The ability to create synthetic regulatory networks offers new opportunities for dynamic control of gene expression. Understanding the expression patterns of simple, synthetic gene regulatory networks will not only shed light into the complexity of naturally occurring networks, but it will also provide a platform for expression control that can be valuable in biotechnological applications. The behavior of synthetic regulatory networks depends on specific properties of the regulatory elements chosen, as well as the architectural framework in which they are connected. In addition, their expression dynamics is vastly influenced by the fact that the intracellular environment varies among the cells of a population. Therefore, understanding the relationship between the architecture of synthetic regulatory networks and cell population heterogeneity is of fundamental importance. Furthermore, if the regulatory elements can be influenced extracellularly, this provides an additional level of flexibility in controlling their behavior.

The model regulatory network studied here is composed of three coupled promoter-repressor pairs and is termed "the repressilator” [1]. The components of the network are arranged in a cyclic formation of negative feedback loops, which can display either oscillatory behavior or stable expression of the system. In this study, a plasmid encoding this network and a green fluorescent protein (GFP) reporter gene was inserted into an E. coli host. A series of shake flask experiments were performed while varying inducer conditions, and the GFP levels were monitored over time using flow cytometry, thus providing information about the distribution of fluorescence among the cells of the population as well as the mean expression dynamics. The fluorescence distribution patterns and dynamics that were obtained with anhydrotetracycline (aTc) as the inducer revealed a complex system with three steady states as well as multiplicity between them. This was perplexing because, as designed, this synthetic network should not display multiplicity nor only three states. More flexibility in controlling behavior was demonstrated by adding IPTG in addition to aTc, as IPTG dampened the average fluorescence as well as the coefficient of variation. IPTG also could switch the network from one state to another. These results, and further investigation into the network interactions, will be discussed. Insight gained from the characterization of this system will contribute towards the elucidation of the complex interplay between oscillatory behavior at the single-cell level and the distribution of phenotypes at the cell population level as a function of extracellular conditions.

1. Elowitz, M.B. and S. Leibler, A synthetic oscillatory network of transcriptional regulators. Nature, 2000. 403: p. 335-338.