This work (pdf) was done during my postdoc at the BarabasiLab. We investigated the role of network topology in accurately predicting perturbation patterns in biological network. Indeed, the development of high-throughput technologies has allowed mapping a significant proportion of interactions between biochemical entities in the cell. In short, we begin to have a good mapping of the subcellular “interacotme”. However, it is unclear how much information is lost given the lack of measurements on the kinetic parameters governing the dynamics of these interactions. Using biochemical networks with experimentally measured kinetic parameters, we show that a knowledge of the network topology offers 65–80% accuracy in predicting the impact of perturbation patterns. In other words, we can use the increasingly accurate topological models to approximate perturbation patterns, bypassing expensive kinetic constant measurement. These results could open new avenues in modeling drug action and in identifying drug targets relying on the human interactome only.