Systems Biology of the Stress Response
Mammalian cells are constantly challenged with different forms of stress that can originate both from the physical environment and from intrinsic biological processes. To counteract these insults, a sophisticated stress response evolved that allows cells to sense stresses, to integrate these inputs with information about the overall cellular state and to activate an appropriate response. A central hub in the mammalian stress response is the tumour suppressor p53. It coordinates the cellular response to stresses such as DNA damage by changing the expression of hundreds of genes that are involved in repair pathways, cell cycle arrest or the induction of senescence and apoptosis. The tumour suppressor itself is controlled by a complex network of positive and negative regulators. If this crucial signalling pathway is disturbed by mutation, cancer arises. The main focus of our research is to understand the molecular mechanisms that enable p53 and its interacting pathways to faithfully manage the cellular stress response. Through genetics, genomics and proteomics, many molecular players that participate in these signalling processes have been identified, providing topological maps of stress response pathways. The challenge we are facing now is to understand how these molecular networks act dynamically in living cells and how they intersect with each other to control the physiological response of a cell. Individual cells within a population often react differently to the same stress, depending on their initial state. We therefore focus on the analysis of individual cells and investigate the common properties that unite them and the sources of variation that make them different. We use automated time-lapse microscopy of living cells expressing fluorescent reporters to measure the dynamics of signalling networks with high temporal resolution. Since signalling networks contain complex, non-linear interactions that are difficult to understand intuitively, we combine the resulting quantitative data with statistical analysis and mathematical modelling to gain a predictive understanding of cellular stress responses.