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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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Formato: | Texto |
Lenguaje: | English |
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Public Library of Science
2010
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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. |
format | Text |
id | pubmed-2826420 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT shinyongjun statechartsforgenenetworkmodeling AT nouranimehrdad statechartsforgenenetworkmodeling |