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Computational biology predicts metabolic engineering targets for increased production of 102 valuable chemicals in yeast

Domenzain, I.; Lu, Y.; Shi, J.; Lu, H.; Nielsen, J.

2023-02-03 systems biology
10.1101/2023.01.31.526512 bioRxiv
Show abstract

Development of efficient cell factories that can compete with traditional chemical production processes is complex and generally driven by case-specific strategies, based on the product and microbial host of interest. Despite major advancements in the field of metabolic modelling in recent years, prediction of genetic modifications for increased production remains challenging. Here we present a computational pipeline that leverages the concept of protein limitations in metabolism for prediction of optimal combinations of gene engineering targets for enhanced chemical bioproduction. We used our pipeline for prediction of engineering targets for 102 different chemicals using Saccharomyces cerevisiae as a host. Furthermore, we identified sets of gene targets predicted for groups of multiple chemicals, suggesting the possibility of rational model-driven design of platform strains for diversified chemical production. One sentence summaryNovel strain design algorithm ecFactory on top of enzyme-constrained models provides unprecedented chances for rational strain design and development.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.