Cargando…

Automated visualization of rule-based models

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Sekar, John Arul Prakash, Tapia, Jose-Juan, Faeder, James R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5703574/
https://www.ncbi.nlm.nih.gov/pubmed/29131816
http://dx.doi.org/10.1371/journal.pcbi.1005857
_version_ 1783281708814041088
author Sekar, John Arul Prakash
Tapia, Jose-Juan
Faeder, James R.
author_facet Sekar, John Arul Prakash
Tapia, Jose-Juan
Faeder, James R.
author_sort Sekar, John Arul Prakash
collection PubMed
description 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.
format Online
Article
Text
id pubmed-5703574
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-57035742017-12-08 Automated visualization of rule-based models Sekar, John Arul Prakash Tapia, Jose-Juan Faeder, James R. PLoS Comput Biol Research Article 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. Public Library of Science 2017-11-13 /pmc/articles/PMC5703574/ /pubmed/29131816 http://dx.doi.org/10.1371/journal.pcbi.1005857 Text en © 2017 Sekar et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sekar, John Arul Prakash
Tapia, Jose-Juan
Faeder, James R.
Automated visualization of rule-based models
title Automated visualization of rule-based models
title_full Automated visualization of rule-based models
title_fullStr Automated visualization of rule-based models
title_full_unstemmed Automated visualization of rule-based models
title_short Automated visualization of rule-based models
title_sort automated visualization of rule-based models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5703574/
https://www.ncbi.nlm.nih.gov/pubmed/29131816
http://dx.doi.org/10.1371/journal.pcbi.1005857
work_keys_str_mv AT sekarjohnarulprakash automatedvisualizationofrulebasedmodels
AT tapiajosejuan automatedvisualizationofrulebasedmodels
AT faederjamesr automatedvisualizationofrulebasedmodels