Latest revision as of 09:10, 15 October 2019

Nit_Blue

This composite part is a nitrate biosensor. It is composed of the nitrate sensitive promoter PyeaR, a strong rbs, the amilCP blue chromoprotein and a transcriptional terminator. In presence of nitrate, bacteria turn into different blue color intensities according to the concentration of nitrate.

UPNAvarra_Spain 2019

BBa_K2817007 was first designed by Zhaoyu Liu from team NEU_China_A in iGEM 2018. It is a nitrate reporter, PyeaR-amilCP composite. This team only test the BioBrick’s sensitivity at 100 μM sodium nitroprusside dihydrate aqueous solution, confirming the expression of the blue chromoprotein under those conditions. However, we have better characterized the expression of amilCP under a concentration gradient of potassium nitrate.

We transformed the plasmid containing BBa_K2817007 (our twin BBa_K3287000) into E. coli competent cells and cultured at 37ºC until OD = 0.4. Then we add KNO₃ al 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 PyeaR promoter being activated by nitrate (Figure 3).

Figure 3. AmilCP expression levels under increasing concentrations of KNO₃.

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).

Figure 4. Modeling a Nitrate biosensor. A) Input data; B) Regression model.

Sequence and Features