ArticleProceedings of the National Academy of Sciences of the United States of America2007
Combinatorial promoter design for engineering noisy gene expression.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers.
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115 citing papers in PubMed, 217 citations in OpenAlex.
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- Natural promoters and promoter engineering strategies for metabolic regulation in Saccharomyces cerevisiae.Journal of industrial microbiology & biotechnology · 2023Review
- Promoter-proximal introns impact recombinant amylase expression in Saccharomyces cerevisiae.FEMS yeast research · 2023Article
- Engineering a two-gene system to operate as a highly sensitive biosensor or a sharp switch upon induction with β-estradiol.Scientific reports · 2022Article
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- Principles for the design of multicellular engineered living systems.APL bioengineering · 2022Article
- Independent control of mean and noise by convolution of gene expression distributions.Nature communications · 2021Article
- Competitive dCas9 binding as a mechanism for transcriptional control.Molecular systems biology · 2021Article
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3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
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 tetO(2) 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.
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