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Revision as of 02:07, 18 October 2018
C A P T I V A T E
CAPTURE THE DATA | ACTIVATE THE RESPONSE
2018 Lambert iGEM
Our Project
Vibrio cholerae, a pathogenic waterborne bacteria, impacts millions of people annually. Cases are most prevalent in developing countries with a lack of practical diagnostic methods and clean water. Lambert iGEM created a proactive, inexpensive diagnostic kit for V. cholerae detection utilizing frugal hardware devices and toehold switches. Utilizing rainfall, conflict, and cholera case/death data, CALM is able to accurately model the Yemeni V. cholerae outbreak, forecasting outbreaks weeks in advance. These riboregulators activate gene expression in response to predetermined RNA sequences. Engineering E. coli to detect V. cholerae, we targeted ctxB, a non-toxic subunit of a gene specific to all pathogenic V. cholerae. Our Chrome-Q system quantifies aquatic V. cholerae presence utilizing HSV values while the Color-Q app inputs data into our machine learning model, CALM. With this diagnostic kit, Lambert iGEM addresses V. cholerae epidemics using Yemen as a test case by predicting outbreaks, thus providing low-cost sustainable diagnostic tools while enhancing quality prediction.
This overview explains how the multiple parts of our project work together to be a proactive approach to preventing V. cholerae epidemics using Yemen as a test case. Our process begins with the CALM software predicting outbreaks up to 8 weeks in advance. Our kit with the necessary hardware, software and biosensor cells are pre-deployed to aid workers. Text messages are sent to the aid agencies who notify local workers to deploy the testing kits. Water samples are taken and filtered to extract cells in the size range of V. Cholerae. The cells are lysed and RNA or DNA is extracted (depending whether NASBA is available in field) The RNA/DNA is electroporated into the biosensor cells using our Electropen™. Biosensor cells are incubated for 24 hours after which the cell solution is pelleted using the 3-D fuge. The sample is loaded onto the Chrome Q base and using the Color Q App, the results are quantified and uploaded to AWS server which publishes the results. The results from the water sampling feeds into our CALM model completing a feedback loop to ensure continual model improvement.
