Modelling & Optimization

The modelling component of our project was aimed at optimizing the system and creating a 3D visual to demonstrate the way the device operates. With quantitative modelling, we characterized the reporter using the plate reader. With the plate reader, we measured cell count and absorbance over night for E. coli transformed with RFP (Red Fluorescent Protein), E. coli transformed with lacZ, and B. subtilis transformed with RFP. In order to make our diagnostic test as fast as possible, our other goal with modelling was to quantify the experiments performed by biology students on the team to determine most optimal conditions for B. subtilis transformation and growth. Modelling and simulations also allowed us to determine the best way to put our physical device together. 3D animation made using Autodesk Maya software was used to explain how the device works. Creating 3D animation allows to visualize how different parts of the system work as well as how they are connected together. A visual also makes it easier for people from different backgrounds to understand what is happening in the device. Our modelling team also created a 3D animation of our device. It shows the physical outline of the device as well as an animation of the biological system. The animation was created using Autodesk Maya 2013 software and ePMV plug in. It serves as a visual to everything taking place in our device making it easier to understand even for general public.

Quantitative Modelling

In order to describe the expression of the potential reporter proteins, we used the plate reader to measure the cell count and absorbance. Five dilutions were prepared to see how initial cell concentration will affect cell growth and reporter signal. Overnight, 36 measurements were taken with 1800 s (30 min) intervals. Figure 1 shows the results obtained when the plate reader measured cell count. From the graph, it is evident that E. coli with lacZ grew more rapidly, but reached the flat region (or no growth region) within 6-7 hours. It also reached lower cell count numbers than bacteria culture with RFP.E. coli and B. subtilis with RFP grew less rapidly, but did not reach the flat region within 20 hours. However, from the graph it is evident that it is approaching the flat region. Bacteria cultures with RFP also reached higher cell count than LacZ.

Figure 1: Cell count (OD @ 600 nm) versus Time for E. coli transformed with RFP (left), E. coli with lacZ (middle), and B. subtilis transformed with RFP (right).

Figure 2 shows RFP absorbance measurements on E. coli transformed with RFP, E. coli transformed with lacZ and B. subtilis transformed with RFP. E. coli with RFP follows the expected trend. Absorbance values are growing exponentially over time, and higher concentrations reach higher numbers. However, at 18-20 hours, the differences between dilutions is small. Once we determine the threshold level that bacteria in our device will need to reach, we would be able to tell how long it will take to reach from the graph in figure 2. E. coli with lacZ was expected to be 0. However, one of the controls (just media with no culture) is not 0, which suggests it was contaminated with RFP. B. subtilis with RFP graph is not as clean as E. coli graph since dilutions overlap. But the graph still shows general trend that we expected to see.

INSERT FIGURE 2 HERE: Absorbance of RFP vs Time

Figure 3 shows LacZ absorbance measurements on E. coli with RFP, E. coli with lacZ, and B. subtilis with RFP. We expected to see a big difference between E. coli with lacZ and bacteria culture with RFP. However, no major difference was observed because the plate reader does not have appropriate filters to measure lacZ absorbance.

INSERT FIGURE 3 HERE: Absorbance of lacZ vs Time

Our color sensor might be better at measuring blue color (lacZ) than the plate reader. We are planning to set up a similar experiment with the color sensor. We will leave it overnight and get our color sensor to measure the color every 30 minutes. We would only be able to measure one dilution overnight, and will not be able to measure absorbance directly. Instead, the color sensor will output the red, green, and blue components allowing us to see how color changes over time. We will also be able to see how long it takes for bacteria to express detectable color.