Predictive modeling of signal transduction anchored by quantitative imaging

Signal transduction is challenging to investigate because it depends upon both spatial organization of signaling and receptor molecules and alterations of gene expression spanning multiple time and space scales. We argue that mathematical modeling provides the tools to build upon the results from quantitative imaging to analyze and interpret cell signaling across these scales. Quantitative imaging tools such as fluorescence fluctuation spectroscopy methods provide many of the key parameters needed for predictive modeling, including measurements of: protein mobility, oligomerization, and concentrations, as functions of time and space. Comprehensive predictive models can integrate such multi-dimensional and multi-scale data into frameworks that can be used to expand our understanding of biological signaling networks.