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机器学习用于代谢途径优化:一篇综述

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  • 1Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.

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概括
此摘要是机器生成的。

机器学习 (ML) 通过分析复杂的生物数据,加速微生物细胞工厂的发展. 本综述强调了ML在代谢工程中的应用,以改善生物过程.

关键词:
积极学习是指积极学习.贝叶斯的优化是贝叶斯的优化.数据驱动模型是基于数据的模型.机器学习 机器学习机制模型的机制模型.代谢途径优化 - 代谢途径优化

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科学领域:

  • 生物技术是生物技术.
  • 合成生物学 合成生物学
  • 代谢工程是代谢工程.

背景情况:

  • 优化微生物细胞工厂对于生物技术至关重要,但受到复杂的细胞机械的阻碍.
  • 目前工程微生物细胞工厂的方法往往是乏味的和耗时的.

研究的目的:

  • 审查最近机器学习 (ML) 的应用,以推动微生物细胞工厂的发展.
  • 突出ML在构建基因组规模代谢模型和优化生物过程中的作用.

主要方法:

  • 审查最近关于机器学习在代谢工程中的应用文献.
  • 分析机器学习与高吞吐量技术的集成以及设计-构建-测试-学习周期.
  • 专注于ML用于代谢模型构建,途径优化,酶工程和基因调节元件设计.

主要成果:

  • 机器学习有效地分析大型生物数据集,以构建复杂生物过程的数据驱动模型.
  • 通过在代谢模型构建和途径优化方面的应用,ML加速了微生物细胞工厂的发展.
  • 最近的ML进步有助于限制速率的酶工程和基因调节元件设计.

结论:

  • 机器学习为克服微生物细胞工厂优化的挑战提供了强大的解决方案.
  • 将机器学习与实验工作流程相结合,可显著加快高效生物技术生产流程的开发.
  • 对ML限制和解决方案的进一步研究将增强其在合成生物学中的实用性.