Evidence map›Paper›PMID 29131816›Full record

ArticlePLoS computational biology2017

Automated visualization of rule-based models.

John Arul Prakash Sekar, Jose-Juan Tapia, James R Faeder

Abstract read
In one paragraph

Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. bnglViz: online visualization of rule-based models.Bioinformatics (Oxford, England) · 2024
    Article
  4. Article
  5. Article
  6. MCell-R: A Particle-Resolution Network-Free Spatial Modeling Framework.Methods in molecular biology (Clifton, N.J.) · 2019
    Article
  7. Article
  8. The Kappa platform for rule-based modeling.Bioinformatics (Oxford, England) · 2018
    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

3 authors.

John Arul Prakash SekarDepartment of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, United States of America.ORCID http://orcid.org/0000-0002-1605-8130
Jose-Juan TapiaDepartment of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, United States of America.
James R FaederDepartment of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, United States of America.

Funding

Regulation of Cardiolin Byosynthesis in Epithelial InjuryP01HL114453 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI MALLAMPALLI, RAMA K · 2014 to 2023
$21.3M
TrainingP41GM103712 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI FAEDER, JAMES · 2012 to 2021
$16.3M
WESTPA: A high-performance framework for simulating at the frontiers of biologyR01GM115805 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI LILLIAN T CHONG · 2015 to 2026
$4.0M
Negative Control of IL-17R Signaling: Implications for Fungal ImmunityR01AI107825 · NIAID · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI GAFFEN, SARAH L · 2014 to 2018
$1.9M
NHLBI NIH HHS P01 HL114453NIAID NIH HHS R01 AI107825NIGMS NIH HHS P41 GM103712NIGMS NIH HHS R01 GM115805
6 · The paper itself

Abstract

Frameworks such as BioNetGen, Kappa and Simmune use "reaction rules" to specify biochemical interactions compactly, where each rule specifies a mechanism such as binding or phosphorylation and its structural requirements. Current rule-based models of signaling pathways have tens to hundreds of rules, and these numbers are expected to increase as more molecule types and pathways are added. Visual representations are critical for conveying rule-based models, but current approaches to show rules and interactions between rules scale poorly with model size. Also, inferring design motifs that emerge from biochemical interactions is an open problem, so current approaches to visualize model architecture rely on manual interpretation of the model. Here, we present three new visualization tools that constitute an automated visualization framework for rule-based models: (i) a compact rule visualization that efficiently displays each rule, (ii) the atom-rule graph that conveys regulatory interactions in the model as a bipartite network, and (iii) a tunable compression pipeline that incorporates expert knowledge and produces compact diagrams of model architecture when applied to the atom-rule graph. The compressed graphs convey network motifs and architectural features useful for understanding both small and large rule-based models, as we show by application to specific examples. Our tools also produce more readable diagrams than current approaches, as we show by comparing visualizations of 27 published models using standard graph metrics. We provide an implementation in the open source and freely available BioNetGen framework, but the underlying methods are general and can be applied to rule-based models from the Kappa and Simmune frameworks also. We expect that these tools will promote communication and analysis of rule-based models and their eventual integration into comprehensive whole-cell models.

Indexed as

Computational BiologyModels, BiologicalAlgorithmsData CompressionHumansImage Processing, Computer-AssistedSignal Transduction

Identifiers

PMID29131816
PMCPMC5703574

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.