BME100 f2016:Group15 W8AM L6

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Name: Adam Akkad
Name: student
Name: student
Name: student
Name: student
Name: student

Green Diagnostics


Bayesian Statistics

Overview of the Original Diagnosis System

(A)BME 100 students tested a group of patients for a disease associated SNP using PCR reactions. First, the patient samples were split among 16 groups, each group testing two patients. (B) Precautions were taken to minimize error. Gloves, lab coats, solutions were provided with clearly-labeled tubes, and pipettor training was provided to ensure accurate measurements and prevent cross-contamination. In the PCR process, three replicates of each patient's DNA were taken to reduce error. Positive and negative controls provided a reference point of comparison which would later assist in diagnoses. To determine the concentration of DNA in the solutions, we used SYBR Green 1 molecule and measured the fluorescence of the molecule by taking pictures of it when exposed to DNA. The intensity of fluorescence correlated with the concentration of DNA. During the measurement of DNA concentration, three images of each concentration were taken and the mean and standard deviations of the measurements in ImageJ were taken into consideration for accuracy. We used several concentrations of calf thymus DNA and plotted the results on graphs to calibrate our measurements against our actual PCR reaction measurements. (C)The results for initial concentration of DNA in each PCR reaction of both patients were compared against the positive and negative controls' initial DNA concentrations. By comparing which result had an initial concentration closest to either the positive or negative DNA, we determined whether the selected DNA sample was positive or negative for the disease-associated SNP. Each of the group's final data was compiled into a single spreadsheet and compared to a doctor's diagnoses. Then, using Bayesian analysis, we determined the probability of detecting and predicting the disease-associated SNP using PCR reaction tests. Our calculations showed that we could detect the disease in DNA with higher than .72 probability. Thus our conclusions successfully validated the predictions of PCR reactions up to .72 probability. One complication that arose was that two groups did not have their results submitted and there were six inconclusive PCR results. However, the majority of calculations were still there and we were able to proceed in the analyses of our results. One challenge specific to our group was that while we were taking pictures of SYBR Green 1 molecule, we initially placed the drops on the wrong side of the slide. We had to hurriedly redo our pictures before the end of the class. However, the pictures turned out to be good quality and our measurements translated into conclusive results for DNA concentration.

What Bayes Statistics Imply about This Diagnostic Approach

Calculations 1 and 2 are associated with determining the reliability of the PCR and florescence method in diagnosing specific diseases of interest. Using Bayes statistics helps determine the likelihood that individual trials with positive PCR results for a disease result in an overall conclusion that a patient is positive for the disease (as determined in question 1) and the likelihood that individual trials with negative PCR results for a disease result in an overall conclusion that the patient is negative for the disease (as determined in question 2). In question one, the likelihood positive PCR reactions resulting in positive general conclusions was high with a Bayes value close to 90% (.89). In question two, the likelihood negative PCR reaction resulting in negative conclusions for patients having the disease SNP was very high with a Bayes value much closer to 100% (1.01). From this statistical analysis it can be inferred that the PCR diagnosis method is reliable because its conclusions are consistent over multiple trials.

Calculations 3 and 4 show the reliability of PCR to predict (or diagnose) the development of disease. These statistical calculations determine the probability that a negative result will lead to a negative diagnosis (calculation 3), and that a positive result will lead to a positive diagnosis (calculation 4). Calculation 3 shows that 90% of negative results in the PCR are diagnosed as being negative (not going to develop the disease). Calculation 4 shows that about 70% of positive test results in the PCR will be diagnosed as positive (going to develop disease). This means that the PCR reliably predicts when there is no development of disease, and is semi-reliable is predicting the development of disease in an individual.

Sources of Error

During the course of the lab there were several instances where error could have occurred. The samples analyzed in Lab D required specific concentrations of DNA and SYBR GREEN 1. Each of the samples were made manually, making room for possible miscalculations of each of the concentrations. Had these concentrations been calculated inaccurately, the final results of each PCR reaction could have been inaccurate. Where a sample would have normally been positive, it could have been analyzed as negative. During the fluorimeter process, it was required that three pictures be taken of each drop to be analyzed. Multiple pictures allowed for the results to be more accurate and reliable. Had only one picture been taken, the results after it was analyzed could have been highly inaccurate. This would lead to a wrong final PCR conclusion causing the Bayes values to be thrown off. Not only could the lack of pictures throw off the results, but analyzing the wrong picture would have the same effect. Each of the sample pictures were analyzed using ImageJ. During this, the picture had to be separated into three colors: blue, red, and green. To properly analyze the sample, only the green picture was to be measured. If one analyzed either the red or blue sample, or did not even separate it into the colors, the results would have been very different.

Intro to Computer-Aided Design

3D Modeling

We used Tinkercad to build a 3D model of our Green Diagnostics PCR machine. Compared to Solidworks, this software was a lot more intuitive and user friendly. While the software was simple and easy to use, it lacked detail and various functions. Nonetheless, we were able to make a good sketch of up live PCR machine. It was very easy to handle and luckily we did not face any serious difficulties while building our model. Thanks to Tinkercad, we were able to mimic the model we had in mind as best as we could with a rough three-dimensional blueprint.

Our Design

Top.JPGSide closed.JPG Angle open.JPGZoom.JPG

We updated the original PCR machine in several ways. Most significantly, we merged the PCR machine and the flourimeter together to produce a live PCR machine. Live PCR allows the change in florescence to be observed in DNA samples as they go through PCR. This allows for more specific and accurate data with regards to identifying an amplification of an isolated sequence of DNA to diagnose a patient positive or negative for a disease. This live change in florescence is displayed along a curve on a user friendly LCD display on the top of the machine. Moreover, we increased the slots available for DNA samples to 36 slots. Lastly, we designed the live PCR machine to be made of a dark PCB material that is able to withstand extremely high temperatures and keep the flourimeter dark so florescence can be measured accurately.

Feature 1: Consumables

Our consumables kit will only include items that require specialized precision or cannot be commonly obtained in order to do the PCR reaction. Our kit also does not include basic safety equipment such as a lab coat, googles, or gloves because those are assumed precautions in lab procedure and would likely be followed by someone wishing to conduct an experiment of this caliber. Our consumables kit will include all tubes, pipettor tips, pipettors, PCR mix, and primers. Each consumable will be clearly labeled. All the pipettor tips will be biodegradable to make our consumables more eco-friendly. Other items that were needed in our original lab such as a black box or phone camera are already integrated in our improved real-time PCR machine. In addition, the box itself will be arranged in procedural order to make it more intuitive--in other words, the materials will be placed in the box in the order that they need will be used to make the PCR reaction. The procedure for preparing PCR reactions is to (1) first select the desired DNA to be placed into the empty tubes to be tested, (2) then to load pipettor tips onto the pipettor, and finally (3) to use the pipettor to insert the primers, PCR mix, and fluorescent molecule into the DNA sample. Thus, the box will be arranged in three sections:

By procedural order (3 sections)

  • 1. Empty tube with DNA
  • 2. Pipette and tips.
  • 3. Primers, PCR mix, and fluorescent molecule

Screenshot 2016-11-11 at 2.52.29 PM.png

Feature 2: Hardware - PCR Machine & Fluorimeter

  • Determine what that fluorescent molecule is that is directly proportional to the amount of DNA in the molecule (this is different from the SYBR green molecule we think according to some sources that talk about how real-time PCR works)

The current technology that is used in real time PCR (qPCR) machines is diffusion laser technology with mirrors. The way it works is by filtering light from a tungsten-halogen lamp, reflecting the light off of mirrors, and then focusing the light onto an individual well. Any fluorescent light will then reflect off of a fold mirror positioned above the wells, and pass through an emission filter wheel before being detected by a charge-coupled device (Bio-Rad).

Using diffused light and mirrors is becoming an outdated technology and is less efficient than using LEDs. Our qPCR machine has an LED array that beams down onto a Fresnel lens that focuses the beam directly into the corresponding well, this minimizes light loss. Then, the fluorescence emitted from the well goes through a beam splitter, each beam goes through special filters, and then the two beams hit two sensitive photodiodes, which are photodetectors that causes a current to flow when exposed to light (Bio-Rad). The electric current is interpreted by the software and then used to graph the real time results on the machine, instead of having to take the data to a separate system.

Works cited "Introduction to QPCR Instrumentation." Bio-Rad. N.p., n.d. Web. Nov. 2016. <>.