1. Department of Biomedical Engineering, Bioinformatics Program, Center for BioDynamics and Center for Advanced Biotechnology, Boston University, 44 Cummington Street, Boston, Massachusetts 02215, USA
2. Department of Statistics and Operations Research,
3. Department of Mathematics,
4. Department of Pharmacology, University of North Carolina, Chapel Hill, North Carolina 27599, USA
5. Institute for Optical Sciences and Department of Chemical and Physical Sciences, University of Toronto at Mississauga, 3359 Mississauga Rd, Mississauga, Ontario L5L 1C6 Canada
6. Department of Bioengineering, University of California, San Diego, 9500 Gillman Drive, La Jolla, California 92093, USA
7. *These authors contributed equally to this work

Correspondence to: J. J. Collins¹ Correspondence and requests for materials should be addressed to J.J.C. (Email: jcollins@bu.edu).

Abstract

The ability to construct synthetic gene networks enables experimental investigations of deliberately simplified systems that can be compared to qualitative and quantitative models^{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}. If simple, well-characterized modules can be coupled together into more complex networks with behaviour that can be predicted from that of the individual components, we may begin to build an understanding of cellular regulatory processes from the 'bottom up'. Here we have engineered a promoter to allow simultaneous repression and activation of gene expression in Escherichia coli. We studied its behaviour in synthetic gene networks under increasingly complex conditions: unregulated, repressed, activated, and simultaneously repressed and activated. We develop a stochastic model that quantitatively captures the means and distributions of the expression from the engineered promoter of this modular system, and show that the model can be extended and used to accurately predict the in vivo behaviour 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 confirm experimentally. This work shows that the properties of regulatory subsystems can be used to predict the behaviour of larger, more complex regulatory networks, and that this bottom-up approach can provide insights into gene regulation.

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