Computational biology predicts metabolic engineering targets for increased production of 103 valuable chemicals in yeast
Journal article, 2025

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 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.

metabolic engineering

synthetic biology

genome scale modeling

yeast

Author

Iván Domenzain Del Castillo Cerecer

Chalmers, Life Sciences, Systems and Synthetic Biology

Yao Lu

Northwest A&F university

Haoyu Wang

Chinese Academy of Sciences

Shanghai Jiao Tong University

University of Chinese Academy of Sciences

Junling Shi

Northwestern Polytechnical University

Hongzhong Lu

Shanghai Jiao Tong University

Jens B Nielsen

Chalmers, Life Sciences, Systems and Synthetic Biology

Proceedings of the National Academy of Sciences of the United States of America

0027-8424 (ISSN) 1091-6490 (eISSN)

Vol. 122 9 e2417322122

Graphene-based CRISPR-Cas9 gene modification of plant cells

Novo Nordisk Foundation (NNF10CC1016517), 2021-08-01 -- 2024-06-30.

Model-Based Construction And Optimisation Of Versatile Chassis Yeast Strains For Production Of Valuable Lipid And Aromatic Compounds (CHASSY)

European Commission (EC) (EC/H2020/720824), 2016-12-01 -- 2020-11-30.

Bioinformatics Services for Data-Driven Design of Cell Factories and Communities (DD-DeCaF)

European Commission (EC) (EC/H2020/686070), 2016-03-01 -- 2020-02-28.

Subject Categories (SSIF 2025)

Molecular Biology

Bioinformatics (Computational Biology)

Bioinformatics and Computational Biology

DOI

10.1073/pnas.2417322122

PubMed

39999169

Related datasets

SysBioChalmers/CellFactory-ecYeastGEM [dataset]

URI: https://github.com/SysBioChalmers/CellFactory-ecYeastGEM

More information

Latest update

3/24/2025