variation of this character has on fitness. High mutation rates can increase the number of deleterious mutations, whereas low mutation rates can have metabolic costs associated. The existence of these opposing forces causes that natural selection often fails to fully optimize this character. The study of the evolution of mutation rates has been addressed theoretically, and using digital organisms. There are also many reported examples of natural and experimental bacterial populations with higher than standard mutation rates, showing that there are multiple situations in Nature in which being a mutator confers a selective advantage.