Speaker
Description
Gene expression is inherently stochastic, leading to fluctuations in protein levels that can influence cellular function. Negative autoregulatory feedback is a common regulatory motif that can suppress these fluctuations and stabilize gene expression, but its effects can depend strongly on additional cellular processes. We investigate how cell-cycle dynamics influence the noise-reduction properties of negative feedback loops by first considering a framework where cell growth and division are modeled through the effective dilution of proteins. We then extend this framework by introducing an explicit model of the cell cycle that accounts for cell growth, division, and molecular partitioning. Our results demonstrate that explicitly modelling the cell cycle can qualitatively alter noise behavior: depending on parameter regimes, the cell cycle can either amplify or further suppress fluctuations compared to the effective model. These results highlight how model structure influences the relationship between underlying biochemical mechanisms and measurable variability, with direct implications for inference from gene expression data. Finally, we explore how different cell-size regulation strategies—such as sizer, timer, and adder mechanisms—affect noise in protein expression.
