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Data-driven design of cell factories and communities
- 1. Data-Driven Design of Cell Factories and Communities Nikolaus Sonnenschein, Senior Researcher iLoop Core – Design Unit @phantomas1234
- 3. What we do … • Construct cell factories! • Determine the spectrum of molecules that can be produced biologically (basic research). • Shortening the strain design and development process (translational research). • Develop new technologies and transfer knowledge to industry for the benefit of society. • Train the future workforce of a bio- based economy.
- 4. Commercial: Product titre > 10–100 g/L 25–50 FTE for 5–10 years Cost $50M–$250M Death valley Valley of Death Academic: Product titre < 1 g/L 1–2 FTE for 2–3 years Cost < $1M
- 5. Commercial: Product titre > 10-100 g/L < 2 years Cost <$20M Bridging the valley of death Omics Lab automation Valley of Death Systems biology guided design Academic: Product titre < 1 g/L 1–2 FTE for 2–3 years Cost < $1M
- 6. Computer Aided Metabolic Engineering and Optimization of Cell Factories http://cameo.bio http://www.biorxiv.org/content/early/2017/06/09/147199 => https://tinyurl.com/cameo-biorxiv
- 7. Workflow
- 9. Computer aided design (CAD) in engineering
- 10. CAD in civil engineering
- 11. CAD in mechanical engineering
- 12. CAD in electrical engineering
- 13. CAD in chemical engineering
- 14. Intuition aided design (IAD) in metabolic engineering
- 15. CAD in metabolic engineering?
- 16. Data-Driven Design of Cell Factories and Communities http://dd-decaf.eu/ The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 686070
- 17. Data-Driven Design of Cell Factories and Communities Pathways Cells CommunitiesEnzymes Health AgricultureIndustrial biotech Environment Fluxome(Meta)genome Transcriptome Proteome Metabolome OmicsDesignApplications
- 18. Coordinator End users Bioinformatics SMEs Isabel Rocha Markus Herrgård & Niko Sonnenschein Enzymes Communities Vassily Hatzimanikatis Consortium Metabolomics Peer Bork & Kiran Patil Science Jens Nielsen Transcriptomics/ Proteomics
- 19. Platform demo Svetlana Henning MoritzDanny + Nace Matyas
- 21. Data-driven design: proteomics integration via ecYeast7 and Gecko
- 23. Next steps • Registration via Google, Twitter, Github, Facebook, ORCID, … • BYOM: Bring your own models • Mine (meta)genomic data for enzymes • Support more types of omics data • Add community modeling Models Enzymes Omics data AGTGG AGTT AA TC TC T TATT GG AG ATT AA GA GTT ATC TC TT GAA TT G TATGG AC TT GC TT ATT GATC TC GTT AA GAGTT AA TC TT GG AG ATGC TT ATT GATT GTAGAGTT A TT
- 26. memote – a community effort
- 27. Automate all the things High-level strain design “Delete native genes A, B, C, and D. Add foreign genes X, Y,Z. Increase flux through reaction F by twofold while reducing flux through reaction G by threefold. Remove product inhibition in pathway Q …” Magic Compiler Box gRNAs Primers Protocols Genes Promotors Libraries … (b) Workflow Workcell Freezers and incubation PCR Robotics arm Liquid handling Plate reader Plate/test-tube magazine AUTOPROTOCOL Data Protocol API Materials Shipping
- 28. Order button WE ACCEPT CREDIT CARDS
- 29. Acknowledgements The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 686070 Get in touch with us via: http://dd-decaf.eu/ @dddecaf (Twitter) Zachary King (UCSD)
- 30. Presented in the Synthetic Biology & Gene Editing strand of the 4Bio Summit. To find out more, visit: www.global-engage.com