BIOS: The bio-intelligent DBTL cycle, a key enabler catalysing the industrial transformation towards sustainable biomanufacturing

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.