Evidence map›Paper›PMID 24934175›Full record

ArticleBMC systems biology2014

NetworkViewer: visualizing biochemical reaction networks with embedded rendering of molecular interaction rules.

Hsueh-Chien Cheng, Bastian R Angermann, Fengkai Zhang, Martin Meier-Schellersheim

Abstract read
In one paragraph

Article in BMC systems biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. bnglViz: online visualization of rule-based models.Bioinformatics (Oxford, England) · 2024
    Article
  5. Review
  6. Article
  7. Article
  8. Automated visualization of rule-based models.PLoS computational biology · 2017
    Article
  9. Review
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hsueh-Chien ChengLaboratory of Systems Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Building 4, 4 Memorial Drive, 20892 Bethesda, USA. cheng@cs.umd.edu.
Bastian R Angermann
Fengkai Zhang
Martin Meier-Schellersheim

Funding

Computational Biology in Systems Immunology and Infectious Disease ModelingZIAAI001076 · NIAID · NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES · PI MEIER-SCHELLERSHEIM, MARTIN · 2009 to 2025
$25.4M
Intramural NIH HHS
6 · The paper itself

Abstract

backgroundNetwork representations of cell-biological signaling processes frequently contain large numbers of interacting molecular and multi-molecular components that can exist in, and switch between, multiple biochemical and/or structural states. In addition, the interaction categories (associations, dissociations and transformations) in such networks cannot satisfactorily be mapped onto simple arrows connecting pairs of components since their specifications involve information such as reaction rates and conditions with regard to the states of the interacting components. This leads to the challenge of having to reconcile competing objectives: providing a high-level overview without omitting relevant information, and showing interaction specifics while not overwhelming users with too much detail displayed simultaneously. This problem is typically addressed by splitting the information required to understand a reaction network model into several categories that are rendered separately through combinations of visualizations and/or textual and tabular elements, requiring modelers to consult several sources to obtain comprehensive insights into the underlying assumptions of the model.

resultsWe report the development of an application, the Simmune NetworkViewer, that visualizes biochemical reaction networks using iconographic representations of protein interactions and the conditions under which the interactions take place using the same symbols that were used to specify the underlying model with the Simmune Modeler. This approach not only provides a coherent model representation but, moreover, following the principle of "overview first, zoom and filter, then details-on-demand," can generate an overview visualization of the global network and, upon user request, presents more detailed views of local sub-networks and the underlying reaction rules for selected interactions. This visual integration of information would be difficult to achieve with static network representations or approaches that use scripted model specifications without offering simple but detailed symbolic representations of molecular interactions, their conditions and consequences in terms of biochemical modifications.

conclusionsThe Simmune NetworkViewer provides concise, yet comprehensive visualizations of reaction networks created in the Simmune framework. In the near future, by adopting the upcoming SBML standard for encoding multi-component, multi-state molecular complexes and their interactions as input, the NetworkViewer will, moreover, be able to offer such visualization for any rule-based model that can be exported to that standard.

Indexed as

Computer GraphicsSoftwareBinding SitesModels, BiologicalProtein Interaction Mapping

Identifiers

PMID24934175
PMCPMC4094451

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.