# BME100 s2016:Group15 W1030AM L6

BME 100 Spring 2016 Home
People
Lab Write-Up 1 | Lab Write-Up 2 | Lab Write-Up 3
Lab Write-Up 4 | Lab Write-Up 5 | Lab Write-Up 6
Course Logistics For Instructors
Photos
Wiki Editing Help

# OUR COMPANY

 Name: Sidney Covarrubias Name: Byron Alarcon Name: Mason Buseman Name: Kelsey Graft Name: Ibrahim Aljabri Name: Jake Xu

# LAB 6 WRITE-UP

## Bayesian Statistics

Overview of the Original Diagnosis System
In this lab patients were tested to identify if they had a disease marker in their DNA. This was done by sixteen groups that each had six people in them and they tested a total of 32 patients to ensure the sample size was big enough to cut down on the error. Another measure to keep error at a minimum was that 3 replicate PCR samples were tested and positive and negative controls were tested with SYBR Green I solution. This allowed the results to be compared to the controls to identify from the ImageJ calibration controls if they tested positive or negative for the specific SNP. All three drops per patient were analyzed using the oval ImageJ controls. ImageJ was used to find the Mean Pixel Value, Area, and RAWINTDEN data. These were then used in calculations which gave the group results which were recorded into a spreadsheet that allowed the other groups to view the results. These results were then used for Bayesian Statistics. Some difficulties came up when trying to select the drops in ImageJ because if the oval was even a little off it could skew the data and while taking the pictures the group had to be careful while putting the lid down to keep the surrounding light because the wire to the camera might be hit and moved.

For Bayes Statistics calculation 1, it implies that the probability of a patient getting a positive final test conclusion given a positive PCR reaction was close to 1.00 (100%), and the probability of a patient getting a positive PCR reaction given a positive final test conclusion was close to 1.00 (100%). With both these results it statistically implies that the results from calculation 1 are reliable and that the PCR replicates are a valid source to determine whether the patient does have the SNP disease in their DNA.

For Bayes Statistics calculation 2, it implies that the probability of a patient getting a negative final test conclusion given a negative PCR reaction was close to 1.00 (100%), and the probability of a patient getting a negative PCR reaction, given a negative final test conclusion was close to also 1.00 (100%) as well. With both these results it also statistically implies that the results from calculation 2 are reliable and that the PCR replicates are a valid source to determine whether the patient does not have the SNP disease in their DNA.

For Bayes Statistics calculation 3, it implies that the probability of a patient developing the disease given a positive final test conclusion was slightly below 0.5 (50%), and the probability of a patient being given a positive final test conclusion given that the patient will develop the disease was exactly 0.5 (50%). With both these results it statistically implies that the results from calculation 3 are not reliable and that the doctor’s positive test results are not a valid source to determine whether the patient will develop the SNP disease that is within their DNA.

For Bayes Statistics calculation 4, it implies that the probability of a patient not developing the disease given a negative final test conclusion was above 1.00 (100%), and the probability of a patient being given a negative final test conclusion given that the patient will not develop the disease was close to 1.00 (100%). With both these results it statistically implies that the results from calculation 4 are inconclusive in some aspects but reliable in another. The inconclusive aspect is the probability of a patient not developing the disease given a negative final test conclusion, because the statistics suggest a probability above 100%. While the reliable aspect is the probability of a patient being given a negative final test conclusion given that the patient will not develop the disease, because the data was lower than 100% but close to 100% probability. Thus it can be concluded that the doctor’s negative test results could or could not be a valid source to determine whether the patient will not develop the SNP disease that is within their DNA.

Possible Sources of Error

1. Inaccurate images: The webcam that was set up did not have a clear enough resolution in a dark setting to take clear and accurate dark room photos, thus the pictures of the droplets were graining and could have contributed to error in the image processing. And in the process of analyzing bad images, the ImageJ program would over account and or under account for the fluorescence in the drop
2. Light exposed SYBR Green I solution: In the process of preparing each solution of DNA, the SYBR solution (which is light sensitive) could have been overly exposed to the dim lights of the room as the solutions were pipetted for the amount of trials and tubes, thus this could have affected the SYBR Green I solution's fluorescence capabilities in the later drops because they had been exposed to light lobger then the first few drops that were analyzed
3. Amount of PCR solutions and reagants: Before the detection steps, error was highly possibly during the PCR preparation and thermal cycler, especially adding the correct amount of reagent and DNA to the PCR mixture in the tubes, which would affect the amount of DNA that would bind to the SYBR Green to allow for the fluorescence, and this could be due to the newly learning pipetting techniques of new users of the pipettors

All in all, the Baynes values are affected by the the amount of DNA that was calculated using imageJ and the fluorimeter device, and within that program and that device, the main error would have to affected the fluorescence of the droplets in the fluorimeter. And the main error that could have affected the droplets would be on the PCR experiment and preparation for analysis of the DNA in question.

## Intro to Computer-Aided Design

TinkerCAD was a fairly simplistic version of SolidWorks. Both of these programs are 3D-spatial modeling software used to model a many number and sizes of devices, and these files that re created are used to then creat the actual device either through a seperate CAD machine, such as 3D prinitng or injection molding. This was nice in the sense that it wasn’t too difficult to put together a basic design, but it did make it slightly more difficult in some aspects. For example, lining up different components was more difficult in TinkerCAD because there wasn’t as specific measurements to input. For the sake of this project TinkerCAD worked great, as Solid Works would have been a little too advanced for something so simple. Overall, it was fairly simple to design the idea our group had thought up.

Our Design

Bio-Logic's OpenPCR and OpenAnalysis Machine:
Our design for the OpenPCR is primarily focused on completing the PCRs task in the most simplistic fashion. Instead of having a separate fluorimeter where it’s a hassle to do back and forth adjustments on two machines we simply combined the two. The red box on our design represents the fluorimeter which is attached directly to the OpenPCR. The top of the fluorimeter easily unravels to expose the interior allowing easy access to any samples being processed. A camera is also installed directly into the side of the fluorimeter so that there will be no hassle in adjusting a separate device to fit precisely to the fluorimeter. Our model includes much more simplistic design that allows a much more smooth and efficient transaction between the processes of both machines. Much time will be saved while making measurements, and the overall process will be much less of a strain on the operator.

## Feature 1 and Feature 2

### Feature 1: Consumables

Throughout the experiment, it was hard to keep track of the test tubes which made it easy to mix up despite their labeling. A proposed solution to this problem is to attach a labeled rack to the machine so that the primer and the testing tubes that are being used are clearly identified and are not mixed up with the other testing samples. The rack would still fit standard tubes meaning that the product would have no consumables.

### Feature 2: Hardware - PCR Machine & Fluorimeter

Our System:
Our innovation to the hardware of the Fluorimeter and the PCR machine is that they will be combined into one machine making the process easier and quicker. Using the current setup, it was time consuming and tedious transferring samples from the PCR machine to the fluorimeter and then calibrating the camera on the fluorometer to take the picture. Are proposed design will make it easy to transfer the samples between the two and solve the problem with the camera since it will be in a fixed position.

How it addresses the major weaknesses:
Our device has redesigned the PCR machine and the Fluorometer to address the major weaknesses. The product is adjusted in a way that the Fluorimeter and the PCR machine are combined in order to make the device easier to use. The fluorimeter and PCR are combined into one device so that the experiment is more reliable and efficient. When conducting the experiment it was difficult to move the substances around; therefore, we wanted to improve the design in a way that everything necessary was in one device. Also, throughout the experiment, it was hard to keep track of the test tubes which made it easy to mix up up the tubes, despite their labeling. A proposed solution to this problem is to label the rack so that the primer and the testing tube that is are being used are clearly identified and are not mixed up with the other testing samples. The rack would still fit standard tubes meaning that the product would have no consumables.