Tuning promoter strength through RNA polymerase binding site design in Escherichia coli.
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 of 18 unique binding sites for E. coli RNA Polymerase (RNAP) δ⁷⁰ holoenzyme, designed using a model...
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Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002811&type=printable |
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| author | Robert C Brewster Daniel L Jones Rob Phillips |
| author_facet | Robert C Brewster Daniel L Jones Rob Phillips |
| author_sort | Robert C Brewster |
| collection | DOAJ |
| description | 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 of 18 unique binding sites for E. coli RNA Polymerase (RNAP) δ⁷⁰ 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 in k(B)T energy units. These binding sites adhere on average to the predicted level of gene expression over 3 orders of magnitude in constitutive gene expression, to within a factor of 3 in 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 over 100 per 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 30%. 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. |
| format | Article |
| id | doaj-art-e596dbff6b834326b1992cc1ad0d7193 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-e596dbff6b834326b1992cc1ad0d71932025-08-20T02:15:28ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582012-01-01812e100281110.1371/journal.pcbi.1002811Tuning promoter strength through RNA polymerase binding site design in Escherichia coli.Robert C BrewsterDaniel L JonesRob PhillipsOne 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 of 18 unique binding sites for E. coli RNA Polymerase (RNAP) δ⁷⁰ 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 in k(B)T energy units. These binding sites adhere on average to the predicted level of gene expression over 3 orders of magnitude in constitutive gene expression, to within a factor of 3 in 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 over 100 per 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 30%. 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.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002811&type=printable |
| spellingShingle | Robert C Brewster Daniel L Jones Rob Phillips Tuning promoter strength through RNA polymerase binding site design in Escherichia coli. PLoS Computational Biology |
| title | Tuning promoter strength through RNA polymerase binding site design in Escherichia coli. |
| title_full | Tuning promoter strength through RNA polymerase binding site design in Escherichia coli. |
| title_fullStr | Tuning promoter strength through RNA polymerase binding site design in Escherichia coli. |
| title_full_unstemmed | Tuning promoter strength through RNA polymerase binding site design in Escherichia coli. |
| title_short | Tuning promoter strength through RNA polymerase binding site design in Escherichia coli. |
| title_sort | tuning promoter strength through rna polymerase binding site design in escherichia coli |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002811&type=printable |
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