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Modeling of spatially‐restricted intracellular signaling

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Abstract Understanding the signaling capabilities of a cell presents a major challenge, not only due to the number of molecules involved, but also because of the complex network connectivity of intracellular signaling. Recently, the proliferation of quantitative imaging techniques has led to the discovery of the vast spatial organization of intracellular signaling. Computational modeling has emerged as a powerful tool for understanding how inhomogeneous signaling originates and is maintained. This article covers the current imaging techniques used to obtain quantitative spatial data and the mathematical approaches used to model spatial cell biology. Modeling‐derived hypotheses have been experimentally tested and the integration of modeling and imaging approaches has led to non‐intuitive mechanistic insights. WIREs Syst Biol Med 2012, 4:103–115. doi: 10.1002/wsbm.155 This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models

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Fluorescence recovery after photobleaching. (a) Schematic of a typical FRAP experiment. A cell expressing a fluorescent‐tagged protein is photobleached in a defined area (1, red square). The defined area is photobleached until no fluorescence is observed (2). The recovery of fluorescence is monitored (3, 4). (b) The corresponding fluorescence trace is analyzed to determine the mobile and immobile fractions, and the equilibration half‐time.

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(a) Flow diagram of the steps needed to develop a computational model. Model assembly requires the inclusion of the biochemical reaction scheme, the known parameters associated with the reactions, and the assumptions of the model. These steps will rely on previous knowledge available. Model calibration entails the use of experimental input–output relations to constrain any unknown parameters. Model validation involves the use of experimental data to probe the correctness of the model. The experimental data may have been a result of a model prediction. The iterative nature of modeling is shown, if the model fails to replicate the biological phenomena, then the steps of model assembly and calibration have to be revised. (b) ODE‐ and PDE‐based models. Biochemical reactions are represented in a mathematical formulation and placed in their proper compartments. Compartments are mapped to geometries derived from microscopic images. Model simulations lead to predictions that can be compared to experimental data.

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Imaging reporters available for signaling proteins and second messengers. Arrows depict signal flow. All of the molecules depicted here have available imaging reporters, allowing the monitoring of their levels of activity states. Small GTPases are shown as blue circles, while heterotrimeric G‐proteins are green. Enzymes, such as kinases, are shown as orange circles. Transcription factors are red. Histones are shown as teal circles.

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Förster resonance energy transfer. FRET occurs when two fluorophores are in close proximity (a) and have overlapping spectra (b). Blue represents the donor, yellow represents the acceptor. Excitation is depicted as the dashed line, while emission is the solid line. In (b) the gray block presents the possible cross excitation of both the donor and the acceptor. The golden box represents the residual emission of the donor that must be subtracted from the FRET signal. (c) Schematic of the types of FRET probes available. (i) Intermolecular reporters; (ii) intramolecular reporters (iii) reaction reporters.

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Fluorescence correlation spectroscopy. (a) The movement of a fluorescent‐tagged proteins in and out of a small illuminated volume is monitored. (b) (i) This results in multiple fluctuations in the fluorescence intensity over time. (ii) Autocorrelation analysis of fluctuations over time; G(τ) is the amplitude of the correlation and τ is the correlation time. (c) Changes in the autocorrelation function represent changes in (i) concentration or (ii) diffusion.

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