PAcMEN

Key Info

Basic Information

Partner:
Prof. Dr. Ulrich Schwaneberg
Faculty:
Mathematics, Computer Science and Natural Sciences
Pillar:
Excellent Science
Project duration:
01.10.2016 to 30.09.2020
EU contribution:
3.994.075,80 euros
  EU flag This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 722287.  

Title

Predictive and Accelerated Metabolic Engineering Network

Concept

The world economy is dependent on fossil resources: oil, gas and coal. The fossil resources are finite and their consumption causes catastrophic environmental changes. Therefore we need to move towards sustainable economy using renewable resources for energy and chemicals production. Via metabolic engineering approach, novel microbial cells can be created that can convert biomass and waste into fuels and chemicals. Metabolic engineering however distinguishes itself from other engineering disciplines by low predictability of the design and long turnover times for the cell factory construction and screening. Therefore there is a need for scientists, who can address these challenges. European Training Network on Predictable and Accelerated Metabolic Engineering Networks (PAcMEN) will be established at 5 renowned European universities and 2 SMEs with participation of 5 industrial and 1 academic partner organizations. In this program 16 PhD students (of which 15 funded by EU contribution) will learn to conduct state-of-the-art research on metabolic engineering of microbial cell factories and learn to commercialize innovations.

Participants

  • Danmarks Tekniske Universitet, Denmark (Coordinator)
  • Technische Universiteit Delft, Netherlands
  • SilicoLife Lda, Portugal
  • Chalmers Tekniska Hoegskola AB, Sweden
  • SeSaM-Biotech GmbH, Germany
  • École polytechnique fédérale de Lausanne, Switzerland

Success Story

Under the Horizon 2020 project PAcMEN three innovations were developed and listed in the Innovation Radar: Biosensor for detection of pathway intermediates and chemicals, and enzyme evolution (esr8), Directed evolution at high mutational loads (esr10) and Machine learning for metabolic pathway modelling (esr7). The Innovation Radar is a European Commission initiative to identify high potential innovations and innovators in EU-funded research and innovation projects. It has the goal of creating a steady flow of promising tech companies that can scale up into future industrial champions.