Difference between revisions of "Team:Lambert GA/Measurement"

Quantification of reporter expression is a crucial component in synthetic biology, with measurement tools such as plate readers present in laboratories across the world. However, these devices are often difficult to incorporate into settings beyond well-equipped laboratories, such as infield settings, creating a need for novel portable systems for reporter quantification. Within Captivate, this necessity is manifested in the form of reporter quantification from the cholera biosensors to distinguish between positive and negative tests. Within the LacZ model, the distinction between blue and white, including even slight variances, requires a suitable device for quantification. Lambert iGEM’s QSystem addresses this need through a low-cost illumination chamber for standardized image capture of samples called Chrome-Q and subsequent analysis of samples using RGB color theory through an app called Color Q.

Our app, Color Q, generates a relative percentage value, which is calculated from the color variance from a control sample. However, we need to determine what relative percentage value, or threshold, constitutes as a positive test result because a water sample could have a very low concentration, which would not make those who drink it at risk for contraction.

The infectivity rate for Cholera varies from 104 to 1011 bacterium ingested, depending on the strain and conditions of the host [3]. In order to be health protective, we want a negative result to mean that there is less than 10³ bacterium ingested per person from a water source. Therefore, our threshold is 10³ bacterium per two liters of water, the average water intake per day. For our biosensor, a 100 ml water sample is taken, so for a positive test, there must be 50 bacterium in the sample, with some variability due to the uneven spread of cholera in a water source.

Then to determine the relative percentage value for a positive test, we will carry out our biosensor protocol on water samples with known concentrations of Cholera. The samples would be triplicates of concentrations of 0, 25,40, 50, 60 and 100 cholera cells per 100ml to see the change in the relative percentage value with differing concentrations. After using our software, we would average the relative percentage value for the samples with a concentration of 40, 50, 60 cholera cells to receive a threshold value. If a sample in the field meets or exceeds the value determined, it would be a positive sample.

Sensitivity and specificity give insight into the accuracy of disease detection tests. Sensitivity is defined as the percentage of positive test results where it is confirmed that Cholera is present (true positives) out of all of the positive samples, true positives and false negatives [1]. Specificity is the percentage of negative test results that are confirmed to have no Cholera present (true negatives) out of all the negative samples, true negatives and false positives [1]. To find these percentages, we must have another reliable detection method that we will compare our results to. According to the CDC, growing a culture of the sample is the gold standard for Cholera detection [2]. Therefore, we will culture the water sample as well as test our biosensor cells and compare the results to find our test’s accuracy. The results from the culture will be the ‘truth’ of whether the sample really has Cholera or not. Therefore, if the culture is positive and our test’s results are positive, it is a true positive, but if the culture is negative and our results are positive, it is a false positive, and it is the same for true negatives and false negatives. [1]

	Cholera Present	No Cholera Present
Positive Test	True Positive	False Positive
Negative Test	False Negative	True Negative

Once the data collection is completed, there are two formulas to calculate sensitivity and specitivity.

• Sensitivity= true positives/(true positives + false negatives) x 100 [1]
• Specificity=true negatives/(true negatives + false positives) x 100 [1]

The purpose of our biosensor is to detect Cholera at the source to prevent contraction. Therefore, we are more invested in a high sensitivity rather than specificity. With a higher sensitivity, it is more likely to have false positives, but it is safer to have false positives and take precaution than to have false negatives and contract the disease.

Color Q is a free mobile application developed in Java for the Google Play Store. The app was developed in the Android Studio v3.2.1 integrated development environment. It works alongside the Chrome Q hardware also developed by Lambert iGEM in order to effectively quantify the result of a biological reporter, similar to the function of a plate reader. The app is able to use circle detection in order to find the samples on the base of the Chrome Q hardware and then detect the red, green, and blue values (RGB) of the center of each circle. The way the circles are arranged allow for a range of values to be generated. The first row contains 4 circles. The average RGB values of the first two circles are calculated as the negative control and the average RGB values of the second pair of circles are calculated as the positive control. The distance between the positive and negative control is calculated in the 3D-coordinate plane using the following formula:


The Hough Circle Transform is used as the method of circle detection found in the app. Open Computer Vision (OpenCV) is a library that can be imported into Android Studio in order to perform image analysis-based methods. The image taken by the smartphone camera is converted into a grayscale photo. This essentially makes the image more readable in terms of edge detection and "round" estimation. There are several parameters that can be modified and calibrated in order to detect an accurate amount of circles:

• Maximum Radius: The smallest value for the radius of a detected circle
• Minimum Radius: The largest value for the radius of a detected circle
• Minimum Distance: The smallest distance between the centers of any two detected circles
• Edge Gradient Value: The roundness of each detected circle
• Threshold Value: The amount of memory the system has to store the detected circles

The 6 by 6 grid located underneath the first row can be loaded with experimental samples and a percentage value can be determined on a scale from the negative control to the positive control. The circle detection process loops through until the maximum radius reaches 120 pixels. If anywhere from 35 to 40 circles are detected in total, then the loop stops. However, if there are fewer circles detected, then the loops restarts to finish through the maximum radius until anywhere from 25 to 40 circles are detected properly. If less than 25 circles are detected, then an error is caught and another picture is requested to be used. Zooming in or zooming out could possibly make the circle detection process easier for the system and more efficient. The following formula is used to calculate the relative percentage values:


The results are then displayed based upon the row in which the circles fall in on the base of the Chrome Q hardware. The app is able to determine the row in which the circles fall in by comparing y-coordinates. If the y-values are similar to each other, then the circles are classified as being on the same row. The relative values are then transferred to another page within the app where the user is able to enter information that could help contribute to our machine learning model, CALM. The application uses the latitude, longitude, and timestamp values obtained from the phone's GPS to effectively determine where and when the test was run. When the user submits the data, the results are sent to a MySQL database, which is a part of the Relational Database Service (RDS) as a part of the Amazon Web Services (AWS) platform.

GITHUB RELEASE: https://github.com/igemsoftware2018/Team_Lambert_GA/releases/tag/1.0.0
GOOGLE PLAY STORE: https://play.google.com/store/apps/details?id=com.plokia.android_camera