Novel Bioengineering Technique for Genome-Scale Tuning of Gene Expression

The Science

The inability to predict heterologous gene expression levels precisely hinders the ability to engineer biological systems. Using well-characterized regulatory elements offers a potential solution only if such elements behave predictably when combined. Researchers synthesized 12,563 combinations of common promoters and ribosome binding sites and simultaneously measured DNA, RNA, and protein levels from the entire library. Using a simple model, they found that RNA and protein expression were within twofold of expected levels 80% and 64% of the time, respectively. The large dataset allowed quantitation of global effects, such as translation rate on mRNA stability and mRNA secondary structure on translation rate. However, the worst 5% of constructs deviated from prediction by 13-fold on average, which could hinder large-scale genetic engineering projects. The ease and scale this of approach indicates that rather than relying on prediction or standardization, researchers can screen synthetic libraries for desired behavior.


Introduction of new genes encoding desired functional attributes has long been a central tool for metabolic engineering and synthetic biodesign of microorganisms. However, difficulties in accurately predicting the expression levels of these genes in their new hosts significantly slow the design cycle and hinder progress. This is particularly problematic in synthetic biology, where large genetic constructs containing multiple genes are often introduced. Now researchers present a novel technique to more accurately predict gene expression levels in engineered biosystems by combining recent advances in DNA synthesis with novel, multiplexed methods for measuring DNA, RNA, and protein levels simultaneously using next-generation sequencing. This new technique allowed the team to simultaneously measure transcription and translation rates of thousands of synthetic regulatory elements introduced into the model microbe Escherichia coli . The resulting dataset was then used to model gene and protein expression levels under various sets of regulatory elements and “compose” a designed regulatory strategy that enables accurate prediction of expression levels of introduced genetic elements. This new technique has the potential to allow much more sophisticated forward design of genetic engineering strategies to improve production of biofuels and other bioproducts.

BER Program Manager

Dawn Adin

U.S. Department of Energy, Biological and Environmental Research (SC-33)
Biological Systems Science Division
[email protected]


Kosuri, S. D. B. Goodman, G. Cambray, V. K. Mutalik, Y. Gao, A. P. Arkin, D. Endy, and G. M. Church. 2013. “Composability of Regulatory Sequences Controlling Transcription and Translation in Escherichia coli,” Proceedings of the National Academy of Sciences USA 110 , 14024–29. DOI:10.1073/pnas.1301301110.