Cool Preprint led by @mirbachh on comparing machine learning based engineering of a synthetic C1 fixation pathway vs the classic ALE based approach. ML allowed way faster pathway establishment and when combined with the evolved background improved growth by a factor of 3.🔥🎉
Beau Dronsella
How do we engineer metabolism more efficiently ❓
The core work of my PhD focussed on this question, and I am thrilled to now share the respective Preprint:
www.biorxiv.org/content/10.6...
De novo metabolic pathways open possibilities for sustainable biotransformations in microbes. However, the in vivo-implementation of such new-to-nature pathways is highly challenging and heavily relies on adaptive laboratory evolution (ALE) of the host's native metabolic network. Here, we assess how much this need for host-centric ALE can be overcome and/or complemented through the informed design of the newly introduced pathway. Exemplifying for a synthetic CO2-fixation module via methylsuccinate, we established methylsuccinate-dependent growth of Escherichia coli over six months by ALE of E. coli's native metabolism. In parallel, we developed a machine-learning guided workflow (MEVIS) for the automated engineering of the synthetic pathway, resulting in methylsuccinate-dependent growth within three weeks. Critically, performing MEVIS in the background of the ALE-evolved strain is necessary to further approach wild-type like growth, demonstrating how ALE in combination with machine-learning-guided lab automation holds great potential to accelerate and improve design-build-test-learn cycles in contemporary metabolic engineering. ### Competing Interest Statement The authors have declared no competing interest. Max Planck Society, https://ror.org/01hhn8329 Bosch Research Foundation
