Team:ASIJ Tokyo/Results

Confirming the Synthesis of Biomarkers

In order to verify the accurate synthesis of our biomarkers in an E.Coli system, we used qualitative analysis and quantitative analysis to show our success.

For our qualitative analysis, we compared the amount of fluorescence in our transformed colonies to the number of colonies to monitor for success of transformation. Our qualitative analysis can be found below in the detailed document.

For our quantitative analysis, after we purified our recombinant biomarkers following transformation, we quantified the amount of synthesized biomarkers through using a Braford assay. Our results demonstrated that we were able to successfully produce a biomarker from our construct and characterize the efficiency of different promoters and terminators in synthesizing our biomarkers.

To calculate the concentrations of the biomarkers within each well, we first ran the BSA model to create the standard curve, where the x-axis represents absorbance, and y-axis represents concentration in mg/ml. The standard curve later allowed us to convert the measured absorbance into measurements of concentration. The equation and the image of the standard graph we derived from the BSA model is as follows.

Our line of best fit for the BSA standard curve derived from polynomial regression is y = 0.1258x^3 - 0.5139x^2 + 0.848x + 0.2487. We chose to model our equation as a cubic function because it provided a better approximation for our data, with a correlation of 0.9607 and root mean square error (RMSE) of 0.0835 as seen above.

Using the standard curve and the measured absorbance, we interpolated values of concentration for each of the different constructs. We performed this conversion by substituting the absorbance values into the x-variable in our standard curve, to find the corresponding y-variable which represents concentration. The post-conversion concentrations of the biomarkers are listed below.

Construct	Biomarker	Concentration
T7 Promoter+T7 Terminator	Mucin(A1)	1.83
	Her-2(B1)	N/A
	Mammaglobin-B(C1)	0.16
Anderson Promoter+T7 Terminator	Mucin(A2)	N/A
	Her-2(B2)	N/A
	Mammaglobin-B(C2)	N/A
Optimized Promoter+T7 Terminator	Mucin(A3)	N/A
	Her-2(B3)	0.90
	Mammaglobin-B(C3)	0.11
T7 Promoter+T1 Terminator	Mucin(A4)	0.16
	Her-2(B4)	0.30
	Mammaglobin-B(C4)	0.31
Anderson+T1 Terminator	Mucin(A5)	0.08
	Her-2(B5)	0.05
	Mammaglobin-B(C5)	0.31
Optimized Promoter+T1 Terminator	Mucin(A6)	0.16
	Her-2(B6)	0.36
	Mammaglobin-B(C6)	0.40
T7 Promoter+Lux Terminator	Mucin(A7)	0.15
	Her-2(B7)	0.13
	Mammaglobin-B(C7)	0.26
Anderson+Lux Terminator	Mucin(A8)	0.15
	Her-2(B8)	0.29
	Mammaglobin-B(C8)	0.37
Optimized Promoter+Lux Terminator	Mucin(A9)	0.49
	Her-2(B9)	0.33
	Mammaglobin-B(C9)	0.24

For our project, the effectiveness of a construct is measured by the concentration of biomarkers produced. Thus, using the concentrations, we proceeded to compare them among each type of biomarker to see which of the constructs (1-9) was most suitable for cancer detection. For Mucin, our data indicates that construct A1 composed of T7 promoter, and T7 terminator was the most efficient with a concentration of 1.83(µg/mL). For Her-2 the most effective construct was B3 with a concentration of 0.90(µg/mL), which contains the optimized TA prompter and T7 terminator. For Mammaglobin-B, the most effective construct was C6 which contains optimized TA promoter and T1 terminator, with a concentration 0.40(µg/mL). Please see our parts page for a detailed summary of each of our constructs and the compatibility of the different promoters and terminators we utilized.

We performed two trials for each construct to increase the accuracy of the measurement. The values in the table are the averaged values of our two trials. One thing to note about our measurements is that whenever we discovered an error in our procedure, we performed further experiments to confirm our findings. For example, we noticed that we had a negative reading for construct A1 during our first trial, and repeated the experiment again. We hypothesized that this error was caused by inaccurate pipetting as members were unfamiliar with the 96-well plate reader and construction of standard curve. Upon repeating the experiment again, we were able to get a data reading that was accurate and reliable.

Additionally, in the results table, the word “NA” indicates that the values were incalculable under the BSA model. We decided to omit any results that were impossible to extrapolate from the concentration vs absorbance standard curve because doing so would be statistical extrapolation of the BSA model. For future teams, we recommend that protein quantification experiments be repeated multiple times due to the sensitivity of the equipment and high precision skills required for these kinds of assays.