Revision as of 09:25, 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.

TITULO

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).

Figure 1. Figure 1. Construction of expression vector Cu_Blue (BBa_3287002) from parts of 2015_Bielefeld-CeBiTec: BBa_K1758320 and BBa_K1758323 (only the copAP promoter sequence); and amilCP coming from the chromoprotein collection of 2011_Uppsala-Sweden team. The CueR-copAP-amilCP composite part is cloned pSB1C3 vector through the BioBrick suffix-prefix site.

We transformed the plasmid containing BBa_K3287002 into E. coli competent cells and cultured at 37ºC until OD = 0.4. Then we add CuCl₂ at different concentrations to induce the expression at 37 ℃ for 6 hours. After that, 2 mL of the bacterial culture were centrifuged at 3,000 r.p.m for 3 minutes, so we could observe at first sight the result of copAP promoter being activated by cooper (Figure 2).

Figure 2. AmilCP expression levels under increasing concentrations of CuCl₂.

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 CuCl₂ (Figure 3A). For each color, we have subselected the channels that we are interest for the problem. That are the Red and Green channels in this case. It can be seen how the data is easily learnable by a linear regression model (Figure 3B) and, moreover, the error in the model training is aceptable (1.88).

Figure 3. Modeling a Cooper biosensor. A) Input data; B) Regression model.

Reference

Yamamoto, Kaneyoshi; Ishihama, Akira (2005): Transcriptional response of Escherichia coli to external copper. In Molecular microbiology 56 (1), pp. 215–227.

Sequence and Features