以模型为指导的化学环境和代谢网络设计,将途径与细胞适应性结合起来
Natalia Kakko von Koch1, Tuula Tenkanen2, Sandra Castillo2
1Aalto University, School of Chemical Engineering, Department of Bioproducts and Biosystems, Espoo, Finland.
Metabolic engineering communications
|December 11, 2025
概括
一种新的方法,EvolveXGA,使用代谢模型来指导适应性实验室进化 (ALE),以改善微生物中的异质化合物生产. 这种方法与细胞适应性结合了所需的特征,加速了生物技术的菌株优化.
科学领域:
- 合成生物学 合成生物学
- 代谢工程是代谢工程.
- 计算生物学 计算生物学
背景情况:
- 由于复杂的细胞对代谢途径的调节,微生物中的异质化合物生产具有挑战性.
- 传统的应变工程是繁的,经常无法直观地将生产特征与细胞健康联系起来.
- 适应性实验室进化 (ALE) 在改善诸如耐压力等特征方面是有效的,但在异质生产方面却不那么有效.
研究的目的:
- 开发一种新的方法,EvolveXGA,用于基因组规模的代谢模型引导的策略设计,以改善异质化合物生产.
- 通过使用遗传算法,确定将所需的生产特征 (代谢流) 与细胞健康相结合的策略.
- 为了证明EvolveXGA在*Saccharomyces cerevisiae**中优化糖酸生产的有效性.
主要方法:
- 开发了EvolveXGA,一种结合基因组规模代谢模型,遗传算法和ALE的方法.
- 寻找化学环境和代谢网络修改 (例如基因淘汰) 的组合,以实现生产和健康之间的流量合.
- 应用了EvolveXGA来预测29种异质化合物的策略,并实验验证了一种生产甘油酸的策略.
主要成果:
- EvolveXGA确定了使用四种代谢淘汰和三种化学环境成分的29种异质化合物中13种适应性生产途径的策略.
- 对于剩余的化合物,发现了针对本地流量的适应性合策略.
- 与对照菌株相比, *S. cerevisiae* 中糖酸生产的实验演变产生了与葡萄糖产量改善的分离物.
结论:
- EvolveXGA提供了一个通用的框架,用于设计策略,将异质生产路线与细胞适应性结合起来.
- 这种以模型为导向的ALE方法使得优化工程微生物中的异质生产变得更加可行.
- 通过克服缓慢而昂贵的菌株优化,EvolveXGA加速了新型生物工艺的工业化实现.
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