Role of noise and parameter variation in gene circuit dynamics

Stochasticity in gene expression influences the functions and dynamics of gene regulatory circuits. Intrinsic noises, such as those caused by transcriptional bursting and low copy number of molecules, are typically studied by stochastic analysis using Gillespie algorithm and Langevin simulations. Yet, the role of other extrinsic factors, such as the heterogeneity in the microenvironment and cell-to-cell variability, is still elusive. To identify the effects of both intrinsic and extrinsic noises, we integrate stochastic analysis with our newly developed algorithm, named random circuit perturbation (RACIPE). Unlike conventional methods, RACIPE generates and analyzes an ensemble of random models with distinct kinetic parameters. We have shown previously that the expression profiles of stable steady states from random models form robust clusters. Here, we further propose using a constant-noise-based method to capture the basins of attraction and an annealing-based method to identify the most stable states. From the tests on several gene circuits, we found that high intrinsic noises, but not high parameter variations, merge states together. Our study sheds light on a novel mechanism of noise-induced hybrid states.