Biological functionality arises from the complex interactions of simple components. Emerging behaviour is difficult to recognize\r\nwith verbal models alone, and mathematical approaches are important. Even few interacting components can give rise to a wide\r\nrange of different responses, that is, sustained, transient, oscillatory, switch-like responses, depending on the values of the model\r\nparameters. A quantitative comparison of model predictions and experiments is therefore important to distinguish between\r\ncompeting hypotheses and to judge whether a certain regulatory behaviour is at all possible and plausible given the observed\r\ntype and strengths of interactions and the speed of reactions. Here I will review a detailed model for the transcription factor sF, a\r\nregulator of cell differentiation during sporulation in Bacillus subtilis. I will focus in particular on the type of conclusions that can\r\nbe drawn from detailed, carefully validated models of biological signaling networks. For most systems, such detailed experimental\r\ninformation is currently not available, but accumulating biochemical data through technical advances are likely to enable the\r\ndetailed modelling of an increasing number of pathways. A major challenge will be the linking of such detailed models and their\r\nintegration into a multiscale framework to enable their analysis in a larger biological context.
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