BME100 f2017:Group8 W1030 L6

From OpenWetWare
Jump to: navigation, search
Owwnotebook icon.png BME 100 Fall 2017 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
BME494 Asu logo.png


OUR COMPANY

Name: Kristin de Jesus
Name: Jennifer Brodsky
Name: Elizabeth White
Name: Jacob Hayes
Name: John Carey

GaugeCo

LAB 6 WRITE-UP

Bayesian Statistics

Overview of the Original Diagnosis System

Thirty four patients were tested by, 16 groups of 5 students, for the disease-associated SNP through multiple PCR experiments. Each group was assigned two patients, and given 3 replicates of DNA for each patient. The DNA was then put inside a thermocycler which replicated the DNA multiple times. Photos of the DNA samples were taken with a fluorometer, and were amplified for analyzation. Two group members combined the PCR with SYBR Green, and used the fluorometer to capture images of the droplets of each sample. Then, one member used the program ImageJ to enhance, manipulate and calculate the pictures of each droplet. ImageJ provided both qualitative and quantitative data which was used to determine if the patient had the disease-associated SNP.

Many steps were taken in order to prevent error with the DNA samples, as well as the calibration samples, for the PCR reaction. For each patient, three replicate DNA samples were used, allowing for three trials. Positive and negative controls were examined to compare the calf thymus and patient samples. Three images of each sample were captured under the light box when the fluorimeter. The purpose of the light box was to cancel out exterior room lighting in order for the SYBR Green to fluoresce its complete potential. SYBR Green is a nucleic acid marker. This dye fluoresced when when the sample being examined tested positive. Micropipettes were used to achieve accuracy when measuring samples of solution that would be examined. The efforts stated were taken to prevent error in order to achieve accuracy in this lab.

Based on the class's final data spreadsheet, it can be seen that the probability of a positive PCR reaction, given a positive final test, is 0.875. This indicates that for more than half of the time, a positive final test conclusion will be reflected by a positive PCR reaction. On the other hand, the probability of getting a negative PCR reaction, given a negative final test, is 0.923, meaning that a greater frequency of negative PCR reaction test produced a negative test result when compared to the positive tests. Furthermore, the probability that patient tests positive, given the patient develops the disease, was 0.547, while the probability of the patient testing negative, given that the patient will not develop the disease, was 0.769. Error in these calulations could have been a result of the two inconclusive results for the overall patient diagnosis and the inconclusive results that produced them seeing that if the results were found they could alter our data. These inconclusive conclusions are a result of a drop producing data that supports both a negative and positive diagnosis. There was not any blank data that affected the calculations of the frequencies used calculate the Bayesian Statistics from this experiment, though some data sets were missing the concentrations of the positive and negative controls indicating there could have been missteps when the results were formulated.

What Bayes Statistics Imply about This Diagnostic Approach

Calculations one and two analyze how probable it would be to get a positive or negative final test conclusion, given a positive or negative PCR reaction. Seeing that the probability of the patient getting a positive final test conclusion, given a positive PCR reaction, was calculated to occur about 75% of the time, it is pretty reliable to infer that a positive PCR reaction will result in a positive final test conclusion. On the other hand, a patient getting a negative final test result, given a negative diagnostic signal, occurred closer to 90% of the time, implying that a negative diagnostic test signal almost always guarantees a negative final test conclusion. Seeing that the probability for receiving a negative test conclusion was higher than that of a positive final test conclusion based off of the PCR reactions, the negative results could be considered more reliable.

Calculation 3 determines ​the​ ​probability​ ​that​ ​a​ ​patient​ ​will​ ​develop​ ​the​ ​disease,​ ​given​ ​a​ ​positive​ ​final​ ​test conclusion. Based on the results the probability that the patient tests positive given the patient develops the disease was around 60%. The probability that the patient develops the disease given the patient tests positive was also around 60%. Calculation 4 determines the probability that a patient will not develop the disease, given a negative final test conclusion. The probability that the patient tests negative given that the patient will not develop the disease was about 80%. While the probability that the patient does not develop the disease given the patient tests negative was close to 100%. Overall, the results for both calculations proved that the reliability for predicting the development of the disease was accurate.


Three possible sources oh human or machine/device error that could have occurred during the PCR and detection are as listed:

  1. During the setup for PCR, contamination between samples could've occured if micro-pipette tips were not discarded and reapplied after every sample, meaning that negative samples could have contaminated positive samples, diluting their results.
  2. When gathering photos for image analysis, the flash from the camera could've bleached the DNA samples, white-washing the image, resulting in a white image than what it should have been.
  3. When using ImageJ, the parameters for the sample could have not been set properly, including some of the darker space, skewing the results.

Intro to Computer-Aided Design

3D Modeling
TinkerCAD is a user-friendly system used for creating three-dimensional designs. This system is web-based, as no downloads or programming are required. SolidWorks requires a download, and is difficult to use because of the specificities needed. The body-bottom section of the OpenPCR machine was provided and used as a base to add the backing. Although importing shapes using TinkerCAD may be difficult, designing from step one is much easier. The TinkerCAD system is user-friendly because it begins in three-dimension, while SolidWorks begins in one-dimension.


Our Design

PCRbme100.jpegPCR2bme100.jpeg


The basic structure of GaugoCo.'s PCR machine is similar to the OpenPCR. However, there are a few modifications meant to improve accuracy and efficiency of the system. Two cones are added under the lid of the machine. These cones are meant to prevent cross contamination of the samples placed into the PCR system for the reaction to occur. A square programming chip is added to the machine. Because the original OpenPCR design uses several wires and circuits, malfunctions can easily occur and disrupt the process. The programming chip can transfer the results from the PCR reaction into an appropriate PC for examination, thus eliminating error and increasing accuracy of the samples. To decrease the cost of the product, the system will be made of wood and no color will be added. This improves the price which appeals to customers.



Feature 1: Consumables

GaugeCo.'s PCR machine comes with a consumable kit that includes various liquids as well as tools used to complete the reaction. The liquids include PCR mix (concentrated taq DNA polymerase, 200 µl), primer solution (200 µl), SYBR Green solution (200 µl), and buffer (500 µl). Twenty plastic tubes will be provided: 8 tubes of buffer solution, 2 tubes of SYBR Green, and 10 empty tubes. These tubes will be small enough to fit inside the PCR machine. Extra empty tubes are provided for back up in case an error were to occur. Thinly glass slides will be found in the kit, which will be used to examine the DNA and SYBR Green drops under the fluorimeter. The kit itself will consist of a light box which will be used when examining the fluorimeter.

A micropipette, as well as micropipette tips, will not be included. These must be purchased by the customer in order to achieve accuracy in measurements.

The glass slides were modified to be more thin than the original slides. The purpose of this is to prevent error when examining the DNA and SYBR Green drops. Because the previous glass slides had a greater height, they had to slide tightly against the sides of the fluorimeter. The surface tension of the liquid sample broke, causing the sample to lose its shape. This led to examination difficulties. Altering the glass slides to be thinner will allow for a smooth slide into the fluorimeter, which will ultimately prevent error during the sample examination process.

Feature 2: Hardware - PCR Machine & Fluorimeter

GaugeCo is creating our DNA-analyzing machine even better and more convenient than ever. By combining an Open PCR machine and fluorimeter into the body a single machine. An automated system within the body of our new, combined design, will allow our customers ease during experimentation. The two cones under the lid is a mechanized pipetting system that minimizes human error and pipetting mistakes while also reduces the cost of pipetting tips. In order to address a major weakness in the consistency of pictures taken during experimentation, our team designed a camera rig within the fluorimeter-side of our combined design that accommodates a wide range of smart-phone sizes and our compatible iPhone and Android app syncs with our combined design's system to maximize photo consistency.