Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets
Significance Large scale biological network analyses often use concepts used in social networks analysis (e.g. finding "communities," "hubs," etc. However, mathematically advanced engineering concepts have only been applied to analyze small and well characterized networks so far in biology. Here, we applied a sophisticated engineering tool, from control theory, to analyze a large scale directed human protein protein interaction network. Our analysis revealed that the proteins that are indispensable, from a network controllability perspective, are also commonly targeted by disease causing mutations and human viruses or have been identified as drug targets. Furthermore, we used the controllability analysis to prioritize novel cancer genes from cancer genomic datasets. Altogether, we demonstrated an application of network controllability analysis to identify new disease genes and drug targets. The protein protein interaction (PPI) network is crucial for cellular information processing and decision making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as "indispensable," "neutral," or "dispensable," which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease causing mutations, human viruses, and drugs, suggesting that altering a network's control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets.
Proceedings of the National Academy of Sciences