ArticleProceedings of the National Academy of Sciences of the United States of America2025
Computational biology predicts metabolic engineering targets for increased production of 103 valuable chemicals in yeast.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Toward sustainable food preservatives: high-level production of sorbic acid in engineered Saccharomyces cerevisiae.Nature communications · 2026Article
- Systems biology of yeast metabolism.FEMS yeast research · 2026Review
- High-throughput yeast engineering in biofoundries: towards autonomous and scalable synthetic biology.FEMS yeast research · 2026Review
- Comparing metabolic engineering scenarios using simulated design-build-test-learn-cycles.Frontiers in bioengineering and biotechnology · 2026Article
- The Power of Yeast.Yeast (Chichester, England) · 2025Review
- Evolution and applications of genome-scale metabolic models in yeast systems biology studies.FEMS yeast research · 2025Review
- Modeling for understanding and engineering metabolism.QRB discovery · 2025Article
- Harnessing the optimization of enzyme catalytic rates in engineering of metabolic phenotypes.PLoS computational biology · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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 modeling 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 103 different chemicals using
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Registered trials
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