The international Genetically Engineered Machine contest
The international Genetically Engineered Machine (iGEM) contest started in 2004 and has since grown into a global competition involving over 5000 students and 300+ teams. iGEM is a synthetic biology competition in which teams design and implement engineered biological systems in both basic as well as applied areas or research. For example, current iGEM tracks include: Diagnostics, Energy, Environment, Food & Nutrition, Foundational Advance, Information Processing, Manufacturing, New Application and Therapeutics. The EPFL iGEM teams generally consist of an interdisciplinary team of 10 – 12 students from different Schools. The EPFL iGEM team is active from early February to October/November when the Giant jamboree is held in Boston, USA. During the spring semester the students develop and fine-tune a project idea and learn basic synthetic biology lab techniques. During the summer the students work full-time on implementing their project in the lab and present their results at the Giant Jamboree in October/November.
The EPFL teams have been quite successful at the iGEM competitions since participating in the competition for the first time in 2008. So far, the 10 EPFL teams were awarded 1 bronze, 2 silver and 7 gold medals, and won the “Best New BioBrick or Device, Engineered” prize in 2009 and the iGEMers prize in 2010. The 2017 EPFL iGEM team was nominated for Best Diagnostics Project, Best Education and Public Engagement Project, Best Integrated Human Practices, Best New Basic Part, and Best New Software. They ultimately won the award for Best Education and Public Engagement Project. The 2018 EPFL iGEM team was nominated for Best Therapeutics Project and Best Software Project. The magazine Popular Science featured the 2013 project as one of the nine “coolest” iGEM projects (link). More information on each team, and their respective project can be found here: 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018.
Website iGEM : https://igem.org/Main_Page