PYF:一种多功能算法,用于预测Escherichia coli微生物联盟中的生产和优化代谢工程战略
Chen Yang1, Yingqi Zhao1, Boyuan Xue1
1State Key Laboratory of Green Biomanufacturing, National Energy R&D Center for Biorefinery, Beijing Key Laboratory of Green Chemicals Biomanufacturing, Beijing Synthetic Bio-manufacturing Technology Innovation Center, Beijing University of Chemical Technology, No.15, Beisanhuan East Road, Beijing 100029, PR China.
Briefings in bioinformatics
|June 21, 2025
概括
一个名为多微生物细胞工厂产量预测 (PYF) 的新算法通过模拟生物合成和生长来准确预测微生物联盟的生产. 这种方法改善了代谢工程策略,以获得更高的产量.
科学领域:
- 合成生物学 合成生物学
- 代谢工程是代谢工程.
- 计算生物学是一种计算生物学.
背景情况:
- 精确模拟微生物联盟对于优化代谢工程和提高产量至关重要.
- 现有的模型往往无法捕捉生物合成和自我生长之间的复杂相互作用,限制了预测能力.
研究的目的:
- 引入多微生物细胞工厂产量预测 (PYF) 算法,以改进微生物联盟的模拟.
- 通过考虑生物合成路径表达来提高微生物细胞工厂生产的预测准确性.
主要方法:
- 开发了PYF算法,将生物合成途径表达度整合到微生物相互作用模型中.
- 在各种条件 (单代谢物,双碳,双代谢物交换) 中使用*Escherichia coli*-*E. coli*联盟验证了PYF.
- 进行了对代谢工程战略优化的敏感性分析.
主要成果:
- PYF在预测微生物生产方面表现出高准确性,平均相对误差 (MRE) 为0.106.
- 获得了0.883的平均确定系数和0.930.93的假设测试参数.
- 与现有算法相比,PYF将MRE降低了约61.6%,并且不需要酶催化数据.
结论:
- PYF提供了一种简化和准确的方法来模拟多微生物系统,增强代谢工程.
- 该算法有效地预测生产和优化战略,而无需复杂的网络集成.
- PYF代表了一种推进微生物联盟生产的新方法.
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