227 Jin Zhang et al. (eds.), Fluorescent Protein-Based Biosensors: Methods and Protocols, Methods in Molecular Biology, vol. 1071, DOI 10.1007/978-1-62703-622-1_18, © Springer Science+Business Media, LLC 2014 Chapter 18 Integrating Fluorescent Biosensor Data Using Computational Models Eric C. Greenwald, Renata K. Polanowska-Grabowska, and Jeffrey J. Saucerman Abstract This book chapter provides a tutorial on how to construct computational models of signaling networks for the integration and interpretation of FRET-based biosensor data. A model of cAMP production and PKA activation is presented to provide an example of the model building process. The computational model is defined using hypothesized signaling network structure and measured kinetic parameters and then simu- lated in Virtual Cell software. Experimental acquisition and processing of FRET biosensor data is discussed in the context of model validation. This data is then used to fit parameters of the computational model such that the model can more accurately predict experimental data. Finally, this model is used to show how computational experiments can interrogate signaling networks and provide testable hypotheses. This simple, yet detailed, tutorial on how to use computational models provides biologists that use biosensors a powerful tool to further probe and evaluate the underpinnings of a biological response. Key words Computational modeling, FRET biosensors, Virtual cell, Cell signaling, cAMP 1 Introduction Fluorescence microscopy of genetically encoded fluorescence resonance energy transfer (FRET)-based biosensors is a powerful technique for understanding signaling kinetics in living cells. These biosensors allow researchers to visualize and quantify the spatiotem- poral distribution of signaling molecules within the cell. As discussed in previous chapters, a variety of FRET biosensors have been devel- oped to directly detect changes in intracellular concentrations or activation of signaling molecules in real time [1]. It is often the goal to understand how these biochemical dynamics are modulated by the overall signaling network, particularly in response to pharmaco- logic or genetic perturbations. The complexity of signaling networks often hinders attempts to relate biosensor data directly to the molec- ular mechanisms underlying dynamic cell responses. Computational models allow integration of diverse biochemical and biosensor data