Hsiang-Yun WuORCID iD, Martin Nöllenburg, Filipa L. Sousa, Ivan ViolaORCID iD
Metabopolis: Scalable Network Layout for Biological Pathway Diagrams in Urban Map Style
BMC Bioinformatics, 20(187):1-20, May 2019. [paper] [video]

Information

Abstract

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.

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BibTeX

@article{wu-2019-bmc,
  title =      "Metabopolis: Scalable Network Layout for Biological Pathway
               Diagrams in Urban Map Style",
  author =     "Hsiang-Yun Wu and Martin N\"{o}llenburg and Filipa L. Sousa
               and Ivan Viola",
  year =       "2019",
  abstract =   "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.",
  month =      may,
  doi =        "http://doi.org/10.1186/s12859-019-2779-4",
  journal =    "BMC Bioinformatics",
  number =     "187",
  volume =     "20",
  pages =      "1--20",
  keywords =   "Biological pathways, Graph drawing, Mapmetaphor,
               Orthogonallayout, Floorplanning, Edgerouting",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2019/wu-2019-bmc/",
}