ArticleScientific reports2017
Controlling Directed Protein Interaction Networks in Cancer.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- In-silico personalized protein-protein interaction networks prioritize candidate compounds for glioblastoma.PLoS computational biology · 2026Article
- Discovering anticancer drug target combinations via network-informed signaling-based approach.Communications medicine · 2025Article
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- A statistical mechanics investigation of unfolded protein response across organisms.Scientific reports · 2024Article
- Anticancer Target Combinations: Network-Informed Signaling-Based Approach to Discovery.bioRxiv : the preprint server for biology · 2024Article
- Progress in mass spectrometry approaches to profiling protein-protein interactions in the studies of the innate immune system.Journal of proteins and proteomics · 2024Article
- Targeting the FSH/FSHR axis in ovarian cancer: advanced treatment using nanotechnology and immunotherapy.Frontiers in endocrinology · 2024Review
- Acyl-CoA Thioesterase 1 Contributes to Transition of Steatosis to Metabolic-Associated Steatohepatitis.International journal of hepatology · 2024Article
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- Cancer-keeper genes as therapeutic targets.iScience · 2023Article
- Total network controllability analysis discovers explainable drugs for Covid-19 treatment.Research square · 2023Article
- Bakdrive: identifying a minimum set of bacterial species driving interactions across multiple microbial communities.Bioinformatics (Oxford, England) · 2023Article
- Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation.Nature communications · 2023Article
- Targeting stressor-induced dysfunctions in protein-protein interaction networks via epichaperomes.Trends in pharmacological sciences · 2023Review
- Article
- An integrated network representation of multiple cancer-specific data for graph-based machine learning.NPJ systems biology and applications · 2022Article
- Network controllability solutions for computational drug repurposing using genetic algorithms.Scientific reports · 2022Article
- Article
- Pharmacologically controlling protein-protein interactions through epichaperomes for therapeutic vulnerability in cancer.Communications biology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Control theory is a well-established approach in network science, with applications in bio-medicine and cancer research. We build on recent results for structural controllability of directed networks, which identifies a set of driver nodes able to control an a-priori defined part of the network. We develop a novel and efficient approach for the (targeted) structural controllability of cancer networks and demonstrate it for the analysis of breast, pancreatic, and ovarian cancer. We build in each case a protein-protein interaction network and focus on the survivability-essential proteins specific to each cancer type. We show that these essential proteins are efficiently controllable from a relatively small computable set of driver nodes. Moreover, we adjust the method to find the driver nodes among FDA-approved drug-target nodes. We find that, while many of the drugs acting on the driver nodes are part of known cancer therapies, some of them are not used for the cancer types analyzed here; some drug-target driver nodes identified by our algorithms are not known to be used in any cancer therapy. Overall we show that a better understanding of the control dynamics of cancer through computational modelling can pave the way for new efficient therapeutic approaches and personalized medicine.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.