The ability to construct synthetic gene networks enables detailed experimental investigations of simplified systems that can be compared to qualitative and quantitative models. If simple, well-characterized modules can be coupled together into more complex networks whose behavior is then predictable from that of the components, we may begin to build an understanding of cellular regulatory processes from the bottom up. Here, we engineered a promoter to allow simultaneous repression and activation of gene expression in Escherichia coli, and studied its behavior, in synthetic gene networks, under increasingly complex conditions: unregulated, repressed, activated, and simultaneously repressed and activated. We developed a stochastic model that quantitatively captures the means and distributions of the expression from the engineered promoter of this modular system, and showed that the model can accurately predict the in vivo behavior of the network when it is expanded to include positive feedback. The model also reveals the counterintuitive prediction that noise in protein expression levels can increase upon arrest of cell growth and division, which we confirmed experimentally. This work shows that one can indeed use the properties of regulatory subsystems to predict the behavior of larger, more complex regulatory networks, and that this bottom-up approach can provide novel insights into gene regulation.
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