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Statecharts for Gene Network Modeling

State diagrams (stategraphs) are suitable for describing the behavior of dynamic systems. However, when they are used to model large and complex systems, determining the states and transitions among them can be overwhelming, due to their flat, unstratified structure. In this article, we present the...

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Detalles Bibliográficos
Autores principales: Shin, Yong-Jun, Nourani, Mehrdad
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2826420/
https://www.ncbi.nlm.nih.gov/pubmed/20186343
http://dx.doi.org/10.1371/journal.pone.0009376
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author Shin, Yong-Jun
Nourani, Mehrdad
author_facet Shin, Yong-Jun
Nourani, Mehrdad
author_sort Shin, Yong-Jun
collection PubMed
description State diagrams (stategraphs) are suitable for describing the behavior of dynamic systems. However, when they are used to model large and complex systems, determining the states and transitions among them can be overwhelming, due to their flat, unstratified structure. In this article, we present the use of statecharts as a novel way of modeling complex gene networks. Statecharts extend conventional state diagrams with features such as nested hierarchy, recursion, and concurrency. These features are commonly utilized in engineering for designing complex systems and can enable us to model complex gene networks in an efficient and systematic way. We modeled five key gene network motifs, simple regulation, autoregulation, feed-forward loop, single-input module, and dense overlapping regulon, using statecharts. Specifically, utilizing nested hierarchy and recursion, we were able to model a complex interlocked feed-forward loop network in a highly structured way, demonstrating the potential of our approach for modeling large and complex gene networks.
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spelling pubmed-28264202010-02-26 Statecharts for Gene Network Modeling Shin, Yong-Jun Nourani, Mehrdad PLoS One Research Article State diagrams (stategraphs) are suitable for describing the behavior of dynamic systems. However, when they are used to model large and complex systems, determining the states and transitions among them can be overwhelming, due to their flat, unstratified structure. In this article, we present the use of statecharts as a novel way of modeling complex gene networks. Statecharts extend conventional state diagrams with features such as nested hierarchy, recursion, and concurrency. These features are commonly utilized in engineering for designing complex systems and can enable us to model complex gene networks in an efficient and systematic way. We modeled five key gene network motifs, simple regulation, autoregulation, feed-forward loop, single-input module, and dense overlapping regulon, using statecharts. Specifically, utilizing nested hierarchy and recursion, we were able to model a complex interlocked feed-forward loop network in a highly structured way, demonstrating the potential of our approach for modeling large and complex gene networks. Public Library of Science 2010-02-23 /pmc/articles/PMC2826420/ /pubmed/20186343 http://dx.doi.org/10.1371/journal.pone.0009376 Text en Shin, Nourani. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Shin, Yong-Jun
Nourani, Mehrdad
Statecharts for Gene Network Modeling
title Statecharts for Gene Network Modeling
title_full Statecharts for Gene Network Modeling
title_fullStr Statecharts for Gene Network Modeling
title_full_unstemmed Statecharts for Gene Network Modeling
title_short Statecharts for Gene Network Modeling
title_sort statecharts for gene network modeling
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2826420/
https://www.ncbi.nlm.nih.gov/pubmed/20186343
http://dx.doi.org/10.1371/journal.pone.0009376
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