优化代谢物生产与基于邻近的二进制量子行为粒子群集优化和流量平衡分析
Lidan Bai1, Jun Sun1, Vasile Palade2
1Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi, China.
概括
我们开发了NBQPSO-FBA,这是一个使用量子行为粒子群集优化的增强代谢工程工具. 这种方法有效地识别了最佳的基因淘汰,以最大限度地提高工业化合物的微生物生产.
科学领域:
- 代谢工程是代谢工程.
- 计算生物学 计算生物学
- 合成生物学 合成生物学
背景情况:
- 代谢工程优化微生物生产使用基于约束的模型和元启发算法.
- 粒子集群优化 (PSO) 可以通过局部最佳条件来限制.
- 量子行为PSO (QPSO) 和其二进制变体 (BQPSO) 提供了改进.
研究的目的:
- 开发一种先进的代谢工程方法,以优化微生物细胞工厂.
- 为了增强二进制的量子行为粒子群集优化 (BQPSO) 与邻近拓,创建NBQPSO.
- 将NBQPSO与流量平衡分析 (FBA) 集成,以改进淘汰策略的识别.
主要方法:
- 开发了基于邻近的BQPSO (NBQPSO),通过将邻近拓纳入BQPSO.
- 将NBQPSO与流量平衡分析 (FBA) 结合起来,创建NBQPSO-FBA方法.
- 为大型基因组尺度代谢模型 (GSMMs) 引入了一种新的编码策略.
主要成果:
- NBQPSO-FBA表现出与已建立的双层线性编程和启发式方法相匹配或优越的性能.
- 在大肠杆菌中达到理论上最大的乙酸盐产量的90.69%.
- 展示了有效的优化,需要更少的基因淘汰.
结论:
- NBQPSO-FBA是优化微生物细胞工厂代谢物生产的有效和实用工具.
- 该方法解决了对微生物产品的日益增长的工业需求.
- 这种方法在代谢工程策略方面取得了重大进展.
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