BMC Syst Biol 2018 Jun 19; 12(1):74.
BACKGROUND: 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 ).
Huang, Bin; Jia, Dongya; Feng, Jingchen; Levine, Herbert; Onuchic, José N; and Lu, Mingyang, "RACIPE: a computational tool for modeling gene regulatory circuits using randomization." (2018). Faculty Research 2018. 136.
Mingyang Lu is supported by the start-up fund from the Jackson Laboratory.
This open access article is licensed under a Creative Commons Attribution 4.0 International License.