Combining evolution and machine learning-guided pathway optimization to engineer a novel methylsuccinate module for synthetic C1 metabolism in vivo

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

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Industrial applicability of enzymatic and whole-cell processes for the utilization of C1 building blocks - Nature Communications

Significant process has been made in using one carbon sources toward biomanufacturing. Here, the authors explore how to strength its competitiveness, and highlight latest technologies and the released...

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Escherichia coli selection strains for growth-coupled metabolic engineering

Synthetic metabolism has the potential to transform carbon capture, bioremediation, or bioproduction strategies. To transfer metabolic designs from an…

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Computation-aided designs enable developing auxotrophic metabolic sensors for wide-range glyoxylate and glycolate detection - Nature Communications

Auxotrophic metabolic sensors (AMS) are vital for bioengineering but are often time-consuming to develop. Here, the authors present a workflow for designing versatile AMS, demonstrating their use for ...

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