Higher-order molecular organization as a source of biological function
Open accessAbstract
Motivation: Molecular interactions have widely been modelled as networks. The local wiring patterns around molecules in molecular networks are linked with their biological functions. However, networks model only pairwise interactions between molecules and cannot explicitly and directly capture the higher-order molecular organization, such as protein complexes and pathways. Hence, we ask if hypergraphs (hypernetworks), that directly capture entire complexes and pathways along with protein-protein interactions (PPIs), carry additional functional information beyond what can be uncovered from networks of pairwise molecular interactions. The mathematical formalism of a hypergraph has long been known, but not often used in studying molecular networks due to the lack of sophisticated algorithms for mining the underlying biological information hidden in the wiring patterns of molecular systems modelled as hypernetworks. Results: We propose a new, multi-scale, protein interaction hypernetwork model that utilizes hypergraphs to capture different scales of protein organization, including PPIs, protein complexes and pathways. In analogy to graphlets, we introduce hypergraphlets, small, connected, non-isomorphic, induced sub-hypergraphs of a hypergraph, to quantify the local wiring patterns of these multi-scale molecular hypergraphs and to mine them for new biological information. We apply them to model the multi-scale protein networks of bakers yeast and human and show that the higher-order molecular organization captured by these hypergraphs is strongly related to the underlying biology. Importantly, we demonstrate that our new models and data mining tools reveal different, but complementary biological information compared with classical PPI networks. We apply our hypergraphlets to successfully predict biological functions of uncharacterized proteins. Availability and implementation: Code and data are available online at http://www0.cs.ucl.ac.uk/staff/natasa/hypergraphlets.
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Gaudelet et al. modeled pairwise protein interactions, complexes, and pathways as multiscale hypernetworks, and analysis of these hypergraphs resulted in determining protein function for many uncharacterized genes.
“For example, Gaudelet et al. modeled pairwise protein interactions, complexes, and pathways as multiscale hypernetworks; analysis of these hypergraphs resulted in determining protein function for many uncharacterized genes (Gaudelet et al., 2018).”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Gaudelet et al., 2018
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Mapping the multiscale structure of biological systems
Schaffer, L. V., & Ideker, T. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.012 · Cited by 75 · see citing works
- Authors
- Gaudelet, T., Malod-Dognin, N., & Pržulj, N.
- Affiliations
- University College London (GB)
- Department of Computer Science, University College London, London, UK
- Corresponding author
- Pržulj, Nataša
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-09-01
- Volume
- 34
- Issue
- 17
- Pages
- i944-i953
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC6129285
- OpenAlex
- W2797439180
- Cited by
- 46
- References
- 51
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 8 · 2023: 9 · 2022: 6 · 2021: 3 · 2020: 6 · 2019: 2 · 2018: 1
- FWCI
- 1.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- arXiv (Cornell University)
- Funders
- UCL Computer Science
- European Research Council
- ERC (770827)
- Starting Independent Researcher (278212)
- Serbian Ministry of Education and Science Project (III44006)
- Slovenian Research Agency
- Farr Institute of Health Informatics Research
- Medical Research Council, Arthritis Research UK
- British Heart Foundation, Cancer Research UK
- Chief Scientist Office
- Economic and Social Research Council
- Engineering and Physical Sciences Research Council
- National Institute for Health Research
- National Institute for Social Care and Health Research
- Wellcome Trust (MR/K006584/1)
- Medical Research Council (MC_U12266B)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- hypergraphs, hypernetworks, protein-protein interaction, protein complexes, biological pathways, protein function prediction, yeast, human
- MeSH terms
- Algorithms, Humans, Proteins, Saccharomyces cerevisiae, Protein Interaction Mapping
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