Key result
sRACIPE 2.0 enhances gene regulatory circuit simulation with comprehensive modeling features and diagnostics.
Why the study?
To provide a unified platform and major update to the R/Bioconductor package for modeling gene regulatory circuits with diverse interaction types using deterministic and stochastic simulations.
sRACIPE 2.0 provides a unified platform for modeling gene regulatory circuits with new features for perturbation, extrinsic signaling, and time-corrected noise.
May enhance cardiovascular gene circuit modeling; leaves open clinical adoption pending validation.
Summary The Random Circuit Perturbation (RACIPE) algorithm enables the exploration of the dynamical behaviors of gene regulatory circuits (GRCs) by simulating an ensemble of differential equation models via randomization of kinetic parameters. Here, we release sRACIPE 2.0, a major update to the R/Bioconductor package, as a unified platform for modeling GRCs with diverse interaction types using both deterministic and stochastic simulations. The update also introduces new features for modeling perturbation, extrinsic signaling and time-corrected noise, and a new diagnostic tool to ensure proper simulations. We hope that this release will serve as a versatile modeling toolkit for the systems biology community. Availability and Implementation The package is available on GitHub at https://github.com/lusystemsbio/sRACIPE under the MIT license. It is also available on Bioconductor at https://www.bioconductor.org/packages/release/bioc/html/sRACIPE.html. The package and test data are also archived on Zenodo at https://doi.org/10.5281/zenodo.18202342 Supplemental Information Supplemental information is available at Bioinformatics online, and specific package structure is described in the package vignettes. Additional vignettes can be found in the GitHub repo https://github.com/dan-ramirez-23/sRACIPE-Demos.
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Tillman et al. (2026) studied this question. sRACIPE 2.0 provides a comprehensive platform for simulating gene regulatory circuits with enhanced modeling features and diagnostics for the systems biology community.
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