# BME103 s2013:T900 Group4 L3

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# OUR TEAM

 Name: Kinjal Ahir Role:Protocol Name: Zach Young Initial Machine Testing Name: Anna Essex Initial Machine Testing Name: Tuan Phan Research and Design Name: Amelia Lax Research and Design

# LAB 3 WRITE-UP

## Original System: PCR Results

PCR Test Results

 Sample Name Ave. INTDEN* Calculated (μg/mL) Conclusion (pos/neg) Positive Control 1,450,385 1.60 pos Negative Control 488,789 0.18 neg Tube Label: B2 Patient ID: 17818 rep 1 709,603 0.51 pos Tube Label: C2 Patient ID: 17818 rep 2 642,405 0.41 pos Tube Label: D2 Patient ID: 17818 rep 3 417,721 0.07 neg Tube Label: B1 Patient ID: 85158 rep 1 450,174 0.12 neg Tube Label: C1 Patient ID: 85158 rep 2 387,850 0.03 neg Tube Label: D1 Patient ID: 85158 rep 3 376,360 0.01 neg

* Ave. INTDEN = Average of ImageJ integrated density values from three Fluorimeter images

Bayesian Statistics
These following conditional statistics are based upon all of the DNA detection system results that were obtained in the PCR lab for 20 hypothetical patients who were diagnosed as either having cancer or not having cancer.

Bayes Theorem equation: P(A|B) = P(B|A) * P(A) / P(B)

Calculation 1: The probability that the sample actually has the cancer DNA sequence, given a positive diagnostic signal.

• A = frequency of cancer-positive conclusions = 9 / 20 = 0.45
• B = frequency of positive PCR reactions = 26 / 60 = 0.43
• P (B|A) = frequency of positive PCR given cancer-positive conclusion = 24 / 26 = 0.92
• P(A|B) = 0.96 = 96%

Calculation 2: The probability that the sample actually has a non-cancer DNA sequence, given a negative diagnostic signal.

• A = frequency of cancer-negative conclusions = 11 / 20 = 0.55
• B = frequency of negative PCR reactions = 34 / 60 = 0.57
• P (B|A) = frequency of negative PCR given cancer-negative conclusion = 31 / 34 = 0.91
• P(A|B) = 0.88 = 88%

Calculation 3: The probability that the patient will develop cancer, given a cancer DNA sequence.

• A = frequency of "yes" cancer diagnosis = 9 / 20 = 0.45
• B = frequency of "pos" test conclusion = 26 / 60 = 0.43
• P (B|A) = frequency of pos given yes = 24 / 26 = 0.92
• P(A|B) = 0.96 = 96%

Calculation 4: The probability that the patient will not develop cancer, given a non-cancer DNA sequence.

• A = frequency of "no" cancer diagnosis = 11 / 20 = 0.55
• B = frequency of "neg" test conclusion = 34 / 60 = 0.57
• P (B|A) = frequency of neg given no = 31 / 34 = 0.91
• P(A|B) = 0.88 = 88%

## New System: Design Strategy

We concluded that a good system Must Have:

- easily determined results: The easier the results are to read accurately, the less likely a misdiagnosis in either direction. It is undesirable both to give a false negative, where a patient is not treated when care is needed, or to give a false positive, wasting time and resources on those who do not need them. This aspect is central to any diagnostic tool.

- Simple OpenPCR Software: Simplicity increases ease and efficiency in lab experiments and hopefully leads to faster diagnoses. It also makes troubleshooting easier should problems arise. The more straightforward the system, the more quickly users can learn to use the machine.

We concluded that we would Want a good system to have:

- Low cost: Currently an OpenPCR machine costs \$599 and a Fluorimeter costs \$300. An inexpensive material would help reduce cost and increase accessibility, since there is always a limited budget for new equipment. This would not only allow users to increase the amount of tests that can be run at the same time, but also boost sales, which is important for marketing any device.

- integrated camera: phone cameras are easily moveable and vary in size and quality, leading to differing results. Smartphone camera settings can be time consuming or nonexistent. Having a built-in camera increases cost, but it is worth it to increase speed and accuracy. Furthermore, the program is simpler because it does not have to adjust to different cameras and phone sizes and shapes vary enough to make building a cradle to fit them difficult.

We concluded that a good system Must Not Have:

- Troublesome USB Connectivity. USB connectivity should function well in order for OpenPCR machine to work.

- Casing = fire hazard. High temperature with PCR can be dangerous.

We concluded that a good system Should Avoid:

- Avoid slow amplification.

- Hard to adjust phone/ fluorimeter. The phone can be easily moved by accident, which requires readjustment between the phone and the fluorimeter.

## New System: Machine/ Device Engineering

SYSTEM DESIGN

Current design of fluorimeter

Rather than drastically change a fairly-efficient PCR machine, we decided that the fluorimeter setup was more in need of modification. The only change to the PCR machine would be improved USB ports, but the fluorimeter would have a built-in camera to remove the complications of positioning a camera phone. The phone would still be used to run the machine, but it wouldn't directly take the pictures. This new camera would take the place of the current cradle and be at a fixed position in respects to the fluorimeter for most efficient photographing. Also, the slots on the board of the fluorimeter would be labeled to avoid confusion in the process of analysis.

KEY FEATURES

Fluorimeter - We chose to include these new features:

• Integrated Camera - helps reduce inconsistency of photography and time-consuming difficulty of positioning
• Labeled slots - reduces likelihood of error from misidentified photographs

PCR Machine - We chose keep these features the same as the original system:

• Reliable Hardware - the machine is sturdy and does its job efficiently considering its simple construction
• Preexisting Software - the current Open PCR software is well developed and user-friendly

INSTRUCTIONS

• Step 1: Connect the camera unit to the fluorimeter.
• Step 2: Adjust the camera settings according to the current experiment.
• Step 3: Link the camera to the phone being used to control the experiment.
• Step 4: Take photo.
• Step 5: Upload photo for necessary manipulation.

## New System: Protocols

DESIGN
We chose to modify the hardware of the fluorimeter. However, overall protocols should remain the same. As the PCR machine was not modified, its protocols will also remain unaltered.

MATERIALS

 Supplied in the Kit Amount Camera Unit 1 Reaction mix 400μL Battery 1 Software freeware
 Supplied by the User Amount Filter water 1,000μL SYBR Green 2,000μL Primers 4,000μL DNA sample (negative and positive) 400μL

PROTOCOLS

• PCR Protocol
1. Obtain and label 8 50μL DNA samples (4 each from 2 patients, positive control, negative control) and 8 50μL tubes of PCR reaction mix
2. Set micropipette to 75μL and attach disposable tip
3. Transfer all of the liquid from positive control DNA sample to a reaction mix tube, discard tip, label tube
4. Repeat for the remaining 7 DNA samples
5. Set the PCR program to run three stages
 Stage Number of Cycles Temperature (°C) Duration 1 1 95 3 minutes 2 35 95 30 seconds n/a 57 30 seconds n/a 72 30 seconds 3 n/a 72 3 minutes Final hold n/a 4 n/a
6. Load the mixed tubes into the PCR machine, close the lid, run the program
7. Remove tubes at end of program

• DNA Measurement and Analysis Protocol
1. Obtain a tray of sample tubes (8 buffer, 2 SYBR GREEN, 1 H2O, 5 calf Thymus DNA, 8 PCR reaction samples)
2. Set micropipette to 120μL and attach disposable tip
3. Transfer all of the liquid from positive control PCR sample to a buffer tube, discard tip, label tube
4. Repeat for the remaining 7 PCR samples
5. Calibrate the fluorimeter using the calf thymus DNA samples of known concentration
• Set up the integrated camera and adjust the settings, connect the smartphone to the camera for control
• Place 80μL of SYBR GREEN I onto the slide so it forms a definite drop, add 80μL of DNA, align with the LED
• Cover the fluorimeter with the light box and use phone to take picture
• Remove the box, remove the drop, export picture to computer for analysis (Make sure to label!)
6. In ImageJ, adjust the settings and split the color channels of the image to select green
7. Draw an oval around the image and measure, repeat for background
8. Repeat for the remaining concentrations of calf thymus DNA
9. Use these readings and the known DNA concentrations to create a graph with a linear fit for calculating concentration based on INTDEN values
10. Using the same procedure, repeat with the unknown DNA samples from the patients and use the graph to calculate DNA concentration

## New System: Research and Development

BACKGROUND

CHEK2 is a gene located at chromosome 22. It provides instructions for making protein called checkpoint kinase 2, a tumor suppressor. This particular protein responds to damage in DNA, preventing the cell from entering mitosis when the cell's DNA deviates from normal. Mutations of CHEK2 gene can lead to breast cancer, Li-Fraumeni syndrome, and other type cancers and diseases.

DESIGN
In our design, we chose to use primers for both normal and cancer-associated DNA sequences so users can cross-check their results. For example, if a patient's test returns positive for the cancer-associated allele, the test can be run with a normal allele primer to ensure that the test results were accurate, in which case the test for the normal allele should be negative. This addition of primers associated with the normal sequence allows users to test for the presence of normal DNA if they suspect that the cancer-associated DNA is not present. This helps fulfill our goal of making a more reliable test. It takes more time to run more samples, but it provides an extra layer of caution to help ensure correct diagnosis.

Primers for PCR
Normal Allele
Forward Primer: TATGTATGCAATGTAAGAGTT
Reverse Primer: TGAACCACTGGTGAAAAGAAC

Cancerous Allele
Forward Primer: ATACATACGTGACATTCTCAA
Reverse Primer: TGAACCACTGGTGAAAAGAAC

Our primers address the following design needs

• accuracy of diagnosis: As we considered diagnostic accuracy absolutely essential to a new system,
• Design specification 2 - explanation of how an aspect of the primers addresses any of the specifications in the "New System: Design Strategy" section
• Etc.

## New System: Software

As has been seen by the several groups who already have software in development, the need for more efficient PCR and image analysis capabilities are growing. For our particular machine, an app allowing a smartphone to control the integrated fluorimeter camera would be most essential, and ideally this app could also perform image analysis, lessening the complication of transferring large quantities of images that all look very similar to the human eye.

As ImageJ is a Java-based program, converting it to an Android app would be fairly straightforward. Similarly, channeling a phone to control an external camera is nothing particularly revolutionary but would greatly increase the efficiency and accuracy of this lab. Open PCR software could also be streamlined to run more quickly and give more accurate readouts during runtime. In reality, software modifications would provide the most improvement for this lab with relatively little effort.