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Metabopolis: scalable network layout for biological pathway diagrams in urban map style
BACKGROUND: Biological pathways represent chains of molecular interactions in biological systems that jointly form complex dynamic networks. The network structure changes from the significance of biological experiments and layout algorithms often sacrifice low-level details to maintain high-level in...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6466808/ https://www.ncbi.nlm.nih.gov/pubmed/30991966 http://dx.doi.org/10.1186/s12859-019-2779-4 |
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author | Wu, Hsiang-Yun Nöllenburg, Martin Sousa, Filipa L. Viola, Ivan |
author_facet | Wu, Hsiang-Yun Nöllenburg, Martin Sousa, Filipa L. Viola, Ivan |
author_sort | Wu, Hsiang-Yun |
collection | PubMed |
description | BACKGROUND: Biological pathways represent chains of molecular interactions in biological systems that jointly form complex dynamic networks. The network structure changes from the significance of biological experiments and layout algorithms often sacrifice low-level details to maintain high-level information, which complicates the entire image to large biochemical systems such as human metabolic pathways. RESULTS: Our work is inspired by concepts from urban planning since we create a visual hierarchy of biological pathways, which is analogous to city blocks and grid-like road networks in an urban area. We automatize the manual drawing process of biologists by first partitioning the map domain into multiple sub-blocks, and then building the corresponding pathways by routing edges schematically, to maintain the global and local context simultaneously. Our system incorporates constrained floor-planning and network-flow algorithms to optimize the layout of sub-blocks and to distribute the edge density along the map domain. We have developed the approach in close collaboration with domain experts and present their feedback on the pathway diagrams based on selected use cases. CONCLUSIONS: We present a new approach for computing biological pathway maps that untangles visual clutter by decomposing large networks into semantic sub-networks and bundling long edges to create space for presenting relationships systematically. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2779-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6466808 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64668082019-04-22 Metabopolis: scalable network layout for biological pathway diagrams in urban map style Wu, Hsiang-Yun Nöllenburg, Martin Sousa, Filipa L. Viola, Ivan BMC Bioinformatics Research Article BACKGROUND: Biological pathways represent chains of molecular interactions in biological systems that jointly form complex dynamic networks. The network structure changes from the significance of biological experiments and layout algorithms often sacrifice low-level details to maintain high-level information, which complicates the entire image to large biochemical systems such as human metabolic pathways. RESULTS: Our work is inspired by concepts from urban planning since we create a visual hierarchy of biological pathways, which is analogous to city blocks and grid-like road networks in an urban area. We automatize the manual drawing process of biologists by first partitioning the map domain into multiple sub-blocks, and then building the corresponding pathways by routing edges schematically, to maintain the global and local context simultaneously. Our system incorporates constrained floor-planning and network-flow algorithms to optimize the layout of sub-blocks and to distribute the edge density along the map domain. We have developed the approach in close collaboration with domain experts and present their feedback on the pathway diagrams based on selected use cases. CONCLUSIONS: We present a new approach for computing biological pathway maps that untangles visual clutter by decomposing large networks into semantic sub-networks and bundling long edges to create space for presenting relationships systematically. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2779-4) contains supplementary material, which is available to authorized users. BioMed Central 2019-04-15 /pmc/articles/PMC6466808/ /pubmed/30991966 http://dx.doi.org/10.1186/s12859-019-2779-4 Text en © The Author(s) 2019 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. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Wu, Hsiang-Yun Nöllenburg, Martin Sousa, Filipa L. Viola, Ivan Metabopolis: scalable network layout for biological pathway diagrams in urban map style |
title | Metabopolis: scalable network layout for biological pathway diagrams in urban map style |
title_full | Metabopolis: scalable network layout for biological pathway diagrams in urban map style |
title_fullStr | Metabopolis: scalable network layout for biological pathway diagrams in urban map style |
title_full_unstemmed | Metabopolis: scalable network layout for biological pathway diagrams in urban map style |
title_short | Metabopolis: scalable network layout for biological pathway diagrams in urban map style |
title_sort | metabopolis: scalable network layout for biological pathway diagrams in urban map style |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6466808/ https://www.ncbi.nlm.nih.gov/pubmed/30991966 http://dx.doi.org/10.1186/s12859-019-2779-4 |
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