Congrats !

Good luck and congratulations Gerrich, I am sure you will do amazing!

4) Many thanks to @beaubd.bsky.social and Tobias Erb for all the interesting discussions. In addition, want to thank Hai He, María Suárez Diez, and Elad Noor for constructive feedback on this work!

3) My hope is that this is a starting point to compile selection strains into a readily accessible collection. Let me know if you’d be interested !

2) To make the metabolism schemes broadly accessible, you can download the .ai files from github.com/helenaschulz... ! Can only recommend laminating those to sketch on when discussing anything metabolism :) .

1) We dive into how strain growth parameters and pathway attributes are correlated, update some of the most central #schemes to our work (which I first got to work with in the Bar-Even lab!) and reflect on future developments in the field.

Thank you so much Gerrich ! I am so honoured to show my work to such a supportive and interested audience and genuinely enjoyed discussing with you afterwards. Looking forward to see you shape tools for difficult-to-access hosts !

9) It was a true pleasure to work with Enrico Orsi, Hai He, Elad Noor, Ari Satanowski, Pablo Nikel, Tobias Erb and all co-authors involved. The origins of the strains go way back to the Bar-Even lab and Charlie Cotton creating the first strains - I am more than grateful to see it out !

8) The sensor strains are supposed to become a #community resource – so please feel free to reach out to us if you could apply them in your projects! This work would not have been possible without Enrico Orsi doing a tremendous job at leading it.

7) Of course, you can also use them to detect glyoxylate or glycolate in #environmental applications. For example, we could quantify how much glycolate was produced by #cyanobacteria during #photorespiratory growth based on the OD our sensors reached.

6) What can you use those sensors for ? Amongst other things, to select for synthetic metabolic modules. As an example in the context of #C1assimilation, we engineered a module of the HOPAC cycle and show that we can determine the best #expression strength for the catalysts.

5) Next, we characterised the strains by verifying expected metabolic fluxes by isotope tracing and determining their selective demands with #glyoxylate and #glycolate.

4) So, we went ahead and constructed the respective #sensorstrains. By adding some additional #knockouts we prevented the emergence of known metabolic bypasses for the selections.

3) To identify such cross-connections and the relevant gene deletions, we asked Hai He and Elad Noor if they could predict knockouts conferring a growth dependence on glyoxylate. Using the medium-scale iCH360 model they found multiple designs of varying #demands!

2) However, my beloved model microbe #Ecoli doesn’t need glyoxylate for growth - it is nonessential. How do you design a strain that is #auxotrophic for such a metabolite ? Easy, you “just” rewire the metabolism, so that biomass precursor must be made from #glyoxylate!

1) #Glyoxylate is a key intermediate of synthetic #CO2fixation metabolism - for example, it is the product of the CETCH, the HOPAC or the MOG cycles. To implement those designs in vivo, we want to couple cell growth to those synthetic pathways.