生产(经济)
肌酸
选择(遗传算法)
生化工程
生物技术
生物
化学
计算生物学
生物化学
计算机科学
工程类
人工智能
经济
微观经济学
作者
Jinbei Li,Simon R Krarup,Pascal A. Pieters,Tobias B. Alter,Paul Jacottin,Josefin Johnsen,Elsayed T. Mohamed,Thomas Harris,Linda Ahonen,Khem B. Adhikari,Bernhard Ø. Palsson,Adam M. Feist,Lei Yang
标识
DOI:10.1016/j.ymben.2025.07.009
摘要
Creatine is an important energy storage molecule produced exclusively in vertebrates and is crucial for muscle development. It is particularly valuable as a food supplement, especially for plant-based diets. Here, we present an alternative to chemical synthesis by developing a biosynthetic process using an Escherichia coli cell factory expressing a heterologous pathway. We employed a model-driven growth-coupled selection approach combined with adaptive laboratory evolution to overcome metabolic bottlenecks in the heterologous synthesis of creatine. We developed a novel growth-coupling strategy to optimize an important glycine amidinotransferase step guided by genome-scale modeling. We also improved creatine tolerance of E. coli by adaptive evolution. Several design-build-test-learn cycles of evolution and selection resulted in a 58 % increase in titer over the baseline strain from glycine and arginine. This study highlights the advantage of combining production with growth for efficient cell factory generation driven by evolutionary engineering and computational biology.
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