Proteomic and computational methods in systems modeling of cellular signaling

Cellular signaling lies at the core of cellular behavior, and is central for the understanding of many pathologic conditions. To comprehend how signal transduction is orchestrated at the molecular level remains the ultimate challenge for cell biology. In the last years there has been a revolution in the development of high-throughput methodologies in proteomics and genomics, which have provided extensive knowledge about expression profiles and molecular interaction-networks. However, these methods have typically provided qualitative and static information. This is about to turn, and several high-throughput methods are now available that provide quantitative and temporal information. These data are well suited for analysis by computational methods and bioinformatics, which are becoming increasingly valuable tools to grasp the complexity of cellular networks. At present, several cellular pathways have been modeled in silico and the analysis provides new understanding of the underlying properties that contribute to their dynamic features. Here, we review methodologies that are used for in silico modeling as well as methods to obtain large-scale quantitative data, and discuss how they can be integrated to generate powerful and predictive models of cellular processes. We argue that the generation of such models provide powerful tools to understand how systems properties emerges in healthy and pathologic states, and to generate efficient strategies for pharmacological intervention.