RACIPE: a computational tool for modeling gene regulatory circuits using randomization

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dc.citation.articleNumber74en_US
dc.citation.journalTitleBMC Systems Biologyen_US
dc.citation.volumeNumber12en_US
dc.contributor.authorHuang, Binen_US
dc.contributor.authorJia, Dongyaen_US
dc.contributor.authorFeng, Jingchenen_US
dc.contributor.authorLevine, Herberten_US
dc.contributor.authorOnuchic, José Nelsonen_US
dc.contributor.authorLu, Mingyangen_US
dc.contributor.orgCenter for Theoretical Biological Physicsen_US
dc.date.accessioned2018-09-26T14:52:43Zen_US
dc.date.available2018-09-26T14:52:43Zen_US
dc.date.issued2018en_US
dc.description.abstractBACKGROUND: One of the major challenges in traditional mathematical modeling of gene regulatory circuits is the insufficient knowledge of kinetic parameters. These parameters are often inferred from existing experimental data and/or educated guesses, which can be time-consuming and error-prone, especially for large networks. RESULTS: We present a user-friendly computational tool for the community to use our newly developed method named random circuit perturbation (RACIPE), to explore the robust dynamical features of gene regulatory circuits without the requirement of detailed kinetic parameters. Taking the network topology as the only input, RACIPE generates an ensemble of circuit models with distinct randomized parameters and uniquely identifies robust dynamical properties by statistical analysis. Here, we discuss the implementation of the software and the statistical analysis methods of RACIPE-generated data to identify robust gene expression patterns and the functions of genes and regulatory links. Finally, we apply the tool on coupled toggle-switch circuits and a published circuit of B-lymphopoiesis. CONCLUSIONS: We expect our new computational tool to contribute to a more comprehensive and unbiased understanding of mechanisms underlying gene regulatory networks. RACIPE is a free open source software distributed under (Apache 2.0) license and can be downloaded from GitHub ( https://github.com/simonhb1990/RACIPE-1.0 ).en_US
dc.identifier.citationHuang, Bin, Jia, Dongya, Feng, Jingchen, et al.. "RACIPE: a computational tool for modeling gene regulatory circuits using randomization." <i>BMC Systems Biology,</i> 12, (2018) Springer Nature: https://doi.org/10.1186/s12918-018-0594-6.en_US
dc.identifier.digitals12918-018-0594-6en_US
dc.identifier.doihttps://doi.org/10.1186/s12918-018-0594-6en_US
dc.identifier.urihttps://hdl.handle.net/1911/102715en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.rightsThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.subject.keyworddynamical featuresen_US
dc.subject.keywordGRNsen_US
dc.subject.keywordgene regulatory circuitsen_US
dc.subject.keywordRACIPEen_US
dc.subject.keywordrandom circuit perturbationen_US
dc.subject.keywordstatistical analysisen_US
dc.titleRACIPE: a computational tool for modeling gene regulatory circuits using randomizationen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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