Project description
Utilising bio-intelligent DBTL cycles for improved innovation testing
A sustainable circular economy can protect the environment and mitigate the impacts of climate change by reducing waste and greenhouse gas emissions. A circular bioeconomy approach converts sustainable substrates in bioprocesses to provide a range of innovations. However, testing under industrial conditions for synthetic biology solutions is still proving difficult, leading to many innovations failing to make it to market. The EU-funded BIOS project will address this challenge by introducing a bio-intelligent design-build-test-learn cycle for maturing new synthetic biology innovations. It will utilise a mix of biological and mechanical technologies and methodologies to make reaching the final stage more efficient and easier.
Objective
The usage of fossil resources leading to increasing atmospheric CO2 levels and global climate change should be rapidly replaced by implementing a circular economy. Circular bioeconomy converting sustainable substrates in moderately operating bioprocesses offers a plenitude of solutions. While synthetic biology provides a multitude of tools for strain engineering, their rapid use in hosts for optimal performance under industrial conditions is still challenging. Promising innovations are often trapped in the ‘valley-of-death’ as strain engineering faces a too complex space of putative manipulations. Novel approaches are needed to increase speed and success rate of strain and bioprocess engineering.
The bio-intelligent approach, rigorously applied in BIOS, aims to accelerate and improve the conventional ‘design-build-test-learn’ (DBTL) cycle for strain and bioprocess engineering. Interdisciplinary collaboration will bridge microbiology, molecular biology, biochemical engineering with informatics, automation engineering, and mechanical engineering. Novel innovative metrics, biosensors, and bioactuators are developed for bi-directionally communication at biological-technical interfaces. Digital twins are created mimicking cellular and process levels. Integrating AI not only improves prediction quality but also enables hybrid learning, the key reason to increase speed and success rate in the novel bio-intelligent DBTL cycle (biDBTL). The power of biDBTL will be showcased by creating P. putida producer strains for terpenes, polyolefines, and methylacrylate. All are highly attractive products with a high potential for reducing anthropogenic greenhouse footprint. BIOS will open the door to a de-centralized, networked collaboration for strain and process engineering that efficiently links individual expertise for the sake of a symbiotic and rapid progress. BIOS also paves the way to de-centralized bio-manufacturing by implementing autonomous, self-controlled bioprocesses.
Fields of science
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorsbiosensors
- engineering and technologychemical engineeringbiochemical engineering
- natural sciencesbiological sciencessynthetic biology
- engineering and technologymechanical engineering
- social scienceseconomics and businesseconomicssustainable economy
Keywords
Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
70174 Stuttgart
Germany