Lastly, we considered whether computational prediction of interactions, based on prior knowledge and data, could hasten the time to interactome completion. A variety of prediction methods have been proposed based on evolutionary conservation39-41-i.e., transfer of interaction measurements from one species to another-or based on integrating conservation with additional features such as co-expression and co-annotation42-47. Such predictions impact the experimental design by setting the prior probabilities of interaction for each protein pair in lieu of background probabilities. In the Prediction strategy, we explored the effect of setting these prior probabilities using theoretical prediction methods simulated over a range of predetermined prediction accuracies (a range of different values for FPR, FNR, and corresponding FDR of the predictions). We found that even predictors with very high FDRs could have a major impact on the mapping cost (Table 1 and Fig. 3b) . For example, a predictor with 92.2% FDR gave a four-fold reduction in cost over Pooling, with a >50-fold reduction in cost to achieve 50% coverage and a savings of hundreds of fold in the early stages of mapping. Moreover, the 92.2% FDR means that even a predictor that makes 12 false predictions for every true one can lead to a major reduction in the cost of interactome completion. The best prediction method required approximately 385 personyears to achieve 95% coverage of the Drosophila protein network and 12 person-years to achieve 50% coverage. Thus, while obtaining full coverage of an interactome map may still be some years away, a draft scaffold providing half coverage might be feasibly achieved by a team of ~12 technicians working over a period of one year.