微生物细胞工厂的智能设计进展

doi:10.11949/0438-1157.20211163

摘要/Abstract

摘要：

在细胞工厂构建中设计-构建-测试-学习（design-build-test-learn，DBTL）循环是开发微生物细胞工厂的基本研究思路，其中设计环节尤为重要，然而传统的微生物细胞工厂设计方法主要依靠经验、费时费力、准确率低，影响了微生物细胞工厂的开发效率。当前，规模越发庞大的生物数据库和人工智能技术推动了微生物细胞工厂智能设计的快速发展，提升了在生物合成途径设计、调控元件设计和全局优化设计等方面的设计效率与应用。本文综述了微生物细胞工厂中途径预测、元件设计和途径与元件的组合三个环节中的智能设计工具，微生物细胞工厂智能设计的飞速发展将对生物制造领域产生变革性的影响。

关键词: 生物制造, 微生物细胞工厂, 智能设计, 生物逆合成, 调控元件设计

Abstract:

Design-build-test-learn (DBTL) cycle in the construction of cell factories is the basic research idea for the development of microbial cell factories. However, the traditional design methods of microbial cell factories are time-consuming, laborious and low accuracy, which affects the development efficiency of microbial cell factories. At present, the growing scale of biological database and artificial intelligence have promoted the rapid development of computer-aided intelligent design of microbial cell factorials, and improved the design efficiency and application in synthetic pathway design, regulatory element design and global optimization design. This article reviews the intelligent design tools in the three links of pathway prediction, component design, and combination of pathways and components in microbial cell factories. The rapid development of intelligent design of microbial cell factory will have a transformative impact on the field of biological manufacturing.

Key words: biomanufacturing, microbial cell factory, intelligent design, bioretrosynthesis, regulatory element design

中图分类号:

Q 78

张震, 曾雪城, 秦磊, 李春. 微生物细胞工厂的智能设计进展[J]. 化工学报, 2021, 72(12): 6093-6108.

Zhen ZHANG, Xuecheng ZENG, Lei QIN, Chun LI. Intelligent design of microbial cell factory[J]. CIESC Journal, 2021, 72(12): 6093-6108.

图/表 7

参考文献 87

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工具	开发年份	基础数据库	反应规则工具	途径搜索算法	评价指标	文献
XTMS	2014	MetaCyc, KEGG	SMARTS	枚举	热力学可行性、酶序列可行性、途径长度、化合物毒性	[9]
enviPath	2016	EAWAG-BBD	SMILES	EAWAG-PPS	基于机器学习的相对推理方法估计每个转换的概率	[14]
ReactPred	2016	MetaCyc	用户提供的SMARTS	枚举	热力学可行性	[15]
ReactionMiner	2017	KEGG	反应规则（基于图）	子图挖掘	途径长度、理论产量	[16]
EcoSynther	2017	Rhea, KEGG	Graph	基于概率方法	途径长度、理论产量	[17]
novoStoic	2018	MetRxn	分子指纹	混合整数线性规划	热力学可行性、化合物毒性、理论产量、市场利润	[18]
RetroPath2.0	2018	MetaNetX	数据驱动的SMARTS（RetroRules）	枚举	酶序列可行性、理论产量	[11]
Transform-MinER	2019	KEGG	数据驱动的SMARTS	最短路径	底物相似性	[19]
PrecursorFinder	2019	Literature	分子指纹	最大公共子结构	底物相似性	[20]
RetSynth	2019	Multiple	Stoichiometry matrix	混合整数线性规划	途径长度、理论产量	[21]
novoPathFinder	2020	Rhea, KEGG	SMIRKS	启发式搜索	热力学可行性、酶序列可行性、途径长度、化合物毒性、理论产量	[13]
RetroPath RL	2020	MetaNetX	RetroRules	枚举	底物相似性、酶序列可行性、化合物毒性	[12]
BioNavi-NP	2021	MetaCyc, KEGG	AND-OR Tree	Transformer神经网络	底物相似性、途径长度	[7]
ATLASx	2021	bioDB	BNICE.ch	最短路径	热力学可行性、酶序列可行性、途径长度、化合物毒性	[22]

工具	开发年份	基础数据库	反应规则工具	途径搜索算法	评价指标	文献
XTMS	2014	MetaCyc, KEGG	SMARTS	枚举	热力学可行性、酶序列可行性、途径长度、化合物毒性	[9]
enviPath	2016	EAWAG-BBD	SMILES	EAWAG-PPS	基于机器学习的相对推理方法估计每个转换的概率	[14]
ReactPred	2016	MetaCyc	用户提供的SMARTS	枚举	热力学可行性	[15]
ReactionMiner	2017	KEGG	反应规则（基于图）	子图挖掘	途径长度、理论产量	[16]
EcoSynther	2017	Rhea, KEGG	Graph	基于概率方法	途径长度、理论产量	[17]
novoStoic	2018	MetRxn	分子指纹	混合整数线性规划	热力学可行性、化合物毒性、理论产量、市场利润	[18]
RetroPath2.0	2018	MetaNetX	数据驱动的SMARTS（RetroRules）	枚举	酶序列可行性、理论产量	[11]
Transform-MinER	2019	KEGG	数据驱动的SMARTS	最短路径	底物相似性	[19]
PrecursorFinder	2019	Literature	分子指纹	最大公共子结构	底物相似性	[20]
RetSynth	2019	Multiple	Stoichiometry matrix	混合整数线性规划	途径长度、理论产量	[21]
novoPathFinder	2020	Rhea, KEGG	SMIRKS	启发式搜索	热力学可行性、酶序列可行性、途径长度、化合物毒性、理论产量	[13]
RetroPath RL	2020	MetaNetX	RetroRules	枚举	底物相似性、酶序列可行性、化合物毒性	[12]
BioNavi-NP	2021	MetaCyc, KEGG	AND-OR Tree	Transformer神经网络	底物相似性、途径长度	[7]
ATLASx	2021	bioDB	BNICE.ch	最短路径	热力学可行性、酶序列可行性、途径长度、化合物毒性	[22]

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