Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets
arXiv:1511.07768 · doi:10.1073/pnas.1603992113
Abstract
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 comprised of 6,339 proteins and 34,813 interactions. This 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.
arXiv comment31 pages, 4 figures
References in corpus (3)
Cited by in corpus (22)
- Control Principles of Complex Networks
- Structure-based control of complex networks with nonlinear dynamics
- Data-Driven Control of Complex Networks
- Toward Stronger Robustness of Network Controllability: A Snapback Network Model
- Functional observability and target state estimation in large-scale networks
- Controllability and maximum matchings of complex networks
- Identifying and Predicting Parkinson's Disease Subtypes through Trajectory Clustering via Bipartite Networks
- Energy cost for controlling complex networks
- An efficient algorithm for finding all possible input nodes for controlling complex networks
- The evolution of network controllability in growing networks
- Higher-order models capture changes in controllability of temporal networks
- Altering control modes of complex networks based on edge removal
- Altering nodes types in controlling complex networks
- Structural Controllability to Unveil Hidden Regulation Mechanisms in Unfolded Protein Response: the Role of Network Models
- Control contribution identifies top driver nodes in complex networks
- A statistical mechanics investigation of Unfolded Protein Response across organisms
- Robustness of indispensable nodes in controlling protein-protein interaction network
- Empirically Classifying Network Mechanisms
- Fundamental building blocks of controlling complex networks: A universal controllability framework
- Structural Control Analysis of System Dynamics Models
- Structural Controllability of a Consensus Network with Multiple Leaders
- Three-state Majority-Vote Model on Barabási-Albert and Cubic Networks and the Unitary Relation for Critical Exponents