Cellular signaling lies at the core of cellular behavior, and is central for the understanding of many pathologicconditions. To comprehend how signal transduction is orchestrated at the molecular level remains the ultimate challengefor cell biology. In the last years there has been a revolution in the development of high-throughput methodologies inproteomics 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, andseveral high-throughput methods are now available that provide quantitative and temporal information. These data arewell suited for analysis by computational methods and bioinformatics, which are becoming increasingly valuable tools tograsp the complexity of cellular networks. At present, several cellular pathways have been modeled in silico and theanalysis provides new understanding of the underlying properties that contribu te to their dynamic features. Here, we re-view methodologies that are used for in silico modeling as well as methods to obtain large-scale quantitative data, and dis-cuss how they can be integrated to generate powerful and predictive models of cellular processes. We argue that the gen-eration of such models provide powerful tools to understand how systems properties emerges in healthy and pathologicstates, and to generate efficient strategies for pharmacological intervention.
Keywords: Systems biology, proteomics, simulation, cellular signaling, quantification, flow cytometry, mass spectrometry
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