Biological processes are dynamic, whereas the networks that depict them are typically static. Quantitative modeling using differential equations or logic-based functions can offer quantitative predictions of the behavior of biological systems, but they require detailed experimental characterization of interaction kinetics, which is typically unavailable. To determine to what extent complex biological processes can be modeled and analyzed using only the static structure of the network (i.e. the direction and sign of the edges), we attempt to predict the phenotypic effect of perturbations in biological networks from the static network structure.