Emergence of power law distributions in protein-protein interaction networks through study bias
Degree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study biases affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait prot...
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2024-12-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/99951 |
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| author | David B Blumenthal Marta Lucchetta Linda Kleist Sándor P Fekete Markus List Martin H Schaefer |
| author_facet | David B Blumenthal Marta Lucchetta Linda Kleist Sándor P Fekete Markus List Martin H Schaefer |
| author_sort | David B Blumenthal |
| collection | DOAJ |
| description | Degree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study biases affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait proteins in large-scale experiments tend to have many false-positive interaction partners. Studying the degree distributions of thousands of PPI networks of controlled provenance, we address the question if PL distributions in observed PPI networks could be explained by these biases alone. Our findings are supported by mathematical models and extensive simulations, and indicate that study bias and technical bias suffice to produce the observed PL distribution. It is, hence, problematic to derive hypotheses about the topology of the true biological interactome from the PL distributions in observed PPI networks. Our study casts doubt on the use of the PL property of biological networks as a modeling assumption or quality criterion in network biology. |
| format | Article |
| id | doaj-art-eb931e3a8e6048f69052ff64f5d88dfb |
| institution | Kabale University |
| issn | 2050-084X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj-art-eb931e3a8e6048f69052ff64f5d88dfb2025-01-09T16:16:54ZengeLife Sciences Publications LtdeLife2050-084X2024-12-011310.7554/eLife.99951Emergence of power law distributions in protein-protein interaction networks through study biasDavid B Blumenthal0https://orcid.org/0000-0001-8651-750XMarta Lucchetta1Linda Kleist2Sándor P Fekete3Markus List4https://orcid.org/0000-0002-0941-4168Martin H Schaefer5https://orcid.org/0000-0001-7503-6364Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, GermanyDepartment of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, ItalyDepartment of Computer Science, TU Braunschweig, Braunschweig, GermanyDepartment of Computer Science, TU Braunschweig, Braunschweig, Germany; Braunschweig Integrated Centre of Systems Biology (BRICS), Braunschweig, GermanyData Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Freising, Germany; Munich Data Science Institute (MDSI), Technical University of Munich, Garching, GermanyDepartment of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, ItalyDegree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study biases affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait proteins in large-scale experiments tend to have many false-positive interaction partners. Studying the degree distributions of thousands of PPI networks of controlled provenance, we address the question if PL distributions in observed PPI networks could be explained by these biases alone. Our findings are supported by mathematical models and extensive simulations, and indicate that study bias and technical bias suffice to produce the observed PL distribution. It is, hence, problematic to derive hypotheses about the topology of the true biological interactome from the PL distributions in observed PPI networks. Our study casts doubt on the use of the PL property of biological networks as a modeling assumption or quality criterion in network biology.https://elifesciences.org/articles/99951protein-protein interaction networkspower law distributionsstudy bias |
| spellingShingle | David B Blumenthal Marta Lucchetta Linda Kleist Sándor P Fekete Markus List Martin H Schaefer Emergence of power law distributions in protein-protein interaction networks through study bias eLife protein-protein interaction networks power law distributions study bias |
| title | Emergence of power law distributions in protein-protein interaction networks through study bias |
| title_full | Emergence of power law distributions in protein-protein interaction networks through study bias |
| title_fullStr | Emergence of power law distributions in protein-protein interaction networks through study bias |
| title_full_unstemmed | Emergence of power law distributions in protein-protein interaction networks through study bias |
| title_short | Emergence of power law distributions in protein-protein interaction networks through study bias |
| title_sort | emergence of power law distributions in protein protein interaction networks through study bias |
| topic | protein-protein interaction networks power law distributions study bias |
| url | https://elifesciences.org/articles/99951 |
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