SUMMARY: Discrete stochastic models of gene regulatory networks are fundamental tools for in silico study of stochastic gene regulatory networks. Likelihood-free inference and model exploration are critical applications to study a system using such models. However, the massive computational cost of complex, high-dimensional and stochastic modelling currently limits systematic investigation to relatively simple systems. Recently, machine-learning-assisted methods have shown great promise to handle larger, more complex models. To support both ease-of-use of this new class of methods, as well as their further development, we have developed the scalable inference, optimization and parameter exploration (Sciope) toolbox. Sciope is designed to support new algorithms for machine-learning-assisted model exploration and likelihood-free inference. Moreover, it is built ground up to easily leverage distributed and heterogeneous computational resources for convenient parallelism across platforms from workstations to clouds. AVAILABILITY AND IMPLEMENTATION: The Sciope Python3 toolbox is freely available on https://github.com/Sciope/Sciope, and has been tested on Linux, Windows and macOS platforms. SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.
SUMMARY: Discrete stochastic models of gene regulatory networks are fundamental tools for in silico study of stochastic gene regulatory networks. Likelihood-free inference and model exploration are critical applications to study a system using such models. However, the massive computational cost of complex, high-dimensional and stochastic modelling currently limits systematic investigation to relatively simple systems. Recently, machine-learning-assisted methods have shown great promise to handle larger, more complex models. To support both ease-of-use of this new class of methods, as well as their further development, we have developed the scalable inference, optimization and parameter exploration (Sciope) toolbox. Sciope is designed to support new algorithms for machine-learning-assisted model exploration and likelihood-free inference. Moreover, it is built ground up to easily leverage distributed and heterogeneous computational resources for convenient parallelism across platforms from workstations to clouds. AVAILABILITY AND IMPLEMENTATION: The Sciope Python3 toolbox is freely available on https://github.com/Sciope/Sciope, and has been tested on Linux, Windows and macOS platforms. SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.
Authors: Richard Jiang; Bruno Jacob; Matthew Geiger; Sean Matthew; Bryan Rumsey; Prashant Singh; Fredrik Wrede; Tau-Mu Yi; Brian Drawert; Andreas Hellander; Linda Petzold Journal: Bioinformatics Date: 2021-01-29 Impact factor: 6.937