在代谢表型的工程中利用酶催化速率的优化
Zahra Razaghi-Moghadam1,2, Fayaz Soleymani Babadi1,2, Zoran Nikoloski1,2
1Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
PLoS computational biology
|November 4, 2024
概括
一种新的计算方法,即克服动力速率障碍 (OKO),通过修改酶周转数来预测代谢工程策略. 这种方法成功地使大肠杆菌和大肠杆菌中的40多种化合物的产量翻了一番,对细胞生长的影响最小.
科学领域:
- 代谢工程是代谢工程.
- 系统生物学 系统生物学
- 计算生物学 计算生物学
背景情况:
- 从实验和深度学习模型中,酶周转率的数字越来越多.
- 目前的计算方法缺乏基于周转数修改的代谢工程预测策略.
- 改变营业额的数字,而不改变转录规则,对于增强化学品生产的充分性是不清楚的.
研究的目的:
- 开发一种计算方法,用酶周转数来预测代谢工程策略.
- 评估仅仅修改营业额的数字是否可以增加目标化学品的生产.
- 为了整合酶受约束的代谢模型与精确的工程设计的营业额数据.
主要方法:
- 开发了一种基于约束的建模方法,即克服动力速率障碍 (OKO).
- 利用受酶约束的代谢模型进行in silico预测.
- 应用OKO对大肠杆菌和大脑菌的代谢模型.
- 精炼的OKO包括对酶丰度的操纵.
主要成果:
- OKO预测的策略至少使40多种化合物的产量增加了一倍,增长处罚最小.
- 目标化学品的过度生产不仅仅是通过增加营业额来实现的.
- 精细的OKO方法有效地整合了代谢工程设计的实验和预测的营业额数字.
结论:
- 酶受约束的代谢模型与OKO相结合,可以精确地预测代谢工程策略的in silico预测.
- 修改酶周转数是提高化学生产的可行策略.
- OKO框架扩大了基因组规模代谢模型用于蛋白质工程和生物技术设计的应用.
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