Team:ETH Zurich/Results

  • We will attend the Giant Jamboree in Boston, present a slideshow and a poster about our project, and run a stand where we will showcase our robot AROMA to the public.
  • We wrote up a complete Attributions page which clearly states the involvement of the students, advisors, PIs, and every other person collaborated in the project.
  • We submitted the Judging Form right on time.
  • We successfully participated in the InterLab study and created a page detailing the results of our measurements.
  • In total, we submitted 28 Parts of our own design to the Biobrick registry. Especially for our fusion protein CLuc-TeTR (BBa_K2845048) and for EPIC Luciferase under the control of pOmpC (BBa_K2845007), we were able to present thorough characterization.
  • We participated in an InterLab study from the iGEM team Marburg, mentored a team from Rio de Janeiro and coordinated our project with the team from Utrecht. Find out more on our Collaborations webpage.
  • For Human Practices, we consulted not only experts but also engaged with critical audiences like Greenpeace and the general public to evaluate the societal benefit of our project.
  • From the beginning of our project we integrated the knowledge of experts into the design and workflow of our project. After learning about general concerns (i.e. the containment of GMOs) by the public, we went on and integrated these insights into the design of our robot. For this, we collaborated with the Department for Safety, Security, Health and Environment of our university and refined the mechanical specifications of our robot. Furthermore, we investigated possible options for biological containment systems which can, in the future, be integrated into our biosensors. Learn More
  • First, each part of our system was modeled separately with the help of experimental data to guide the design of our project. This includes not only the biological biosensor systems but also all mechanical parts of our project ranging from molecule uptake over image processing up to source localization algorithms. Subsequently, we integrated all components into an overall model which allowed us to capture the behavior of our entire system.
  • At the very end of our project, we were more than happy to see that not only in theory (i.e. in our models) but also in reality, our robot was able to navigate only by the input given from cell-imaging data. Find out more.
  • We designed, assembled and tested a LCCSM (Low Cost Casing System for Microscopy) which allows brightfield imaging with a resolution down to single-cell level on a small platform like AROMA. Furthermore, it also provides a modular platform for the assembly of a lensless or luminescence microscope.
  • For the operation of genetically modified bacteria in the field, we designed a robotic platform, which enables the integration of cell-based biosensors onto a robotic device by directly processing their output in an elegant way.
  • The heart of our system is a microfluidic chip which we developed to control the encountering of our engineered bacteria with bubbled medium, while allowing an easy readout by our microscope.
  • To control the medium exchange on our robotic device, we designed our own DIY syringe pumps.
  • For the mass transfer of the target molecule from air into medium, we designed and built a bubbling device, allowing the integration of this step on a small scale.
  • For the read-out of our engineered biosensors, we developed an image processing software. It can analyze obtained microscopy data (i.e. luminescence or tumbling frequency) and compute the respective input.
  • For controlling our whole robot, we further developed a software package that takes care of autonomously planning its movement and controlling its components such as our syringe pumps, bubbling device, microscope and actuators.
  • As our overall aim was to detect sources of pollutants, we also designed an algorithm which allows AROMA to reliably track molecules to their source, which we iteratively refined with the help of our integrated model.
  • We successfully employed E. coli’s highly sensitive and rapid chemotaxis system to sense molecules of interest in a matter of seconds by tethering them to a glass surface and analysing their rotational direction.
  • We devised an additional very fast biosensor design that employs split luciferase complementation upon binding of a transcription factor to DNA.
  • We established an assay for efficiently screening very large mutant libraries and optimized protocols for library generation in order to lay the foundations for tailoring the specificity of the Tar receptor.
Contact Us