ArticleScientific reports2017
Identification of Topological Network Modules in Perturbed Protein Interaction Networks.
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 10 papers.
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Who cites it
10 citing papers in PubMed.
- Integrative approaches for predicting protein network perturbations through machine learning and structural characterization.Journal of proteomics · 2025Article
- Identification of stem cells from large cell populations with topological scoring.Molecular omics · 2021Article
- Modularity in Biological Networks.Frontiers in genetics · 2021Review
- The chromatin bound proteome of the human malaria parasite.Microbial genomics · 2020Article
- Topological scoring of protein interaction networks.Nature communications · 2019Article
- Modeling and interpreting mesoscale network dynamics.NeuroImage · 2018Review
- Article
- Malaria intensity in Colombia by regions and populations.PloS one · 2018Article
- Genome maintenance functions of the INO80 chromatin remodeller.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017Review
- Unravelling the biology of chromatin in health and cancer using proteomic approaches.Expert review of proteomics · 2017Review
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Authors and funding
5 authors.
Funding
Abstract
Biological networks consist of functional modules, however detecting and characterizing such modules in networks remains challenging. Perturbing networks is one strategy for identifying modules. Here we used an advanced mathematical approach named topological data analysis (TDA) to interrogate two perturbed networks. In one, we disrupted the S. cerevisiae INO80 protein interaction network by isolating complexes after protein complex components were deleted from the genome. In the second, we reanalyzed previously published data demonstrating the disruption of the human Sin3 network with a histone deacetylase inhibitor. Here we show that disrupted networks contained topological network modules (TNMs) with shared properties that mapped onto distinct locations in networks. We define TMNs as proteins that occupy close network positions depending on their coordinates in a topological space. TNMs provide new insight into networks by capturing proteins from different categories including proteins within a complex, proteins with shared biological functions, and proteins disrupted across networks.
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