Cells are constantly bombarded with environmental signals. Often these signals are in opposition; for example, one signal on its own might lead to growth, another to death. When both signals are simultaneously present, the cell needs to integrate two pieces of information to make a single decision. Currently, there is little understanding of how the integration of multiple inputs is achieved, but it is key to predicting the behavior of cells in complex environments. For yeast, a well-known example of multiple signaling is that of the presence of mixtures of the two sugars glucose and galactose. The textbook description of the integration event is simple: glucose signaling dominates over galactose signaling. This project will test a hypothesis challenging that description - for a broad range of concentrations, the response is far more complex, and involves a novel "ratio-sensor" mechanism. The mechanism would make it possible for the cell to opt for "the best of both" rather than being limited to "all or none". Establishing the existence of such flexibility in yeast responsiveness to sugar mixtures has far-reaching implications, in that it may extend to any number of other trade-off situations between two conflicting physiological or evolutionary objectives where the optimal decision would depend on the relative levels of a plurality of signals.
Broader Impacts: Many institutions have highlighted the need to introduce quantitative, theoretical and computational approaches into the life sciences mainstream. In response to that need, an initiative has been undertaken to develop an integrated quantitative curriculum for life scientists, beginning with a quantitative "bootcamp". The bootcamp has been extremely well received and materials have been used at other institutions around the world; within the next year a series of modules derived from the bootcamp will be made available via Harvard's online course offerings, HarvardX. The present project will generate data and conceptual approaches that will be used as examples in the bootcamp, and also in other curricula to be developed that will teach, hand-in-hand, experimental design, visualization, and quantitative and analysis methods.
