form of human HSF1 has been shown to associate with an Hsp90immunophilin-p23 complex, and this is thought to repress HSF1 transcriptional activity. Furthermore, HSP90 modulates HSF1 regulation in Xenopus oocytes. In yeast, mutations that interfere with Hsp90 function have been shown to derepress the expression of Hsf1-dependent MedChemExpress Neuromedin N reporter genes in S. cerevisiae. These data infer the existence of an autoregulatory loop in yeast, whereby Hsf1 activates HSP90 expression, and then Hsp90 downregulates Hsf1 activity. How could this autoregulatory loop control the dynamics of heat shock adaptation over time The functionality ” of biological systems depends upon both negative and positive feedback loops, such that system inputs reinforce or oppose the system output, respectively. Systems biology approaches are being increasingly utilised as a tool to examine the functionality, behaviour and dynamic properties of complex biological systems. However, despite the fundamental importance of heat shock regulation, the application of mathematical modelling to this adaptive response has been very limited. A few studies have examined the robustness of bacterial heat shock systems, which involve the transcriptional control of heat shock functions by the sigma factor s32. Also, there has been 
minimal modelling of heat shock systems in eukaryotic cells. Rieger and co-workers examined the regulation of HSP70 gene transcription by HSF1 in response to heat shock in cultured mammalian cells. Meanwhile Vilaprinyo and co-workers modelled the metabolic adaptation of yeast cells to heat shock. However, there has been no mathematical examination of the relationship between Hsp90 and Hsf1 in any system. Furthermore, few dynamic models have been reported for any molecular systems in C. albicans or other fungal pathogens. Yet it is clear that mathematical modelling will provide useful complementary approaches to the experimental dissection of these organisms, and will help to accelerate our progress in elucidating how pathogens adapt to the complex and dynamic microenvironments they encounter in their human host. Modelling biochemical networks allows the integration of experimental knowledge into a logical framework to test, support or falsify hypotheses about underlying biological mechanisms. Indeed, modelling can emphasise holistic aspects of systems which can often disappear in the experimental dissection of individual components of large systems. Moreover, when a model has been established, it can be used to further test hypotheses, or simulate behaviours that would be difficult to test in the laboratory. We reasoned that a combination of mathematical modelling and experimental dissection will enhance our understanding of how pathogens adapt to the temperature shifts they encounter in febrile patients, for example. Therefore, in this study we have exploited an integrative systems biology approach to study the dynamic regulation of the heat shock response in C. albicans. Our model was constructed around the assumption that an autoregulatory loop involving Hsf1 and Hsp90 plays a central role in the control of thermal adaptation. The model was parameterised using experimental data that defined the dynamics of the heat shock response in this pathogen. The model was then utilised to make well-defined predictions about the behaviour of this system that were subsequently confirmed experimentally. 18003836” This has allowed us to draw several important conclusions. In particular we ha
