ArticleProceedings of the National Academy of Sciences of the United States of America2016
Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers.
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Who cites it
104 citing papers in PubMed.
- In-silico personalized protein-protein interaction networks prioritize candidate compounds for glioblastoma.PLoS computational biology · 2026Article
- Artificial intelligence in drug research and development: a review of methods and applications in drug repurposing.Briefings in bioinformatics · 2026Review
- Network Analysis to Identify MicroRNAs Involved in Alzheimer's Disease and to Improve Drug Prioritization.Biomedicines · 2026Article
- Controllability Analysis of Intercellular Protein-Protein Interaction Networks.Computational and structural biotechnology journal · 2026Article
- Constructing genotype and phenotype network helps reveal disease heritability and phenome-wide association studies.BMC genomics · 2025Article
- Integrative multi-omics framework for causal gene discovery in Long COVID.PLoS computational biology · 2025Article
- Network Controllability Reveals Key Mitigation Points for Tumor-Promoting Signaling in Tumor-Educated Platelets.International journal of molecular sciences · 2025Article
- LogicSR: prior-guided symbolic regression for gene regulatory network inference from single-cell transcriptomics data.Briefings in bioinformatics · 2025Article
- Network controllability analysis reveals the antiviral potential of Etravirine against hepatitis E virus infection.mSystems · 2025Article
- Network-based multi-omics integrative analysis methods in drug discovery: a systematic review.BioData mining · 2025Review
- Article
- DTGHAT: multi-molecule heterogeneous graph transformer based on multi-molecule graph for drug-target identification.Frontiers in pharmacology · 2025Article
- A statistical mechanics investigation of unfolded protein response across organisms.Scientific reports · 2024Article
- Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms.Translational psychiatry · 2024Review
- Network analysis to identify driver genes and combination drugs in brain cancer.Scientific reports · 2024Article
- A practically efficient algorithm for identifying critical control proteins in directed probabilistic biological networks.NPJ systems biology and applications · 2024Article
- Identification and verification of immune-related genes for diagnosing the progression of atherosclerosis and metabolic syndrome.BMC cardiovascular disorders · 2024Article
- Target control of linear directed networks based on the path cover problem.Scientific reports · 2024Article
- LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data.Briefings in bioinformatics · 2024Article
- Measuring criticality in control of complex biological networks.NPJ systems biology and applications · 2024Article
44 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors.
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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 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.
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