Scalable machine learning-assisted model exploration and inference using Sciope

Abstract

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.