Environmentally-modulated changes in fluorescence distribution in cells with oscillatory genetic network dynamics

Abstract

We investigated the distribution of green fluorescent protein (GFP) expression levels in a population of E. coli cells expressing an artificial genetic regulatory network, known as the “repressilator”. This network originally constructed by Elowitz and Leibler in 2000 consists of three cyclically-inhibiting promoter–repressor pairs. It is because of this architecture that the network has been known to oscillate at the single-cell level under certain conditions. A series of shake flask experiments were performed and analyzed using flow cytometry to test how cell populations carrying this system could be controlled extracellularly using the inducers anhydrotetracycline (aTc) and isopropyl-β-d-thiogalactopyranoside (IPTG). With variation of [aTc], it exhibits a novel bi-threshold behavior, such that the entire culture reaches one of three steady states at a quasi-time-invariant “reference state.” Also, there is significant hysteresis. Transiently, the middle state shows damping oscillations, while the low and high states show a stable steady state. The addition of IPTG serves to fine-tune the characteristics of the aTc-only expression, lowering the average and coefficient of variation (CV) of the distributions, and possibly perturbing the network to a different state. However, in modeling this system, the multiplicity and bi-threshold behavior are not theoretically possible according to the designed interactions. In order to explain this discrepancy, we hypothesize that one or more of the repressors have a significant nonspecific interaction with a promoter that does not contain its operator site. The new modeling results incorporating these extra interactions qualitatively match our experimental findings. After constructing plasmids to test these hypotheses, we discover that at least four of these interactions exist, which can create the low and high states and multiplicity seen experimentally. This genetic architecture has flexibility in its behavior that has not been demonstrated before, and the combination of experiment and modeling enlightened our understanding of the molecular interactions driving the network's behavior, leading us to discover the significance of nonspecific interactions.