Team:NEU China B

Title Page

project description Wine is the most common drink in people's lives. However, during the production of alcohol, lactic acid is often produced, which not only affects the taste of the wine, but also inhibits the viability of the fermenting yeast, significantly reducing the efficiency of alcohol fermentation. Therefore, NEU China B is dedicated to the construction of a lactic acid biosensor that uses E. coli's group-sensing effect to sense lactic acid during alcohol fermentation and monitor it in real time. When the lactic acid content reaches its peak, it will alarm.

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InterLab study works as a guidance for us to develop the conception including building the major principle, in order to setting a standardization. Reliable and repeatable measurement, which are known as the key factors toward synthetic biology. However, the existence of disparities is inevitable due to multiple reasons like the equipment, raw materials they used during the experiments, and the different methods they process their data. By collecting data from teams, the difference would be narrowed. The 2018 InterLab study aims to provide a trust-worth plate reader-based measurement protocol and flow cyptometry protocol suitable throughout the world. Results and observations The 2018 InterLab study was separated into 2 parts, plate reader measurement and flow cyptometry measurement. In the InterLab study, we used an Spectra Max M2 as our molecular plate reader device and an BD LSRFortessa as our flow cyptometry device. The plate reader part includes 3 sets of unit calibration measurements and the cell measurement under the setting in the calibration. The calibration contains an OD600 reference point(fig 1), a particle standard curve(fig 2), and a fluorescein standard curve(fig 3). (fig 1) As we can see, in the first part of calibration, we concluded the rate of OD600 and Absorbance is 4.131. (fig 2.1) (fig 2.2) Originally, the values of both graph should form a straight line in a slope 1:1. But the log scale graph appears to be an upper throw line. This could have been due to a consistent pipetting error. Also, the detector could have been oversaturated, because low concentrations were linear, while at high concentrations it either saturated or fell. (fig 3.1) (fig 3.2) As shown above, both the two graphs of fluorescein standard curve demonstrated a linear shape at a constant slope of 1:1 . In the IGEM’s InterLab measurement package, there are 8 devices: Positive control(BBa_R0040), Negative control(BBa_ R0040), Test 1 (BBa_J364000), Test 2 (BBa_J364001), Test 3 (BBa_J364002), Test 4 (BBa_J364007), Test 5 (BBa_J364008) and Test 6 (BBa_J364009). We transformed and cultured them all in the circumstance of 10 mL LB(Luria Bertani) medium and Chloramphenicol. 2 colonies were picked from each plate and cultured into 5 ml cultures for 16 hours, at 37 ºC using 220 rpm condition. Then we measured their OD600 and fluorescence emission resulting in 2 specific time: 0 h and 6 h (which required light-avoiding culture). For this experimental groups, we used the same plate reader instrument, a Spectra Max M2 Microplate Reader and sample condition as volume performed at standard protocols. By analyzing the OD600 absorbance value, we observed a generally suitable trend. According to the theory, the negative control group has no gene to encoding the GFP protein and the metabolic load is the lowest, hence it would have the highest absorbance. When we looked through the data among the different devices ( which are composed of strong RBS and promoter sequence), the Device 3 showed the highest values of absorbance and the lowest fluorescence emission. After processing the data in 0h and 6h, we drew to a conclusion that the expression order of the 6 devices counting in ascending order is TD3, TD6, TD2, TD1, TD5 and TD4. However, the results of TD5 seem to had an opposite tendency between fluorescence emission and absorbance compared to the other groups. And the whole replicates of TD5 showed a consistency in contradict tendency. A possible explanation for this could be that during the preparation of the samples and the growth phases, out of the operational miss could have played a role in the differentiation of the data.

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