Bayesian Inference of Gene Regulatory Networks at Stochastic Steady State.

ABSTRACT: Gene Regulatory Networks (GRNs) form the regulatory backbone that coordinates gene expression. The architecture of GRNs shapes their function and constraints the biochemical pathways through which information flows. Inferring the structure of regulatory interactions is thus essential for understanding biological systems, and designing targeted therapies. Despite substantial progress in GRN inference, most approaches - from statistical methods to deep learning - do not take into account fundamental biochemical processes that drive regulatory dynamics. To address this shortcoming, here we present a novel Bayesian inference approach based on using the Chemical Langevin Equation (CLE) as a model of gene expression dynamics at stochastic equilibrium. Interactions in GRNs are sparse, and we thus

SUBMITTER: Gupta A 

PROVIDER: S-EPMC12879671 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature