Cell phenotype dynamic homeostasis contrasts with the inherent randomness of intracellular reactions. Although feedback control of regulator genes (RG) is a key strategy for limiting the range of downstream gene expression, understanding the quantitative constraints and corresponding mechanisms enabling such a dynamic stability under noise remains elusive. Here we model RG expression as a stochastic process and downstream genes as sensors whose responses conditionally induce RG activity. We show that at homeostatic regime: i. the trajectories of the RG expression levels can be adjusted towards specific ranges using both the exact solutions of the stochastic model and the exact stochastic simulation algorithm (SSA); ii. there exists a sampling rate which optimizes the feedback control of the RG activity, and non-optimal controls resulting in alternative homeostatic dynamics; iii. the feedback control of RG activity leads to updates whose intensities and time intervals are non-linearly related; iv. the ON state probability of an RG promoter has dynamics confined within a narrow domain. Our results help to understand the quantitative constraints underpinning dynamic homeostasis despite randomness, the mechanisms underlying alternative, non-optimal, homeostatic regimes, and may be useful for theoretically prototyping therapies aiming at gene network modulation. • Control of noisy gene expression enables dynamic homeostasis of cellular phenotype. • Control using exact stochastic model solutions matches simulation trajectories. • Rate of sampling of transcript optimizes gene network feedback effects. • Tuning sampling/degradation rates enables alternative dynamic homeostatic phenotypes.
Giovanini et al. (Fri,) studied this question.