Bayesian learning of nonlinear gene regulatory networks with switching architectures

Abstract

Introduction: Gene regulatory networks (GRNs) are characterized by their dynamism, meaning that the regulatory interactions which constitute these networks evolve with time. Identifying when changes in the GRN architecture occur can inform our understanding of fundamental biological processes, such as disease manifestation, development, and evolution. However, it is usually not possible to know a priori when a change in the network architecture will occur. Furthermore, an architectural shift may alter the underlying noise characteristics, such as the process noise covariance.Methods: We develop a fully Bayesian hierarchical model to address the following: a) sudden changes in the network architecture; b) unknown process noise covariance which may change along with the network structure; and c) unknown measurement noise covariance. We exploit the use of conjugate priors to develop an analytically tractable inference scheme using Bayesian sequential Monte Carlo (SMC) with a local Gibbs sampler.Results: Our Bayesian learning algorithm effectively estimates time-varying gene expression levels and architectural model indicators under varying noise conditions. It accurately captures sudden changes in network architecture and accounts for time-evolving process and measurement noise characteristics. Our algorithm performs well even under high noise conditions. By incorporating conjugate priors, we achieve analytical tractability, enabling robust inference despite the inherent complexities of the system. Furthermore, our method outperforms the standard particle filter in all test scenarios.Discussion: The results underscore our method’s efficacy in capturing architectural changes in GRNs. Its ability to adapt to a range of time-evolving noise conditions emphasizes its practical relevance for real-world biological data, where noise presents a significant challenge. Overall, our method provides a powerful tool for studying the dynamics of GRNs and has the potential to advance our understanding of fundamental biological processes.