Tuning Promoter Strength through RNA Polymerase Binding Site Design in Escherichia coli
Author(s) -
Robert C. Brewster,
Daniel Jones,
Rob Phillips
Publication year - 2012
Publication title -
plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1002811
Subject(s) - promoter , rna polymerase , repressor , biology , binding site , gene expression , genetics , transcription (linguistics) , microbiology and biotechnology , bacterial transcription , gene , computational biology , rna , linguistics , philosophy
One of the paramount goals of synthetic biology is to have the ability to tune transcriptional networks to targeted levels of expression at will. As a step in that direction, we have constructed a set ofunique binding sites for E. coli RNA Polymerase (RNAP)holoenzyme, designed using a model of sequence-dependent binding energy combined with a thermodynamic model of transcription to produce a targeted level of gene expression. This promoter set allows us to determine the correspondence between the absolute numbers of mRNA molecules or protein products and the predicted promoter binding energies measured inenergy units. These binding sites adhere on average to the predicted level of gene expression overorders of magnitude in constitutive gene expression, to within a factor ofin both protein and mRNA copy number. With these promoters in hand, we then place them under the regulatory control of a bacterial repressor and show that again there is a strict correspondence between the measured and predicted levels of expression, demonstrating the transferability of the promoters to an alternate regulatory context. In particular, our thermodynamic model predicts the expression from our promoters under a range of repressor concentrations between several per cell up to overper cell. After correcting the predicted polymerase binding strength using the data from the unregulated promoter, the thermodynamic model accurately predicts the expression for the simple repression strains to within. Demonstration of modular promoter design, where parts of the circuit (such as RNAP/TF binding strength and transcription factor copy number) can be independently chosen from a stock list and combined to give a predictable result, has important implications as an engineering tool for use in synthetic biology.
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