Advances in protein phosphorylation analysis by mass spectrometry (MS) are enabling the generation of high quality, quantitative datasets of protein phosphorylation with a breadth of coverage and reproducibility not previously attainable. Comparisons of signaling responses in cells at a network level are now feasible and studies looking at cellular response to ligand stimulation, drug treatment or genetic modification are transforming our understanding of how cellular decision processes are encoded through the signaling network. The large and dynamic datasets acquired through MS-based phosphoproteomics can be combined with other types of biological data for computational modeling of cellular decision processes with direct biological relevance to cellular state and predictive of cellular response. Signaling analysis at a network level is just beginning. Challenges remain in validating and translating initial models generated using defined in vitro models to in vivo systems. The advent of higher throughput methods for validating models generated with MS will deepen our understanding of the relationship between signaling and disease and therefore the development and implementation of therapeutics.