Combinatorial Promoter Design for Engineering Noisy Gene Expression

• Published in: Proceedings of the National Academy of Sciences - PNAS 2007-07, Vol.104 (31), p.12726-12731
• Main Authors: Murphy, Kevin F., Balázsi, Gábor, Collins, James J.
• Format: Article
• Language: English
• Subjects: Binding Sites; Biochemistry; Biological Sciences; Computer Simulation; Design engineering; Dose response relationship; Gene expression; Gene Expression - genetics; Genes; Genetic engineering; Genetic Engineering - methods; Mathematical models; Models, Biological; Molecular biology; Physical Sciences; Plasmids; Promoter Regions, Genetic - genetics; Repression; Repressor Proteins - metabolism; Saccharomyces cerevisiae; Signal noise; TATA box; Yeasts
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.0608451104

Understanding the behavior of basic biomolecular components as parts of larger systems is one of the goals of the developing field of synthetic biology. A multidisciplinary approach, involving mathematical and computational modeling in parallel with experimentation, is often crucial for gaining such insights and improving the efficiency of artificial gene network design. Here we used such an approach and developed a combinatorial promoter design strategy to characterize how the position and multiplicity of tetO₂ operator sites within the GAL1 promoter affect gene expression levels and gene expression noise in Saccharomyces cerevisiae. We observed stronger transcriptional repression and higher gene expression noise as a single operator site was moved closer to the TATA box, whereas for multiple operator-containing promoters, we found that the position and number of operator sites together determined the dose-response curve and gene expression noise. We developed a generic computational model that captured the experimentally observed differences for each of the promoters, and more detailed models to successively predict the behavior of multiple operator-containing promoters from single operator-containing promoters. Our results suggest that the independent binding of single repressors is not sufficient to explain the more complex behavior of the multiple operator-containing promoters. Taken together, our findings highlight the importance of joint experimental-computational efforts and some of the challenges of using a bottom-up approach based on well characterized, isolated biomolecular components for predicting the behavior of complex, synthetic gene networks, e.g., the whole can be different from the sum of its parts.