iGEM Toronto aimed to develop a genetic switch controlled by light using a novel fusion protein LacILOV, to control the powerful CRISPR/Cas9 gene editing technique. Read more here.
iGEM Toronto proposed to use synthetic biology to improve another aspect of gold mining: gold prospecting and detection. Implementing an existing method of biosensing, we created biological reactive paper-sensors that would allow cheap, easy, and environmentally friendly detection of gold deposits in geographically large regions. Read more here.
iGEM Toronto proposed to develop a new cost-effective bioremediation technology to reduce the accumulation of toxins from oil sands tailings and increase the rate of tailings ponds’ reclamation activities. We created a genetically modified Escherichia coli bacterium that will metabolize toluene toxins, develop a pro-gram simulator to show the potential of its use in tailing ponds, and formulate a policy framework for the industrial use of this technology. Read more here.
iGEM Toronto proposed to use a “self-deleting” CRISPR/Cas9 plasmid as an alternative approach for genetic safeguard system. Read more here.
iGEM Toronto characterized in detail the decision-making machinery in E. coli that decides between a stationary, low growth state and a mobile, high growth state. We developed a semi-high throughput procedure to measure several biochemical parameters in parallel in a microtiter plate format and characterized wild type and knockout strains, as well as strains that overproduced relevant factors through expression plasmids we had constructed, in a multitude of stimuli conditions. Read more here.
iGEM TorontoMaRSDiscovery has taken an interdisciplinary approach to systematically investigate how nature implements metabolic channeling and how this knowledge may be exploited for biotechnological applications, such as the production therapeutic molecules and biofuels and the degradation of toxic wastes. Read more here.
iGEM BlueGenes developed a simple two-input-one-output light directed feedforward neural network using E. coli cells, with the ultimate goal of being trained to function as different types of digital logic gates. Read more here.
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