Difference between revisions of "Part:BBa K3926002"
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===Usage and Biology===
 
===Usage and Biology===
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<span class='h3bb'>Sequence and Features</span>
 
<span class='h3bb'>Sequence and Features</span>
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Usage and Biology
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Introduction
 
Introduction
 
<br>We improved part: BBa_K216005 (PyeaR promoter), which is the promoter of the Escherichia coli yeaR/yoaG operon. The most remarkable feature of this promoter is its ability to sense nitrate and nitrite. In order to better regulate the response of the promoter to nitrate, we use machine learning models to predict and design new PyeaR sequences. Compared to the original sequence, five or six bases have been changed.
 
<br>We improved part: BBa_K216005 (PyeaR promoter), which is the promoter of the Escherichia coli yeaR/yoaG operon. The most remarkable feature of this promoter is its ability to sense nitrate and nitrite. In order to better regulate the response of the promoter to nitrate, we use machine learning models to predict and design new PyeaR sequences. Compared to the original sequence, five or six bases have been changed.

Revision as of 11:42, 20 October 2021


An improved PyeaR with higher expression strength

We use machine learning model to design this promoter.

Sequence and Features


Assembly Compatibility:
  • 10
    COMPATIBLE WITH RFC[10]
  • 12
    COMPATIBLE WITH RFC[12]
  • 21
    COMPATIBLE WITH RFC[21]
  • 23
    COMPATIBLE WITH RFC[23]
  • 25
    COMPATIBLE WITH RFC[25]
  • 1000
    COMPATIBLE WITH RFC[1000]



Usage and Biology
Introduction
We improved part: BBa_K216005 (PyeaR promoter), which is the promoter of the Escherichia coli yeaR/yoaG operon. The most remarkable feature of this promoter is its ability to sense nitrate and nitrite. In order to better regulate the response of the promoter to nitrate, we use machine learning models to predict and design new PyeaR sequences. Compared to the original sequence, five or six bases have been changed.

Construction of improved PyeaR
Based on the original sequence, we designed and predicted three mutation sequences that can increase the intensity of the promoter by using our machine learning model. By modifying the PCR primers, we successfully obtained the mutated PyeaR promoters. Through homologous recombination, we replaced the wild-type promoter with the improved promoter.