Difference between revisions of "Part:BBa K3287002"
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<h1>'''UPNAvarra_Spain 2019, Improve the Characterization of BBa_K2817007'''</h1> | <h1>'''UPNAvarra_Spain 2019, Improve the Characterization of BBa_K2817007'''</h1> | ||
− | + | Our biosensor for cooper detection is composed of a regulatory sequence made up by the CueR activator and the cooper specific promoter copAP. This promoter is regulated by CueR, which binds Cu<sup>2+</sup> ions (Yamamoto and Ishihama 2005), that is under the control of a constitutive promoter (BBa_K608002). Then we used a blue chromoprotein (amilCP BBa_K592009) downstream the promoter for a first sight detection, with a strong RBS (BBa_B0030). The correct construction of this plasmid was confirmed by sequencing (Figure 1). | |
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Revision as of 09:14, 14 October 2019
Cu_Blue
This composite part is a cooper biosensor. It is composed of the cueR activator under the control of a constitutive promoter, the cooper specific promoter copAP, a strong rbs, the amilCP blue chromoprotein and a transcription terminator. In presence of cooper, bacteria turn into different blue color intensities according to the concentration of cooper.
UPNAvarra_Spain 2019, Improve the Characterization of BBa_K2817007
Our biosensor for cooper detection is composed of a regulatory sequence made up by the CueR activator and the cooper specific promoter copAP. This promoter is regulated by CueR, which binds Cu2+ ions (Yamamoto and Ishihama 2005), that is under the control of a constitutive promoter (BBa_K608002). Then we used a blue chromoprotein (amilCP BBa_K592009) downstream the promoter for a first sight detection, with a strong RBS (BBa_B0030). The correct construction of this plasmid was confirmed by sequencing (Figure 1).
For these experimental results we generated a mathematical model, in order to prove that the (imaging) data we have gathered in the lab is in fact learnable by a simple regression model. We have opted out by a standard Least-Square error (linear) regression model, which has been run on the dataset obtained in the imaging part. This dataset consists of the average RGB color in the colored part of the pellets used at different concentrations of KNO3 (Figure 4A). For each color, we have subselected the channels that we are interest for the problem. That is the Red channel in this case. It can be seen how the data is easily learnable by a linear regression model (Figure 4B) and, moreover, the error in the model training is rather small (0.18).
Sequence and Features
- 10COMPATIBLE WITH RFC[10]
- 12INCOMPATIBLE WITH RFC[12]Illegal NheI site found at 7
Illegal NheI site found at 30 - 21COMPATIBLE WITH RFC[21]
- 23COMPATIBLE WITH RFC[23]
- 25COMPATIBLE WITH RFC[25]
- 1000INCOMPATIBLE WITH RFC[1000]Illegal BsaI.rc site found at 475
Illegal SapI.rc site found at 625