Domenzain Iván, Lu Yao, Wang Haoyu, Shi Junling, Lu Hongzhong, Nielsen Jens
Department of Life Sciences, Chalmers University of Technology, Gothenburg SE41296, Sweden.
Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg SE41296, Sweden.
Proc Natl Acad Sci U S A. 2025 Mar 4;122(9):e2417322122. doi: 10.1073/pnas.2417322122. Epub 2025 Feb 25.
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 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.
开发能够与传统化学生产工艺相竞争的高效细胞工厂是复杂的，并且通常由基于目标产品和微生物宿主的特定案例策略驱动。尽管近年来代谢建模领域取得了重大进展，但预测用于提高产量的基因改造仍然具有挑战性。在此，我们提出了一种计算流程，该流程利用代谢中蛋白质限制的概念来预测用于增强化学生物生产的基因工程靶点的最佳组合。我们使用我们的流程以[具体宿主]为宿主预测了103种不同化学品的工程靶点。此外，我们确定了针对多种化学品组预测的基因靶点集，这表明有可能对用于多样化化学生产的平台菌株进行合理的模型驱动设计。
