PCR and Friends
PCR and Friends
LAB 6 WRITEUP
Bayesian Statistics
Overview of the Original Diagnosis System
Variable 
Description 
Numerical Value

A 
Positive final test conclusion 
0.25

B 
Positive PCR 
0.274

P(B/A) 
Positive PCR, given a positive test conclusion 
0.870

P(A/B) 
Positive test conclusion, given a positive PCR 
0.793

What is the probability that a patient will get a negative final test conclusion, given a
negative diagnostic signal?
Variable 
Description 
Numerical Value

A 
Negative final test conclusion frequency 
0.68

B 
Negative PCR frequency 
0.63

P(B/A) 
Negative PCR, given a negative test conclusion frequency 
0.905

P(A/B) 
Negative test conclusion, given a negative PCR frequency 
0.98

What is the probability that a patient will develop the disease, given a positive final test
conclusion?
Variable 
Description 
Numerical Value

A 
Patient will develop the disease 
0.25

B 
Positive final test conclusion 
0.25

P(B/A) 
Positive final test conclusion, given the patient will develop the disease 
0.571

P(A/B) 
The patient will develop the disease, given positive final test conclusion 
0.571

What is the probability that that a patient will not develop the disease, given a negative final test conclusion?
Variable 
Description 
Numerical Value

A 
Patient will not develop the disease 
0.57

B 
Negative final test conclusion 
0.93

P(B/A) 
Negative final test conclusion, given the patient will not develop the disease 
0.75

P(A/B) 
The patient will not develop the disease, given negative final test conclusion 
0.57

What calculation describes the sensitivity of the system regarding the ability to detect the disease SNP? Calculation 1, 0.870
What calculation describes the sensitivity of the system regarding the ability to predict the disease? Calculation 3, 0.571
Which calculation describes the specificity of the system regarding he ability to detect the disease SNP? Calculation 2, 0.57
Which calculation describes the specificity of the system regarding the ability to predict the disease? Calculation 4, 0.75
We as a team in BME 100 performed a PCR reaction to determine whether or not two patients that we were given had a particular disease. The way that we distributed the workload was that two people performed the actual experiment and diluted the DNA into solution to be put into the PCR reaction. The next week, two other people performed the florimeter part of the experiment to see whether or not qualitatively if the dna that was replicated contained any of the disease that we were given. The other three members of the group at this time, analyzed the images in the image J part of the experiment to obtain the data values that were needed for the data charts. Next the lab report was split up among the group and each member created a different chart or graph to fulfill the requirements of the report. In order to prevent error within the lab, during the PCR reaction we carefully diluted each and every DNA sample that we had and also checked with the TA to see whether or not we were doing anything wrong. We also tried to keep our pictures very consistent throughout the florimeter process to keep our data as precise as possible. Our final conclusion is that Patient 76322: yielded a negative result we observed a value of 0.03 μg/mL which is close to the positive result as well as Patient 12480 : yielded a postive result of 0.96 μg/mL close to the negative result. The biggest challenge that we had as a group was communication. Due to not being as open as we could have been with each other, this might have effected our data. Another thing that could have effected our data is that the pictures, although we did our best to keep them as consistent as possible there might have been error in the cameras lack of focusing.
What Bayes Statistics Imply about This Diagnostic Approach
The result for calculation 1 tells about the sensitivity of the system in regards to being able to detect the disease SNP, moreover, how probable it is that a person with a positive pcr result will receive a positive final test conclusion. The result for this calculation was close to 80%. The result for calculation 2 tells us about the specificity of the system in regards to being able to detect the disease SNP. Specificity measures the true negative rate of the samples of the population. This was about 75%. These results show that a good majority of the population were correctly identified with either a positive or negative final test result.
The result for calculation 3 tells us the sensitivity of the system in regards to being able to predict the disease in the population. This was close to 100%, meaning the system was very accurate in being able to predict who would develop the disease from those who received a positive final test result. The result for calculation 4 tells us about the specificity of the system in regards to being able to predict the disease in the population. This was about 60%, meaning the system was less accurate in being able to detect who would not develop the disease from the total negative test conclusion results.
In the PCR lab there were multiple sources of possible human error. Among these there is the chance of micropipetting the wrong amount of DNA or solution which could affect the accuracy of the results depending on how large or small the difference is. There is also the interpretation of the color being incorrect, which would negatively affect the Bayes value because it would predict the wrong color. Another possible source of human error could be the miscalculation of sensitivity and specificity of the system regarding the ability to predict and detect the disease. Miscalculation of either of these values would result in the misinterpretation of the probability of developing the disease.
Intro to ComputerAided Design
3D Modeling
In the PCR lab we used a thermocycler which is a device that functions as a laboratory apparatus used to amplify segments of DNA through a PCR. A con of this device is it is very outdated and uses old technology. Along with the outdated technology, the process necessary to obtain results from the machine is very tedious. It would be best for someone to reinvent the machine with more modern technology or just a more appealing and userfriendly interface in general so that the process is less painful. The other piece of technology is the fluorimeter which is a device used to measure parameters of fluorescence. This was extremely painful to use considering we were constantly worried about the water droplet breaking and it was an extremely slow process. A solution for this device would be to discover an easier set up for it.
Our Design
Our design incorporates a way to take pictures as it cycles the DNA. We chose this design to ensure overall productivity and to gain better imaging measures in order to quantify our results more accurately.
Feature 1: Consumables
 specialized tubes
 necessary reagents
 cuvettes
 micropipetter
Feature 2: Hardware  PCR Machine & Fluorimeter
for our hardware components, we found it important to implement a system to take more accurate photos. The new design will incorporate a camera inside the device a take photos periodically. Ultimately, it will come up with a composite sketch and display a mean value for necessary data.
