From sequence to function and back – Data-driven engineering of synthetic microbes for sustainable bioproduction

Synthetic microbes engineered to produce value-added chemicals based on renewables are an attractive means to replace petrochemical production. However, their engineering remains intricate and prone to unsatisfying outcomes, such as insufficient productivity or the incapability to access many non-natural compounds.

In my talk, I will show how we combine high-throughput experimental technology with data-driven modeling (machine learning) to overcome some of the shortcomings of our “engineering toolbox”. I will showcase how such model-guided approaches can streamline the engineering of microbes such as Escherichia coli on all levels of the central dogma to tweak gene regulation, enzymatic activity and metabolic flux.