Abstract:
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Protein signaling network models are often used for investigating complex biochemical pathways. In recent years, computational models for such networks have become quite popular for understanding different processes. Of course, using experimental data helps constrain such networks and provide further insight into the physical system. In this talk, an approach for calibrating a protein-signaling network model is presented. The data structure of interest is a complex network with non-linear transfer functions between nodes. The data include observations under different experimental stimuli, but with incomplete sampling of network nodes. We cast this application as a computer model calibration problem and develop a new sequential MCMC approach for exploring the network structure and quantifying uncertainty.
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