Supplementary Table 1. ChIP-chip data normalization. Comparison with the control,

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1 Supplementary Table 1. ChIP-chip data normalization. Comparison with the control, unnormalized binding ratio (BR) data allows us to gauge the performance of the percentile rank (PR) and our P-value (PV) based normalization techniques. Gene-protein interactions in each data representation were classified as bound and unbound according to the threshold rule in column 2, producing a similar number of bound and unbound targets. We measured the total mutual information (TMI) at 356 known protein interactions (Supplementary Table 3) for the three data representations. Moreover, we used the 1637 interactions with the lowest mutual information within the three data representations, to find the TMI at unlikely interactions (termed total noise ). Normalization of the ChIP-chip data using our P-value based method extracts 67% more information at known interactions and also attenuates the noise at unlikely interactions by 12%, outperforming the percentile rank normalization technique. Supplementary Table 2. Significant clusters. Our semi-supervised clustering algorithm uncovers 35 highly significant clusters, 26 of which are confirmed or suggested by the literature. Question marks signify that we could not find any previous studies that tested the interactions within a given cluster. Supplementary Table 3. Test set of 356 interactions from 31 sets confirmed by literature. In the first pass, our network correctly predicts 324 of 356 (91%) of the known interactions at the stringent thresholds of The second pass predicts 20 more known interactions at a threshold of 10-30, increasing the hit rate to 344/356 (97%). Therefore,

2 the second pass reduces false negative predictions in our test set by 6% and introduces very few false positives since the new links interact with members of a complex consistently. Using the more relaxed first and second pass thresholds of /10-15, the algorithm predicts 352 (99%) of the known interactions correctly. However, the more lenient threshold produces 2.5 times more links, therefore increasing the false positive interactions by 2.5 fold. Thus, our choice for the conservative thresholds /10-30 minimizes the false positive predictions in our data, while still capturing 97% of the known interactions (or 3% false negative rate). Supplementary Table 4. Genome-wide binding of Sir2 in glucose-rich and alpha-factor media and of Esc1 in glucose-rich media across open reading frames (ORFs) in yeast genome. ChIP-chip was performed as previously described by Casolari et al. 1 Data is attached in file SupTable4.xls. Supplementary Table 5. Network Predictions. File SupTable5.xls includes all significant synergistic (positive) binding relationships, as designated by <+>, and opposing (negative) interactions, as designated by <->, between pairs of regulators across all levels. Supplementary Table 6. Unified ChIP-chip datasets. We integrated ChIP-chip data from several different laboratories using different microarray platforms and statistical

3 analyses. File SupTable6.zip includes the integrated, unnormalized dataset of binding ratios and the normalized dataset of P-values and standard deviations. Supplementary Table 7. Choosing gene-protein bound/unbound threshold. We learned from Supplementary Table 1 that the normalized PV data representation contains the most information at known interactions compared to other representations. In order to choose an appropriate threshold for classifying bound and unbound gene-protein interactions, we considered 5 commonly used thresholds for PV data (first column). The second column shows that less stringent thresholds produce more bound targets (unlike Supplementary Table 1, the number of bound targets differ significantly here). Since more bound targets increase the entropy of our data, the mutual information (or entropy reduction) at 356 known interactions increases proportionately to bound targets (third column). To rectify this, we measured the mutual information normalized by the maximum entropy of the two binary vectors, which corresponds to the fraction of entropy reduction and cannot exceed 1. We chose a bound/unbound threshold of 0.01 for our data, since it maximizes the normalized mutual information (last column). Moreover, the table shows that other common thresholds extract only about 20% less information, demonstrating that our results/conclusions are not sensitive to the chosen threshold. Supplementary Table 8. List of active and inactive factors used in our study of network adaptivity.

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