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Sensitivity analysis is a critical yet challenging problem for understanding complex systems. In genomic signal processing, it has been recognized that many biological systems are asymptotically stable. The sensitivity regarding the structural and dynamical uncertainty of network models may provide a deep understanding of the robustness, adaptability, and controllability of biological processes. We focus on the Boolean network model, as it has been shown to be able to capture the switching behavior of many biological processes by appropriate modeling of multivariate nonlinear relationships among genes. We study two different sensitivity measures for the Boolean network model, one directly related to individual predictor Boolean functions and the other to long-term network dynamics. Although there is some correlation between the measures, our study shows that these different sensitivities characterize different aspects of network behavior, so that their application depends on how they relate to specific translational goals.