In 2017, the iGEM Team Munich  – a group of 17 students – won first runner-up by creating a cheap diagnostic system for bacterial infections based on easy-to-use paper strips. Using a derived version of the CRISPR/Cas-System, ribonukleic acids of bacterial and viral origin can be detected in a quick, easy and highly automated manner. This way, a consumer can easily determine if the use of antibiotics is necessary or not.

  • First Runner-up
  • Best diagnostics project
  • Best model
  • Best Hardware
  • Best applied design
  • Best Software
  • Nominated for Best Wiki
  • Nominated for Best Presentation
  • Nominated for Best Measurement
  • Nominated for Best integrated Human Practices

Team of CascAID


The ongoing crisis of increasing antibiotic resistance demands innovative preventive strategies. Recently, the RNA-targeting protein CRISPR-Cas13a has been used for highly sensitive DNA and RNA detection, promising diverse applications in point-of-care diagnostics. We integrated Cas13a in the detection unit of CascAID, our GMO-free diagnostic platform. CascAID combines an automated microfluidic device for rapid lysis and extraction of nucleic acids with a paper-based readout system. We demonstrated the performance of our device by targeting the 16S rRNA from E. coli. We improved the detection limit of our platform, using simulations to optimize our amplification scheme and the final readout.
Conceived as a distributable platform for rapid point-of-care diagnostics, CascAID can be used to distinguish between bacterial and viral infections, thus minimizing the widespread use of antibiotics. Furthermore, Cas13a allows the fast design of target sequences, making our system adaptive to the emergence of new viral outbreaks or fast mutating pathogens.

After moving our labs location from Freising (Prof. Dr. Arne Skerra) to Garching (Prof. Dr. Friedrich Simmel) the Team “CascAID+” continued to show the excellence of our universities by winning first Runner-up in the Overgraduate Segment.

visit their page for more information: Team TUM/LMU Munich 2017

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