Huang, BinJia, DongyaFeng, JingchenLevine, HerbertOnuchic, José NelsonLu, Mingyang2018-09-262018-09-262018Huang, 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.https://hdl.handle.net/1911/102715BACKGROUND: 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 ).engThis 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.Journal articledynamical featuresGRNsgene regulatory circuitsRACIPErandom circuit perturbationstatistical analysiss12918-018-0594-6https://doi.org/10.1186/s12918-018-0594-6