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 fluorescence 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.

3D Device Animation using Autodesk Maya (Click here if video isn't working)

Quantitative Modelling

In order to describe the expression of the potential reporter proteins, we used a plate reader to measure the cell count and fluorescence. 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 (OD @ 600 nm). 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 fluorescence 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. Fluorescence values are growing exponentially over time, and higher concentrations reach higher numbers. However, at 18-20 hours, the differences between dilutions are 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 takes based on the graph in figure 2. E. coli with lacZ was expected to be 0. However, one of the controls (media with no culture) is not 0, but instead follows the same curve as bacteria transformed with RFP which suggests it was contaminated. B. subtilis with RFP graph is not as clean as E. coli graph since dilutions overlap. But the graph still shows the general trend that we expected to see.

Figure 2: Fluorescence versus Time for E. coli transformed with RFP (left), E. coli with lacZ (middle), and B. subtilis transformed with RFP (right).

We also attempted to measure lacZ fluorescence on E. coli with lacZ using the plate reader. However, the plate reader did not have the settings and required filters to perform such measurements. Our colour sensor might be better at measuring blue colour (lacZ) than the plate reader, so we will set up a similar experiment using the colour sensor. We will leave it overnight and get our colour sensor to measure the colour every 30 minutes. We would only be able to measure one dilution overnight, and will not be able to measure fluorescence directly. Instead, the colour sensor will output the red, green, and blue components allowing us to see how colour changes over time. We will also be able to see how long it takes for bacteria to express detectable colour.

Fluid Flow Analysis and Angle Optimization

In our initial design, the three chambers were located at an arbitrary angle. With a fluid flow simulation we found that most of the fluid would flow into the outside chambers from the PCR chamber with our initial design idea. In order to make sure the fluid flows equally into the three chambers, fluid flow analysis was performed. The optimal angle for the tubings was found to be 20 degrees, this was used as a reference for our first iteration of our prototype.

Figure 3: Fluid flow analysis for 15° angle between the tubings

Figure 4: Fluid flow analysis for 20° angle between the tubings

Figure 5: Fluid flow analysis for 25° angle between the tubings

Figure 6: Fluid flow in the PCR chamber

After receiving valuable feedback, our team made the informed decision to alter our prototype to have the detection chambers located symmetrically and radially oriented around the PCR chamber. This allows the end user to alter the number of chambers if necessary which consequentially decreases the tubing distance and the resulting cost. The fluid flow analysis on the new design with three chambers was also performed to ensure that with this changed design the fluid is still flowing equally into the chambers. Additionally it was very important to ensure that when adding more chambers, considerations are made for the amount of fluid available in the PCR chamber. This should not be an issue for our developed prototypes and their designs, however if the need arose to have a very large amount of detection chambers, this would be a very important fact to consider.

Figure 7: Fluid flow analysis for the new design with three chambers around the PCR chamber