# BME100 s2015:Group1 12pmL6

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# OUR COMPANY

 Name: Jorge Hernandez Name: Jake Packer Name: Andre Nguyen Name: Kaylena Conklin Name: student Name: student

# LAB 6 WRITE-UP

## Bayesian Statistics

Overview of the Original Diagnosis System In an attempt to test each each subject, 34 teams with each about 6 people, were assigned two patients. PCR was preformed on the two patients in order to increase the amount of DNA with which to work. Then a test was done to detect the SNP (single nucleotide polymorphism) DNA in the patient's sample. Once the results from the test were gathered, various tests were done in specific the the Bayesian equation. In a lab such as this, there are many possibilities for error. These could be from the data itself to the calculations that were preformed. Attempts to decrease the error included throwing out any data that showed improper values for the positive and negative controls. Also included were controls in the PCR to controls in the ImageJ calibration. The ImageJ calibration controls were to determine the similarities and differences of our sample to samples that were known to be positive and negative. Pictures were taken during the ImageJ test to show how much the sample glowed and allow a visual representation of the data gathered. Several pictures were taken to allow for error in some of them. Some of the pictures could have been blurred or simply not taken as an accurate picture.

Bayesian statistics were employed to understand the data set. Calculation 1 explains the sensitivity of the system, or the ability of the system to come to a positive conclusion about a subject given a positive PCR reaction. The Bayes equation was utilized, giving a value close to 100%. This explanation and value show a great ability by the system to be sensitive to giving a positive conclusion given a positive reading. Calculation 2 recounts the PCR system's ability to have specificity in finding results. Negative conclusions given a negative signal were computed, giving a value less than the machine's sensitivity ability, but still a number close to 100%. This calculation hits upon the machine's ability to throw out negative samples as just that: a negative conclusion given a negative signal. The possible human errors that brought these value below the ideal 100% mark include simple input errors; mixing up values upon input on the data table, negligence regarding labeling of test tubes, and miscounting the number of positive conclusions/positive reactions while calculating the Bayes value.

Calculation 3 showed a sensitivity ability to PREDICT the disease upon computing the Bayes value. The development of the disease hinges upon many other biological factors, including other SNPs of the specific individual's genome that may offset the SNP in question. The Bayes value (in percent) for this sensitivity to predict disease was the lowest value computed of the four calculations. The reasoning for that low value is the machine's inability to calculate and analyze the other possible variables in the genome that may affect development of the disease. Calculation 4 explains the specificity of the PCR process to predict the disease. Nearly the same as the sensitivity ability explained above, this specificity reflects the machine's ability to throw out negative "development of disease" readings and instead focus solely on the readings that come back positive. The ability of the process to do so reflected a Bayes value above its sensitivity to predict the disease, but still well under the ideal 100%.

## Computer-Aided Design

The TinkerCAD tool is an online program that allows users to create their own 3D designs. For this lab, TinkerCAD was used to transfer designs that Dr. Haynes had already created. These were used in order to find the dimensions of the piece that the lab group wanted to modify.

Our Design

## Feature 1: Consumables Kit

PART 1: Assessing the original design:

Consumables (i.e plastics, pipettor, and reagents): Strengths: The pipettor, when used properly, receives and dispenses accurate amounts of liquid. Pipette tips are cheap, and although they are used frequently, the re-use could result in contamination.

Weaknesses: The pipettor is expensive, the plastic containers can be difficult to open (especially when wearing gloves).

HOW TO FIX THE CONSUMABLES KIT:

Something small, but possibly important to reduce spillage is an automatic button on the tops of the aliquot containers that opens upon release. Clicking in the button releases a mini hold that opens the container.

Also, any research to create re usable tip would be extremely groundbreaking. The use of plastic of these tips for one use apiece is cost consuming.

## Feature 2: Hardware - PCR Machine & Fluorimeter

PART1: OpenPCR

Strength: Creates almost innumerable copies of the strand in question. This main feature is the backbone of the system.

Weakness: TAKES TIME! 2-3 hours at minimum.

IMPROVEMENT: Add heating and cooling systems that can more quickly fluctuate the inside temperature of the OpenPCR. If properly controlled, these systems could dramatically decrease the time used for PCR.

Fluorimeter System

Strength: Provides clear pictures of SYBR Green when reacted with sample+primer. Also, slides have a multi-use function to minimize waste (and cost), i.e slides are reusable (up to 5 drops of solution).

Weaknesses: Monotony/ease of human error due to length of procedure. SYBR green has high sensitivity to light.

IMPROVEMENT: Completely recognizing that these may already exist, an automated, or semiautomated fluorimeter would be incredibly useful to assessing images taken using the SYBR green/primer. A immobile camera setup (mitigating the need to calculate distance between camera lens and SYBR green/primer droplet) would be set up to work side by side with the fluorimeter black-box. The camera would have an automatic setting that snaps three quick pictures without having to re-open the box and possibly jarring the camera angle. The camera setup is also equipped with a USB port for quick transfer to a flashdrive for image analysis