Team:CCU Taiwan
Welcome to iGEM 2017!
We are CCU_Taiwan from National Chung Cheng university in Chiayi, Taiwan
We apologize for the unpleasing appearance of this wiki page.
Taiwan, having one of the finest health insurance systems in the world, it has some issues nowadays. Established in 1995, the health insurance has been covering people for over 20 years, on the one hand saving money and lives, but on the other hand costing a significant amount of government funding at the same time. This glamorous system, as a matter of fact, leads to lots of serious problems, for instance, fiscal deficits for the government, hospitals forced to cut budgets, doctors getting less payment, etc. (et cetera). Those problems are causing our medical system to go downhill. So this year, we want to do our part to make a small improvement in this serious situation.
We aim at cavity. Cavities, a well known and familiar disease in everyday life, it is actually an easily ignorable health insurance cost in Taiwan. It only amounts to 1.6% of the whole health insurance cost, which seems to be a really small amount, right? But do you know the exact number? In fact, it costs Taiwan 5 billion US dollars per year. What an enormous and horrifying number that is. This is why our team wanted to attack cavities, allowing people to care for and check their own oral condition regularly in order to maintain health and also reduce health insurance cost at the same time. Now, allow me to proudly introduce our product, the Carindex.
Carindex is an abbreviation of caries and index, providing an index of the likelihood of cavities. Carindex consist of both biosensor and non-biosensor components. It can detect the bacteria concentration, lactic acid, pH level, and by computing the data with machine learning, it gives an index of the risk of caries, or cavities.
Biosensor
We choose S. mutans and lactic acid as targets because the main cause of cavities, lactate, is secreted by S. mutans and Lactobacilli. In different stages of cavities, the levels of the two bacteria and lactate would be different. At first S. mutans attaches to your teeth, then secretes lactate as it grows. The acidic environment would degrade the enamel, and drill a “hole” on it, revealing the dentin. Next, the hole serves as a shelter for Lactobacilli, which cause dentin decay. But we can’t use Lactobacilli as a target, because there are many types of Lactobacilli in our mouth, so it’s hard to find a single test for all of them. So we chose the levels of S. mutans and lactate as parameters to determine the risk of cavities.
How do we detect those? For S. mutans, we know the level of S. mutans is positively correlated with CSP, so by detecting CSP, we can determine the S. mutans level. In this study, we clone the quorum-sensing system of S.pneumoniaeinto B. subtillis to detect CSP.
In B. subtillis, the CSP will bind with the membrane receptor, comD, and the CSP-comD complex phosphorylates comE, then the phosphorylated comE will trigger the production of GFP. To achieve this strategy, we separate the system into two plasmids. One of them, consists of comD and comE. The other one has phosphorylated comE triggered promoter and GFP. So the “light” will be turned on if CSP is present, and we can detect the light intensity to quantize the CSP, then infer the concentration of S. mutans.On the other hand, we haven’t yet found a way to quantize the level of lactate, but we are still trying hard.
Non-Biosensor
In our mind, our device will look like a box with a built-in sensor which is used to detect the targets’ concentration. Besides, detecting the light intensity generated by the bacteria, we also focus on some non-biological factors, such as pH value. After assuring each target has a positive correlation with cavities, we figure out a simple way to detect them. We will also apply machine learning to enhance our accuracy.
For the device’s modeling, in bio-sensing, we will place saliva in two test tubes; each tube contains a type of bacteria, the reason why we are doing this is to avoid the two types of bacteria from affecting each other. And for the non-bio-sensing, if the saliva used above, remains the same property, it will be detected by the non-bio-sensors. As for the data modeling, dry lab will cooperate with wet lab in the experimental stage on predicting the experiment results and modeling the experimental data. And after the solid product is produced, dry lab will model its detection’s accuracy.
