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Geometric characterisation of disease modules
There is an increasing accumulation of evidence supporting the existence of a hyperbolic geometry underlying the network representation of complex systems. In particular, it has been shown that the latent geometry of the human protein network (hPIN) captures biologically relevant information, leadin...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214295/ https://www.ncbi.nlm.nih.gov/pubmed/30839777 http://dx.doi.org/10.1007/s41109-018-0066-3 |
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author | Härtner, Franziska Andrade-Navarro, Miguel A. Alanis-Lobato, Gregorio |
author_facet | Härtner, Franziska Andrade-Navarro, Miguel A. Alanis-Lobato, Gregorio |
author_sort | Härtner, Franziska |
collection | PubMed |
description | There is an increasing accumulation of evidence supporting the existence of a hyperbolic geometry underlying the network representation of complex systems. In particular, it has been shown that the latent geometry of the human protein network (hPIN) captures biologically relevant information, leading to a meaningful visual representation of protein-protein interactions and translating challenging systems biology problems into measuring distances between proteins. Moreover, proteins can efficiently communicate with each other, without global knowledge of the hPIN structure, via a greedy routing (GR) process in which hyperbolic distances guide biological signals from source to target proteins. It is thanks to this effective information routing throughout the hPIN that the cell operates, communicates with other cells and reacts to environmental changes. As a result, the malfunction of one or a few members of this intricate system can disturb its dynamics and derive in disease phenotypes. In fact, it is known that the proteins associated with a single disease agglomerate non-randomly in the same region of the hPIN, forming one or several connected components known as the disease module (DM). Here, we present a geometric characterisation of DMs. First, we found that DM positions on the two-dimensional hyperbolic plane reflect their fragmentation and functional heterogeneity, rendering an informative picture of the cellular processes that the disease is affecting. Second, we used a distance-based dissimilarity measure to cluster DMs with shared clinical features. Finally, we took advantage of the GR strategy to study how defective proteins affect the transduction of signals throughout the hPIN. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s41109-018-0066-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6214295 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-62142952018-11-13 Geometric characterisation of disease modules Härtner, Franziska Andrade-Navarro, Miguel A. Alanis-Lobato, Gregorio Appl Netw Sci Research There is an increasing accumulation of evidence supporting the existence of a hyperbolic geometry underlying the network representation of complex systems. In particular, it has been shown that the latent geometry of the human protein network (hPIN) captures biologically relevant information, leading to a meaningful visual representation of protein-protein interactions and translating challenging systems biology problems into measuring distances between proteins. Moreover, proteins can efficiently communicate with each other, without global knowledge of the hPIN structure, via a greedy routing (GR) process in which hyperbolic distances guide biological signals from source to target proteins. It is thanks to this effective information routing throughout the hPIN that the cell operates, communicates with other cells and reacts to environmental changes. As a result, the malfunction of one or a few members of this intricate system can disturb its dynamics and derive in disease phenotypes. In fact, it is known that the proteins associated with a single disease agglomerate non-randomly in the same region of the hPIN, forming one or several connected components known as the disease module (DM). Here, we present a geometric characterisation of DMs. First, we found that DM positions on the two-dimensional hyperbolic plane reflect their fragmentation and functional heterogeneity, rendering an informative picture of the cellular processes that the disease is affecting. Second, we used a distance-based dissimilarity measure to cluster DMs with shared clinical features. Finally, we took advantage of the GR strategy to study how defective proteins affect the transduction of signals throughout the hPIN. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s41109-018-0066-3) contains supplementary material, which is available to authorized users. Springer International Publishing 2018-06-18 2018 /pmc/articles/PMC6214295/ /pubmed/30839777 http://dx.doi.org/10.1007/s41109-018-0066-3 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Härtner, Franziska Andrade-Navarro, Miguel A. Alanis-Lobato, Gregorio Geometric characterisation of disease modules |
title | Geometric characterisation of disease modules |
title_full | Geometric characterisation of disease modules |
title_fullStr | Geometric characterisation of disease modules |
title_full_unstemmed | Geometric characterisation of disease modules |
title_short | Geometric characterisation of disease modules |
title_sort | geometric characterisation of disease modules |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214295/ https://www.ncbi.nlm.nih.gov/pubmed/30839777 http://dx.doi.org/10.1007/s41109-018-0066-3 |
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