Improvement of the memory function by negative autoregulation of a mutual repression network in a stochastic environment

Abstract Cellular memory is a ubiquitous function of biological systems. By generating a sustained response to a transient inductive stimulus, often due to bistability, memory is central to the robust control of many important biological functions. However, our understanding of the mechanistic basis of cellular memory remains incomplete. Specifically, stochastic fluctuations that are inherent to most biological systems have been shown to hamper memory function. Yet, how stochasticity changes the behavior of genetic circuits is generally not clear from a deterministic analysis of the network alone. Here, we apply deterministic, stochastic, and theoretical analyses to investigate how intrinsic noise affects the memory function in a mutual repression network. We find that the addition of negative autoregulation improves the persistence of memory by reducing stochastic fluctuations. Our theoretical analyses reveal that this improved memory function stems from an increased stability of the steady states of the system. Moreover, we show how the tuning of critical network parameters can further enhance memory. Our work highlights the power of stochastic and theoretical approaches to understanding biological circuits, and the importance of considering stochasticity to designing synthetic circuits with memory function.