1. 中国科学院分子植物科学卓越创新中心 中国科学院合成生物学重点实验室，上海 200032;
2. 中国科学院大学，北京 100039
收稿日期：2020-11-15；接收日期：2021-02-14；网络出版时间：2021-02-23
基金项目：国家重点研发项目(No. 2018YFA0900600)，国家自然科学基金(Nos. 31670099, 41876084)，中国科学院战略性先导科技专项(B类) (No. XDB27020202)，中国科学院重点部署项目(Nos. XDPB0400, KFZD-SW-215)，上海市优秀学术带头人计划项目(No. 20XD1404400)，上海市科委自然科学基金(No. 17ZR1435000)，中国科学院国际合作项目(No. 153D31KYSB20170121) 资助
作者简介：王勇 ??博士，中国科学院分子植物科学卓越创新中心研究员、博士生导师，中国科学院“****”，中国科学院合成生物学重点实验室副主任，上海市生物工程学会秘书长、副理事长，中国生物工程学会理事。主要研究方向为天然产物合成生物学，研究内容涉及天然产物生物合成途径的解析、合成生物学设计与异源合成等.
摘要：20世纪90年代，Bailey及Stephanopoulos等提出了经典代谢工程的理念，旨在利用DNA重组技术对代谢网络进行改造，以达到细胞性能改善，目标产物增加的目的。自代谢工程诞生以来的30年，生命科学蓬勃发展，基因组学、系统生物学、合成生物学等新学科不断涌现，为代谢工程的发展注入了新的内涵与活力。经典代谢工程研究已进入到前所未有的系统代谢工程阶段。组学技术、基因组代谢模型、元件组装、回路设计、动态控制、基因组编辑等合成生物学工具与策略的应用，大大提升了复杂代谢的设计与合成能力；机器学习的介入以及进化工程与代谢工程的结合，为系统代谢工程的未来开辟了新的方向。文中对过去30年代谢工程的发展趋势作了梳理，介绍了代谢工程在发展中不断创新的理论与方法及其应用。
关键词：代谢工程动态控制进化工程机器学习
An evolving and flourishing metabolic engineering
Zhifeng Liu1,2, Yong Wang1
1. CAS Center for Excellence in Molecular Plant Sciences, Key Laboratory of Synthetic Biology, Chinese Academy of Sciences, Shanghai 200032, China;
2. University of Chinese Academy of Sciences, Beijing 100039, China
Received: November 15, 2020; Accepted: February 14, 2021; Published: February 23, 2021
Supported by: National Key Research and Development Program of China (No. 2018YFA0900600), National Natural Science Foundation of China (Nos. 31670099, 41876084), Strategic Priority Research Program of Chinese Academy of Sciences (No. XDB27020202), Key Research Program of the Chinese Academy of Sciences (Nos. XDPB0400, KFZD-SW-215), Program of Shanghai Academic Research Leader (No. 20XD1404400), Natural Science Foundation of Shanghai Municipal Science and Technology Committee (No. 17ZR1435000), International Partnership Program of Chinese Academy of Sciences (No. 153D31KYSB20170121)
Corresponding author: Yong Wang. Tel: +86-21-18601627698; E-mail: yongwang@cemps.ac.cn.
Abstract: In 1990s, Bailey and Stephanopoulos put forward the concept of classic metabolic engineering, aiming to use DNA recombination technology to rewire metabolic network to achieve improved cell performance and increased target products. In the last 30 years since the birth of metabolic engineering, life science have flourished, and new disciplines such as genomics, systems biology and synthetic biology have emerged, injecting new connotations and vitality into the development of metabolic engineering. Classic metabolic engineering research has entered into an unprecedented stage of systems metabolic engineering. The application of synthetic biology tools and strategies, such as omics technology, genomic-scale metabolic model, parts assembly, circuits design, dynamic control, genome editing and many others, have greatly improved the design, build, and rewiring capabilities of complex metabolism. The intervention of machine learning and the combination of evolutionary engineering and metabolic engineering will further promote the development of systems metabolic engineering. This paper analyzes the development of metabolic engineering in the past 30 years and summarizes the novel theories, techniques, strategies, and applications of metabolic engineering that have emerged over the past 30 years.
Keywords: metabolic engineeringdynamic controlevolution engineeringmachine learning
1991年6月，Science期刊发表了“生物技术前沿”专辑，其中James E. Bailey的“Toward a science of metabolic engineering”以及Stephanopoulos & Vallino的“Network rigidity and metabolic engineering in metabolite overproduction”系统总结了20世纪80年代以来科学工作者对生物反应系统的设计与操作，奠定了代谢工程的科学基础，是代谢工程正式诞生的标志。经典代谢工程，旨在利用基因工程技术调控特定的基因与反应、改善细胞功能、提高目标化合物的产量[1-2]。随着技术的发展以及工程理念的创新，代谢工程在过去的30年里经历了4个不同的发展阶段(图 1)。2000年，人类基因组计划的完成使得生命科学研究全面进入“组学”时代，组学技术与代谢工程相结合，数学模型、计算机算法在生物系统中的应用，极大地推动了代谢工程对生物系统的模拟、分析、设计与改造能力[3]。2004年10月，Nature Biotechnology期刊出版了系统生物学专辑，Stephanopoulos等[4]首次系统阐述了利用系统生物学方法研究生物系统，并利用生物的复杂性进行菌株改造，为修饰细胞机器、实现特定的代谢工程目标提供无限的可能性。代谢工程诞生10年后，合成生物学开始起步。合成基因回路的出现[5-6]与DNA组装技术、染色体工程、基因表达调控等合成生物学技术极大地丰富了经典代谢工程途径构建、合成测试、流量优化三步循环的内涵，使得复杂细胞工厂的建立成为可能。2007年，Stephanopoulos实验室在Trends in Biotechnology期刊撰文提出将合成生物学作为4种主要工具之一，在代谢工程的系统设计及表型控制方面发挥作用[7]。2012年5月，Metabolic Engineering期刊出版了“合成生物学：在代谢工程过程中的新方法和应用”专辑，充分肯定了利用代谢工程的原理和方法，结合合成生物学新工具的重要性。2019年，Sang Yup Lee实验室提出了系统代谢工程的内涵是将系统生物学、合成生物学、进化工程与传统的代谢工程相结合，促进高性能菌株的发展[8]。可以看出，过去30年，代谢工程不断整合新学科、新技术，其研究手段和学科内涵不断更新发展，组学工具的发展、代谢工程策略的进步、in silico代谢模拟、遗传和基因组工程、高通量筛选正在加速优化代谢通量，以提高目标生物产品的生产。本文梳理了过去30年来从经典代谢工程到系统代谢工程的发展过程和趋势。
 |
| 图 1 代谢工程发展阶段及其相应的技术策略[1-8] Fig. 1 Features of metabolic engineering in different development stages[1-8]. |
| 图选项 |
1 代谢设计：从简单到复杂1.1 宿主体系代谢工程的本质是对宿主的代谢网络进行改造，从而实现目标化合物的高效合成。因此，选择合适的宿主体系是代谢工程的基础。大肠杆菌Escherichia coli与酿酒酵母Saccharomyces cerevisiae等代谢相对清晰、遗传操作技术成熟的模式生物被广泛地用作代谢工程宿主[9-13]。在全基因组测序、基因组编辑技术、DNA大片段合成与组装技术的促进下，代谢工程宿主不再局限于E. coli与S. cerevisiae，而可以根据目标产物的合成特点，选择相应的宿主(如前体供应充足、还原力丰富、耐受性强等) (图 2)。比如，谷氨酸棒杆菌Corynebacterium glutamicum适合生产氨基酸；而梭状芽孢杆菌Clostridium sp. 用于生产丁醇；红球菌Rhodococcus opacus与解脂耶氏酵母Yarrowia lipolytica油脂合成能力突出；而琥珀酸曼氏杆菌Mannheimia succiniciproducens高产琥珀酸等。极端微生物如恶臭假单胞杆菌Pseudomonas putida由于其极强的极端环境耐受能力，还原型化合物如NAD(P)H合成能力突出，是合成毒性化合物的理想宿主。以儿茶酚(Catechol) 为底物发酵生产黏康酸(Cis, cis-muconic acid，MA) 时，产物MA诱导型启动子Pcat诱导儿茶酚1, 2-双加氧酶(基因catA与catA2) 表达，儿茶酚耐受性、双加氧酶的表达水平以及儿茶酚的转化率均得到提高，P. putida KT2440改造的工程菌株MA-6可生产高达64.2 g/L的黏康酸[14]。除微生物宿主之外，其他各具特色的体系如重组蛋白表达良好的小立碗藓Physcomitrella patens[15]、脂类代谢丰富的拟球藻Nannochloropsis sp.[16]以及模式植物底盘本氏烟草Nicotiana benthamiana[17-18]等也正广泛应用于代谢工程。
 |
| 图 2 代谢设计从简单到复杂 Fig. 2 Metabolic engineering design: from simple to complex. |
| 图选项 |
此外，新型代谢工程反应体系也在不断发展。无细胞体系不受细胞代谢调控的影响，可实现精确的在线控制，也广泛应用于遗传回路的体外分析、生物装置的体外组装、非天然化合物与生物聚合物的反应等方面[19]。Zhang等[20]利用化学酶反应平台，在10步反应内合成了9个高度氧化与骨架多样的二萜化合物。2015年，Stephanopoulos实验室将紫杉醇前体的合成途径分成两个模块，分别导入工程改造的E. coli与S. cerevisiae中，在混合培养过程中互惠共生，最终发酵生产氧化紫杉醇33 g/L[21]。Wang等[22]将环烷烃生成脂肪族α, ω二羧酸(Aliphatic α, ω-dicarboxylic acids，DCAs) 的合成途径分成3个模块导入不同的细胞进行混合培养，实现环烷烃到DCAs的高效合成。
1.2 合成途径30年来，越来越多的复杂代谢物的生物合成机制得以解析。S. cerevisiae、N. benthamiana等工程改造的底盘体系也为复杂代谢物的合成提供了许多关键中间体。因而，代谢工程合成的复杂代谢物也越来越多(图 2)。抗疟疾药物前体青蒿酸(Artemisinic acid) 在S. cerevisiae中异源合成是其中的典型。S. cerevisiae可以合成法尼基焦磷酸(Farnesyl pyrophosphate，FPP)，但无法合成紫穗槐二烯(Amorphadiene)，因此需引入黄花蒿Artemisia annua来源的紫穗槐二烯合酶(Amorphadiene synthase，ADS)。紫穗槐二烯生成青蒿酸需要3步反应，分别由紫穗槐二烯氧化酶(Amorphadiene oxidase，CYP71AV1、CPR1、CYB5)、青蒿醛脱氢酶(Artemisinic aldehyde dehydrogenase，ALDH1)、醇脱氢酶(Alcohol dehydrogenase，ADH1) 催化完成。因此，需要在S. cerevisiae中表达4个关键基因才能实现青蒿酸的合成[13]。此后，Luo等[23]在S. cerevisiae中表达9个基因成功合成大麻素(Cannabinoids)。Brown等[24]在S. cerevisiae中表达14个基因合成单萜吲哚生物碱异胡豆苷(Strictosidine)。在此基础上，15个基因参与合成的莨菪烷类生物碱(Tropane alkaloids)[25]，需13个异源基因的鸦片(Opioids) 与25个异源基因的那可汀(Noscapine) 均在S. cerevisiae中成功合成[26-27]。最近，秋水仙碱(Colchicine) 在N. benthamiana中也已成功合成[17]。复杂化合物的合成过程涉及区间化修饰。东莨菪碱(Scopolamine) 在S. cerevisiae中的合成需经过线粒体、过氧化物酶体、核膜、液泡与高尔基体等多个不同区间内部或者膜修饰过程[25]。
1.3 工程设计早期代谢工程主要是提升细胞原有的代谢能力，合成生物技术引入代谢工程领域，大大提升了细胞“从无到有”的代谢能力。一方面，通过工程改造或者定向进化获得新途径或者新化合物(图 2)。如理性设计的乙醇醛合酶(GALS) 与磷酸转酮酶(ACPS) 组成的全新乙酰辅酶A合成途径[28]。而工程改造的2-酮酸脱羧酶(KIVD) 与醇脱氢酶(ADH6)，在E. coli中实现支链氨基酸到非天然长链醇的合成[29]。另一方面，代谢途径重新设计也取得了成功。Schwander等[30]将9个不同物种来源的17个酶组装成体外CO2固定途径，经过多次酶工程改造与代谢验证，每分钟每毫克蛋白可固定5 nmol的CO2。South等[31]将苹果酸合成酶以及绿藻来源的乙醇酸脱氢酶转到烟草叶绿体中，使乙醇酸在叶绿体中不断生成苹果酸进入卡尔文循环，从而提高光合作用效率。计算机辅助的途径预测工具也用于代谢途径设计。Yim等[9]利用SimPheny BioPathway Predictor预测获得琥珀酰辅酶A与α-酮戊二酸为前体的1, 4-丁二醇的合成途径，两条途径在E. coli中同时表达可发酵生产18 g/L的1, 4-丁二醇。
工程设计增加菌株的代谢性能，比如利用C1资源的能力，随着环境保护、资源供应等问题的突出而越来越受到关注。Kim等[32]在E. coli中建立的甘氨酸可逆剪切途径以及卡尔文循环(Calvin-cycle) 与四氢叶酸(Tetrahydrofolate) 循环途径均可利用CO2与甲酸。染色体进一步整合ftl、fch、mtd基因，过表达甘氨酸剪切反应，增加丙酮酸合成等，E. coli最终可直接利用CO2或者甲酸进行生长。Gleizer等[33]在敲除中心代谢途径的E. coli中，共表达卡尔文循环途径(Rubisco)与甲酸脱氢酶(Formate dehydrogenase，FDH)、磷酸核糖激酶(Phosphoribulo-kinase，Prk)、碳酸酐酶(Carbonic anhydrase)，通过实验室适应性进化也成功获得了直接利用CO2的菌株。
2 流量优化：从静态到动态系统生物学的研究，使得代谢流量与代谢控制分析(Metabolic fluxes and metabolic control)、代谢工程的计算方法(Computational methods of metabolic engineering) 等理论与技术得以发展[34-35]。随着合成生物学技术的发展，代谢工程改造从传统的过表达与途径敲除，发展成了基于代谢流量分布的理性控制。通过增加前体供应、辅因子循环[36]、启动子工程[11]、核糖体工程[37]、基因间区调控(Tunable intergenic regions，TIGRs)[38]等策略平衡各基因的表达；利用底物通道[39]，途径模块化等代谢流量优化方法，减少中间产物积累，提高代谢工程的产量。随着基因组尺度代谢模型(Genome-scale metabolic model，GSM) 在多个生物体系中的建立[40-46]，基于基因组代谢模型的菌株优化方法也广泛应用于代谢工程改造[47]。为解决工程改造引起的菌株生长缺陷与代谢负担，合成小调控RNA (Synthetic small regulatory RNA)[48]，CRISPR干扰(Clustered regularly interspaced short palindromic repeats interference)[49]等转录水平调控方法也不断发展(图 3)。利用细胞的生存压力驱动目标产物合成也是平衡细胞生长与产物合成的重要方法。比如敲除E. coli BW25113的丙酮酸合成途径后，邻氨基苯甲酸(Anthranilate) 合成途径成为丙酮酸的唯一来源，在生长压力下，邻氨基苯甲酸实现高效合成[50]。
 |
| 图 3 静态代谢调控策略 Fig. 3 Strategies for static metabolic engineering. |
| 图选项 |
随着调控元件(如转录调控因子、核糖开关)的不断丰富，动态调控机制研究的逐渐深入，动态调控系统已经成功应用于代谢工程的精细调控[51]。根据调控信号的不同，动态调控可以分为温度、光、pH、溶氧等环境因素诱导；异丙基-β-D-硫代吡喃半乳糖苷(Isopropyl β-D-thiogalactoside，IPTG) 等化学诱导物诱导，群体感应诱导以及细胞代谢产物诱导几大类(图 4)。温敏抑制子cI857调控的PL与PR启动子是目前应用最广泛的温度诱导系统[52]。海滨赤杆菌Erythrobacter litoralis来源的光遗传转录系统，可控制S. cerevisiae在光照条件下生长，而在黑暗条件下进行产物合成[53]。在黑曲霉Aspergillus niger中生产有机酸时，Pgas启动子在pH 2.0启动基因表达；而在pH高于5.0时关闭基因表达[54]。在E. coli中合成2, 3-丁二醇和1, 3-丙二醇时，Nar启动子响应厌氧条件[55]。化学诱导物常用于双稳态切换系统。当细胞生长到一定程度时，添加化学诱导物启动目标产物合成[56]。群体感应是依赖于细胞密度的调控系统，具有广泛适用性。通过调控感应系统的表达强度，信号响应可以在不同细胞密度发生，从而提高肌醇、葡糖二酸的产量[57]。多个群体感应系统的多层代谢调控也成功应用于代谢工程改造[58]。途径代谢物动态调控可通过转录因子与核糖开关应答实现。其中转录因子响应代谢物浓度变化的调控系统包括响应acyl-CoA的FadR[59]、响应malonyl-CoA的FapR[60]、响应柚皮素的FdeR[61]、响应香兰素的HucR[62]、响应6-磷酸葡糖胺的NagR与GamR[63]等。核糖开关通过控制转录起始调控基因的表达。目前已经有响应茶碱[64]、硫胺素焦磷酸[65]、赖氨酸[66]、甘氨酸[67]以及唾液酸[68]等核糖开关成功应用于代谢工程控制。
 |
| 图 4 动态代谢调控策略 Fig. 4 Strategies for dynamic metabolic engineering. |
| 图选项 |
3 进化工程：从基因到菌株由于细胞代谢、调控以及信号网络尚不完全清楚，理性改造提高宿主的代谢性能面临诸多挑战。进化工程通过模拟自然进化，迅速获得优良细胞特性，而无需深入地理解细胞代谢，是理性代谢工程的互补方法。随着进化工程与自动化细胞培养、在线监测、高通量测序、多组学分析等技术的结合，其在系统代谢工程中发挥着不可替代的作用。我们从定向进化(Directed evolution)与实验室适应性进化(Adaptive laboratory evolution，ALE) 两个方面对进化工程在代谢工程中的应用进行总结(图 5)。
1993年美国科学家Frances H. Arnold首次提出“酶的定向进化”，旨在通过快速随机突变与高通量筛选在短时间内实现酶的功能优化或者改造。由此，Arnold实验室获得了不依赖于TrpA的色氨酸合酶TrpB[69]、高效氧化烷烃生成醇类化合物的P450[70]以及氧化烯烃生成醛的P450氧化酶[71]。因其在酶的定向进化方面的开创性贡献，2018年Arnold被授予诺贝尔化学奖。此外，定向进化获得的异戊二烯合酶(Isoprene synthase) 在工程改造的S. cerevisiae中可发酵生产3.7 g/L的异戊二烯[72]。定向进化的突变体文库可通过由随机突变或者定点突变产生。易错PCR突变方法。全局转录机制工程(Global transcription machinery engineering，gTME)[74]、多重自动基因组工程(Multiplex automated genome engineering，MAGE)[75]、转录因子激活样效应核酸酶(Transcription activator-like effector nucleases，TALENs)[76]、CRISPR/Cas9[77]等基因组水平的进化方法，也用于增加突变体库的遗传多样性。最近开发的M13噬菌体辅助连续进化系统(PACE)，进化速度比传统的定向进化快100倍，可实现微生物“自发”的连续定向进化[78]。
 |
| 图 5 进化工程：定向进化与实验室适应性进化 Fig. 5 Evolutionary engineering: directed evolution and adaptive laboratory evolution. |
| 图选项 |
与酶的定向进化不同，ALE直接对菌株进行连续培养和筛选，以获得耐受性能改善、生长速率提高、碳源利用增加的菌株。例如，利用ALE重塑S. cerevisiae的代谢途径，获得高产脂肪酸的菌株[79]。缺乏丝氨酸降解途径的E. coli经过45 d的适应性进化，最终可发酵生产37.3 g/L的丝氨酸[80]。酪氨酸缺陷型菌株以苯丙氨酸羟化酶为遗传选择压力，在限制性培养基中进化后，成功将苯丙氨酸转化成酪氨酸维持细胞生长，并带动辅因子循环途径[81]。
4 机器学习：从代谢设计到流量优化代谢工程改造需要长期的试验与纠错过程，才能最终获得成功。比如，Amyris公司需要花费150每人每年的时间生产青蒿酸；Dupont公司则需要575每人每年的时间生产丙二醇[82]。这种低效的模式显然是不可持续的，亟需成熟的生物设计来减少试错的过程。而成熟的生物设计面临的最大挑战是准确预测代谢工程的结果。组学数据的爆发式增长，为基因发现、生物功能理解、生物改造提供了强大的支撑。然而，缺乏深度解析的数据却不能为代谢工程改造提供可行的策略。
利用多功能组学数据系统改善菌株性能，为生物设计提供预测的机器学习，是解决以上问题的关键。目前，机器学习在自动驾驶[83]、自动翻译[84]、面部识别[85]、自然语言解析[86]、癌症检测[87]、歌词显示[88]等领域已获得巨大成功。作为人工智能(Artificial intelligence，AI) 的子学科，机器学习是通过训练自动提高计算机算法预测能力的过程。目前应用于代谢工程的机器学习算法包括深度学习(Deep learning)、人工神经元网络(Artificial neural network，ANN)、聚类(Clustering)、决策树(Decision tree)、线性回归(Linear regression)、偏最小二乘法回归(Partial least squares regression)、高斯过程(Gaussian process) 以及支持向量机(Support vector machine，SVM) 等[89]。
机器学习在代谢设计的基因注释、途径设计与构建、代谢流量优化等方面均有应用(图 6)。预测翻译起始位点以及开放读码框的DeepRibo[90]、预测酶学委员会编号的DeepEC[91]均是深度神经元网络训练的。3个人工神经元网络与蒙特卡洛树搜索算法(Monte carlo tree search algorithm，3N-MCTS) 组成的逆合成法可用于代谢途径发现，为代谢设计提供更多选择[92]。当合成途径的酶未知时，支持向量机[93]和高斯过程[94]可用于预测酶催化反应。机器学习辅助的定向进化，可帮助获得催化效率提高[95]、颜色改变[96]、热力学稳定性更好[97]的新酶。在深度学习算法的帮助下，理性蛋白设计也已经成功实现[98-100]。在代谢流量优化方面，神经元网络预测基因表达[101]，偏最小二乘法回归优化启动子强度与诱导物浓度和时间[102]，随机预测与神经元网络预测核糖开关的动态范围[103]等，均是对基因表达剂量的优化。机器学习还直接对多基因代谢途径进行优化，包括支持向量回归指导柠檬烯(Limonene) 在E. coli中合成[104]、高斯过程指导番茄红素(Lycopene) 在E. coli中合成[105]、模型集成指导色氨酸在S. cerevisiae中合成[106]。此外，机器学习也用于改善代谢工程工具如CRISPR的基因编辑效率[107]、DNA组装与转化效率[108]。最后，机器学习算法比如决策树、遗传算法等也应用于代谢放大过程中的发酵参数分析[109]。为工业发酵，例如德巴利氏酵母Debaryomyces nepalensis发酵生产木糖醇(Xylitol)，提供了重要的参考意见[110]。
 |
| 图 6 机器学习在代谢工程中的应用 Fig. 6 Application of machine learning in metabolic engineering. |
| 图选项 |
5 总结与展望科学认知的不断深刻，遗传操作技术的迅猛发展，生物元件的开发，进化工程与机器学习的持续创新，工程方法与策略在应用中大放异彩，代谢工程在30年进化过程中，取得了巨大的成就(表 1)。
表 1 不同策略在代谢工程中的应用Table 1 The applications of different strategies in metabolic engineering
| Host | Product/goal | Strategies/tools | References | |
| Metabolic design | E. coli | Reticuline | Selected enzymes from different host | [12] |
| S. cerevisiae | Artemisinic acid | Precursor improvement/new P450 | [13] | |
| P. putida | Cis, cis-muconic acid | Synthetic promoter | [14] | |
| N. benthamiana | Colchicine alkaloid | Co-expression/truncation | [17] | |
| Flux optimization | E. coli | Taxadine | Pathway modularization | [11] |
| E. coli | Resveratrol | CRISPRi | [49] | |
| E. coli | Anthranilate | Metabolite addiction | [50] | |
| A. niger | Itaconic acid | Low-pH-inducible promoter, Pgas | [54] | |
| E. coli | 5-aminolevulinic acid | Glycine Riboswitch | [67] | |
| Evolutionary engineering | S. cerevisiae | Isoprene | Directed evolution | [72] |
| S. cerevisiae | Free fatty acids | Adaptive laboratory evolution | [79] | |
| E. coli | L-serine | Adaptive laboratory evolution | [80] | |
| E. coli | Grow | Adaptive laboratory evolution | [81] | |
| Machine learning | E. coli | Expression of dxs | Neural network | [101] |
| E. coli | Limonene | Support vector machine | [104] | |
| E. coli | Lycopene | Gaussian process | [105] | |
| S. cerevisiae | Tryptophan | Ensemble models | [106] | |
| D. nepalensis | Xylitol | Artificial neural network | [110] |
表选项
以5G技术为标志，人类社会正在进入一个全新的智能时代。生物技术与人工智能、自动化、云计算以及物联网等技术的联系越来越紧密。合成生物学与电子信息、材料等其他学科的交叉融合产生了许多新的方向。智能手机控制小鼠体内的血糖浓度已经成功实现[111]，全自动化合物合成机器人也已问世[112]，现代生物工厂已进入标准化、自动化、智能化模式[113-114]。2019年，美国科学基金会提出了半导体生物学发展指南，拟解决合成生物学与半导体的集成问题，制造新型材料，研发新的信息存储技术等，从而突破生物与电子元器件的界限，开拓新的生物技术领域。这预示着代谢工程未来发展的方向：通过自动化、智能化的设计，全面提升细胞和细胞、细胞和传感器、细胞和反应器、细胞和计算机之间的交互能力。人工智能强大的数据采集与过程处理能力，有望实现代谢过程的自动化与智能化过程控制。可以预见，代谢工程与其他新兴学科和技术相结合，仍将焕发持续的生机和活力。
参考文献
| [1] | Bailey JE. Toward a science of metabolic engineering. Science, 1991, 252(5013): 1668-1675. DOI:10.1126/science.2047876 |
| [2] | Stephanopoulos G, Vallino JJ. Network rigidity and metabolic engineering in metabolite overproduction. Science, 1991, 252(5013): 1675-1681. DOI:10.1126/science.1904627 |
| [3] | Kitano H. Systems biology: a brief overview. Science, 2002, 295(5560): 1662-1664. DOI:10.1126/science.1069492 |
| [4] | Stephanopoulos G, Alper H, Moxley J. Exploiting biological complexity for strain improvement through systems biology. Nat Biotechnol, 2004, 22(10): 1261-1267. DOI:10.1038/nbt1016 |
| [5] | Gardner TS, Cantor CR, Collins JJ. Construction of a genetic toggle switch in Escherichia coli. Nature, 2000, 403(6767): 339-342. DOI:10.1038/35002131 |
| [6] | Elowitz MB, Leibler S. A synthetic oscillatory network of transcriptional regulators. Nature, 2000, 403(6767): 335-338. DOI:10.1038/35002125 |
| [7] | Tyo KE, Alper HS, Stephanopoulos GN. Expanding the metabolic engineering toolbox: more options to engineer cells. Trends Biotechnol, 2007, 25(3): 132-137. DOI:10.1016/j.tibtech.2007.01.003 |
| [8] | Choi KR, Jang WD, Yang D, et al. Systems metabolic engineering strategies: integrating systems and synthetic biology with metabolic engineering. Trends Biotechnol, 2019, 37(8): 817-837. DOI:10.1016/j.tibtech.2019.01.003 |
| [9] | Yim H, Haselbeck R, Niu W, et al. Metabolic engineering of Escherichia coli for direct production of 1, 4-butanediol. Nat Chem Biol, 2011, 7(7): 445-452. DOI:10.1038/nchembio.580 |
| [10] | Andreessen B, Lange AB, Robenek H, et al. Conversion of glycerol to poly(3-hydroxypropionate) in recombinant Escherichia coli. Appl Environ Microbiol, 2010, 76(2): 622-626. DOI:10.1128/AEM.02097-09 |
| [11] | Ajikumar PK, Xiao WH, Tyo KEJ, et al. Isoprenoid pathway optimization for Taxol precursor overproduction in Escherichia coli. Science, 2010, 330(6000): 70-74. DOI:10.1126/science.1191652 |
| [12] | Nakagawa A, Minami H, Kim JS, et al. A bacterial platform for fermentative production of plant alkaloids. Nat Commun, 2011, 2: 326. DOI:10.1038/ncomms1327 |
| [13] | Ro DK, Paradise EM, Ouellet M, et al. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature, 2006, 440(7086): 940-943. DOI:10.1038/nature04640 |
| [14] | Kohlstedt M, Starck S, Barton N, et al. From lignin to nylon: Cascaded chemical and biochemical conversion using metabolically engineered Pseudomonas putida. Metab Eng, 2018, 47: 279-293. DOI:10.1016/j.ymben.2018.03.003 |
| [15] | Reski R, Bae H, Simonsen HT. Physcomitrella patens, a versatile synthetic biology chassis. Plant Cell Rep, 2018, 37(10): 1409-1417. DOI:10.1007/s00299-018-2293-6 |
| [16] | Poliner E, Farré EM, Benning C. Advanced genetic tools enable synthetic biology in the oleaginous microalgae Nannochloropsis sp. Plant Cell Rep, 2018, 37(10): 1383-1399. DOI:10.1007/s00299-018-2270-0 |
| [17] | Nett RS, Lau W, Sattely ES. Discovery and engineering of colchicine alkaloid biosynthesis. Nature, 2020, 584(7819): 148-153. DOI:10.1038/s41586-020-2546-8 |
| [18] | Li JH, Mutanda I, Wang KB, et al. Chloroplastic metabolic engineering coupled with isoprenoid pool enhancement for committed taxanes biosynthesis in Nicotiana benthamiana. Nat Commun, 2019, 10(1): 4850. DOI:10.1038/s41467-019-12879-y |
| [19] | Perez JG, Stark JC, Jewett MC. Cell-free synthetic biology: engineering beyond the cell. Cold Spring Harb Perspect Biol, 2016, 8(12): a023853. DOI:10.1101/cshperspect.a023853 |
| [20] | Zhang X, King-Smith E, Dong LB, et al. Divergent synthesis of complex diterpenes through a hybrid oxidative approach. Science, 2020, 369(6505): 799-806. DOI:10.1126/science.abb8271 |
| [21] | Zhou K, Qiao KJ, Edgar S, et al. Distributing a metabolic pathway among a microbial consortium enhances production of natural products. Nat Biotechnol, 2015, 33(4): 377-383. DOI:10.1038/nbt.3095 |
| [22] | Wang F, Zhao J, Li Q, et al. One-pot biocatalytic route from cycloalkanes to α, ω-dicarboxylic acids by designed Escherichia coli consortia. Nat Commun, 2020, 11(1): 5035. DOI:10.1038/s41467-020-18833-7 |
| [23] | Luo X, Reiter MA, D'Espaux L, et al. Complete biosynthesis of cannabinoids and their unnatural analogues in yeast. Nature, 2019, 567(7746): 123-126. DOI:10.1038/s41586-019-0978-9 |
| [24] | Brown S, Clastre M, Courdavault V, et al. De novo production of the plant-derived alkaloid strictosidine in yeast. Proc Natl Acad Sci USA, 2015, 112(11): 3205-3210. DOI:10.1073/pnas.1423555112 |
| [25] | Srinivasan P, Smolke CD. Biosynthesis of medicinal tropane alkaloids in yeast. Nature, 2020, 585(7826): 614-619. |
| [26] | Galanie S, Thodey K, Trenchard IJ, et al. Complete biosynthesis of opioids in yeast. Science, 2015, 349(6252): 1095-1100. DOI:10.1126/science.aac9373 |
| [27] | Li Y, Li S, Thodey K, et al. Complete biosynthesis of noscapine and halogenated alkaloids in yeast. Proc Natl Acad Sci USA, 2018, 115(17): E3922-E3931. DOI:10.1073/pnas.1721469115 |
| [28] | Lu X, Liu Y, Yang Y, et al. Constructing a synthetic pathway for acetyl-coenzyme A from one-carbon through enzyme design. Nat Commun, 2019, 10(1): 1378. DOI:10.1038/s41467-019-09095-z |
| [29] | Zhang KC, Sawaya MR, Eisenberg DS, et al. Expanding metabolism for biosynthesis of nonnatural alcohols. Proc Natl Acad Sci USA, 2008, 105(52): 20653-20658. DOI:10.1073/pnas.0807157106 |
| [30] | Schwander T, Schada von Borzyskowski L, Burgener S, et al. A synthetic pathway for the fixation of carbon dioxide in vitro. Science, 2016, 354(6314): 900-904. DOI:10.1126/science.aah5237 |
| [31] | South PF, Cavanagh AP, Liu HW, et al. Synthetic glycolate metabolism pathways stimulate crop growth and productivity in the field. Science, 2019, 363(6422): eaat9077. DOI:10.1126/science.aat9077 |
| [32] | Kim S, Lindner SN, Aslan S, et al. Growth of E. coli on formate and methanol via the reductive glycine pathway. Nat Chem Biol, 2020, 16(5): 538-545. DOI:10.1038/s41589-020-0473-5 |
| [33] | Gleizer S, Ben-Nissan R, Bar-On YM, et al. Conversion of Escherichia coli to generate all biomass carbon from CO2. Cell, 2019, 179(6): 1255-1263. DOI:10.1016/j.cell.2019.11.009 |
| [34] | Kim HU, Kim TY, Lee SY. Metabolic flux analysis and metabolic engineering of microorganisms. Mol Biosyst, 2008, 4(2): 113-120. DOI:10.1039/B712395G |
| [35] | Moreno-Sánchez R, Saavedra E, Rodríguez-Enríquez S, et al. Metabolic control analysis: a tool for designing strategies to manipulate metabolic pathways. J Biomed Biotechnol, 2008, 597913. |
| [36] | Satoh Y, Tajima K, Munekata M, et al. Engineering of L-tyrosine oxidation in Escherichia coli and microbial production of hydroxytyrosol. Metab Eng, 2012, 14(6): 603-610. DOI:10.1016/j.ymben.2012.08.002 |
| [37] | Darlington APS, Kim J, Jiménez JI, et al. Dynamic allocation of orthogonal ribosomes facilitates uncoupling of co-expressed genes. Nat Commun, 2018, 9(1): 695. DOI:10.1038/s41467-018-02898-6 |
| [38] | Pfleger BF, Pitera DJ, Smolke CD, et al. Combinatorial engineering of intergenic regions in operons tunes expression of multiple genes. Nat Biotechnol, 2006, 24(8): 1027-1032. DOI:10.1038/nbt1226 |
| [39] | Dueber JE, Wu GC, Malmirchegini GR, et al. Synthetic protein scaffolds provide modular control over metabolic flux. Nat Biotechnol, 2009, 27(8): 753-759. DOI:10.1038/nbt.1557 |
| [40] | Monk JM, Lloyd CJ, Brunk E, et al. iML1515, a knowledgebase that computes Escherichia coli traits. Nat Biotechnol, 2017, 35(10): 904-908. DOI:10.1038/nbt.3956 |
| [41] | F?rster J, Famili I, Fu P, et al. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res, 2003, 13(2): 244-253. DOI:10.1101/gr.234503 |
| [42] | Oh YK, Palsson BO, Park SM, et al. Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J Biol Chem, 2007, 282(39): 28791-28799. DOI:10.1074/jbc.M703759200 |
| [43] | Senger RS, Papoutsakis ET. Genome-scale model for Clostridium acetobutylicum: Part Ⅰ. Metabolic network resolution and analysis. Biotechnol Bioeng, 2008, 101(5): 1036-1052. DOI:10.1002/bit.22010 |
| [44] | Kjeldsen KR, Nielsen J. In silico genome-scale reconstruction and validation of the Corynebacterium glutamicum metabolic network. Biotechnol Bioeng, 2009, 102(2): 583-597. DOI:10.1002/bit.22067 |
| [45] | de Oliveira Dal'Molin CG, Quek LE, Palfreyman RW, et al. AraGEM, a genome-scale reconstruction of the primary metabolic network in Arabidopsis. Plant Physiol, 2010, 152(2): 579-589. DOI:10.1104/pp.109.148817 |
| [46] | Duarte NC, Becker SA, Jamshidi N, et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci USA, 2007, 104(6): 1777-1782. DOI:10.1073/pnas.0610772104 |
| [47] | Maia P, Rocha M, Rocha I. In silico constraint-based strain optimization methods: the quest for optimal cell factories. Microbiol Mol Biol Rev, 2015, 80(1): 45-67. |
| [48] | 宋书杰, 熊智强, 王勇. 利用合成调控RNA技术提高大肠杆菌异源合成红霉素前体6-脱氧红霉内酯B产量. 生物工程学报, 2015, 31(7): 1039-1049. Song SJ, Xiong ZQ, Wang Y. Enhancing erythromycin precursor 6-dEB production by using synthetic small regulatory RNAs in Escherichia coli. Chin J Biotech, 2015, 31(7): 1039-1049 (in Chinese). |
| [49] | Wu JJ, Zhou P, Zhang X, et al. Efficient de novo synthesis of resveratrol by metabolically engineered Escherichia coli. J Ind Microbiol Biotechnol, 2017, 44(7): 1083-1095. DOI:10.1007/s10295-017-1937-9 |
| [50] | Wang J, Zhang RH, Zhang Y, et al. Developing a pyruvate-driven metabolic scenario for growth-coupled microbial production. Metab Eng, 2019, 55: 191-200. DOI:10.1016/j.ymben.2019.07.011 |
| [51] | 于政, 申晓林, 孙新晓, 等. 动态调控策略在代谢工程中的应用研究进展. 合成生物学, 2020, 1(4): 439-452. Yu Z, Shen XL, Sun XX, et al. Application of dynamic regulation strategies in metabolic engineering. Syn Bio J, 2020, 1(4): 439-452 (in Chinese). |
| [52] | Harder BJ, Bettenbrock K, Klamt S. Temperature-dependent dynamic control of the TCA cycle increases volumetric productivity of itaconic acid production by Escherichia coli. Biotechnol Bioeng, 2018, 115(1): 156-164. DOI:10.1002/bit.26446 |
| [53] | Zhao EM, Zhang YF, Mehl J, et al. Optogenetic regulation of engineered cellular metabolism for microbial chemical production. Nature, 2018, 555(7698): 683-687. DOI:10.1038/nature26141 |
| [54] | Yin X, Shin HD, Li JH, et al. Pgas, a low-pH-induced promoter, as a tool for dynamic control of gene expression for metabolic engineering of Aspergillus niger. Appl Environ Microbiol, 2017, 83(6): e03222-16. |
| [55] | Hwang HJ, Kim JW, Ju SY, et al. Application of an oxygen-inducible nar promoter system in metabolic engineering for production of biochemicals in Escherichia coli. Biotechnol Bioeng, 2017, 114(2): 468-473. DOI:10.1002/bit.26082 |
| [56] | Soma Y, Tsuruno K, Wada M, et al. Metabolic flux redirection from a central metabolic pathway toward a synthetic pathway using a metabolic toggle switch. Metab Eng, 2014, 23: 175-184. DOI:10.1016/j.ymben.2014.02.008 |
| [57] | Gupta A, Reizman IMB, Reisch CR, et al. Dynamic regulation of metabolic flux in engineered bacteria using a pathway-independent quorum-sensing circuit. Nat Biotechnol, 2017, 35(3): 273-279. DOI:10.1038/nbt.3796 |
| [58] | Dinh CV, Prather KLJ. Development of an autonomous and bifunctional quorum-sensing circuit for metabolic flux control in engineered Escherichia coli. Proc Natl Acad Sci USA, 2019, 116(51): 25562-25568. DOI:10.1073/pnas.1911144116 |
| [59] | Zhang FZ, Carothers JM, Keasling JD. Design of a dynamic sensor-regulator system for production of chemicals and fuels derived from fatty acids. Nat Biotechnol, 2012, 30(4): 354-359. DOI:10.1038/nbt.2149 |
| [60] | Xu P, Li LY, Zhang FM, et al. Improving fatty acids production by engineering dynamic pathway regulation and metabolic control. Proc Natl Acad Sci USA, 2014, 111(31): 11299-11304. DOI:10.1073/pnas.1406401111 |
| [61] | Lv YK, Gu Y, Xu JL, et al. Coupling metabolic addiction with negative autoregulation to improve strain stability and pathway yield. Metab Eng, 2020, 61: 79-88. DOI:10.1016/j.ymben.2020.05.005 |
| [62] | Liang CN, Zhang XX, Wu JY, et al. Dynamic control of toxic natural product biosynthesis by an artificial regulatory circuit. Metab Eng, 2020, 57: 239-246. DOI:10.1016/j.ymben.2019.12.002 |
| [63] | Wu YK, Chen TC, Liu YF, et al. Design of a programmable biosensor-CRISPRi genetic circuits for dynamic and autonomous dual-control of metabolic flux in Bacillus subtilis. Nucleic Acids Res, 2020, 48(2): 996-1009. DOI:10.1093/nar/gkz1123 |
| [64] | Wachsmuth M, Findeiss S, Weissheimer N, et al. De novo design of a synthetic riboswitch that regulates transcription termination. Nucleic Acids Res, 2013, 41(4): 2541-2551. DOI:10.1093/nar/gks1330 |
| [65] | Masuda S, Izawa S. Applied RNA bioscience. Singapore: Springer Singapore, 2018. |
| [66] | Zhou LB, Zeng AP. Exploring lysine riboswitch for metabolic flux control and improvement of L-lysine synthesis in Corynebacterium glutamicum. ACS Synth Biol, 2015, 4(6): 729-734. DOI:10.1021/sb500332c |
| [67] | Zhou LB, Ren J, Li ZD, et al. Characterization and engineering of a Clostridium glycine riboswitch and its use to control a novel metabolic pathway for 5-aminolevulinic acid production in Escherichia coli. ACS Synth Biol, 2019, 8(10): 2327-2335. DOI:10.1021/acssynbio.9b00137 |
| [68] | Pang QX, Han H, Liu XQ, et al. In vivo evolutionary engineering of riboswitch with high-threshold for N-acetylneuraminic acid production. Metab Eng, 2020, 59: 36-43. DOI:10.1016/j.ymben.2020.01.002 |
| [69] | Buller AR, Brinkmann-Chen S, Romney DK, et al. Directed evolution of the tryptophan synthase β-subunit for stand-alone function recapitulates allosteric activation. Proc Natl Acad Sci USA, 2015, 112(47): 14599-14604. DOI:10.1073/pnas.1516401112 |
| [70] | Glieder A, Farinas ET, Arnold FH. Laboratory evolution of a soluble, self-sufficient, highly active alkane hydroxylase. Nat Biotechnol, 2002, 20(11): 1135-1139. DOI:10.1038/nbt744 |
| [71] | Hammer SC, Kubik G, Watkins E, et al. Anti-markovnikov alkene oxidation by metal-oxo-mediated enzyme catalysis. Science, 2017, 358(6360): 215-218. DOI:10.1126/science.aao1482 |
| [72] | Wang F, Lv XM, Xie WP, et al. Combining Gal4p-mediated expression enhancement and directed evolution of isoprene synthase to improve isoprene production in Saccharomyces cerevisiae. Metab Eng, 2017, 39: 257-266. DOI:10.1016/j.ymben.2016.12.011 |
| [73] | Zhang YX, Perry K, Vinci VA, et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 2002, 415(6872): 644-646. DOI:10.1038/415644a |
| [74] | Alper H, Moxley J, Nevoigt E, et al. Engineering yeast transcription machinery for improved ethanol tolerance and production. Science, 2006, 314(5805): 1565-1568. DOI:10.1126/science.1131969 |
| [75] | Wang HH, Isaacs FJ, Carr PA, et al. Programming cells by multiplex genome engineering and accelerated evolution. Nature, 2009, 460(7257): 894-898. DOI:10.1038/nature08187 |
| [76] | Sun N, Zhao HM. Transcription activator-like effector nucleases (TALENs): a highly efficient and versatile tool for genome editing. Biotechnol Bioeng, 2013, 110(7): 1811-1821. DOI:10.1002/bit.24890 |
| [77] | Garst AD, Bassalo MC, Pines G, et al. Genome-wide mapping of mutations at single-nucleotide resolution for protein, metabolic and genome engineering. Nat Biotechnol, 2017, 35(1): 48-55. DOI:10.1038/nbt.3718 |
| [78] | Richter MF, Zhao KT, Eton E, et al. Phage-assisted evolution of an adenine base editor with improved Cas domain compatibility and activity. Nat Biotechnol, 2020, 38(7): 883-891. DOI:10.1038/s41587-020-0453-z |
| [79] | Yu T, Zhou YJ, Huang MT, et al. Reprogramming yeast metabolism from alcoholic fermentation to lipogenesis. Cell, 2018, 174(6): 1549-1558.e14. DOI:10.1016/j.cell.2018.07.013 |
| [80] | Mundhada H, Seoane JM, Schneider K, et al. Increased production of L-serine in Escherichia coli through adaptive laboratory evolution. Metab Eng, 2017, 39: 141-150. DOI:10.1016/j.ymben.2016.11.008 |
| [81] | Luo H, Yang L, Kim SH, et al. Directed metabolic pathway evolution enables functional pterin-dependent aromatic-amino-acid hydroxylation in Escherichia coli. ACS Synth Biol, 2020, 9(3): 494-499. DOI:10.1021/acssynbio.9b00488 |
| [82] | Hodgman CE, Jewett MC. Cell-free synthetic biology: thinking outside the cell. Metab Eng, 2012, 14(3): 261-269. DOI:10.1016/j.ymben.2011.09.002 |
| [83] | Duarte F, Ratti C. The impact of autonomous vehicles on cities: a review. J Urban Technol, 2018, 25(4): 3-18. DOI:10.1080/10630732.2018.1493883 |
| [84] | Wu YH, Schuster M, Chen ZF, et al. Google's neural machine translation system: bridging the gap between human and machine translation. 2016, arXiv: 1609.08144v2[cs. CL]. |
| [85] | Voulodimos A, Doulamis N, Doulamis A, et al. Deep learning for computer vision: a brief review. Comput Intell Neurosci, 2018, 2018: 7068349. |
| [86] | Kreimeyer K, Foster M, Pandey A, et al. Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review. J Biomed Informatics, 2017, 73: 14-29. DOI:10.1016/j.jbi.2017.07.012 |
| [87] | Paeng K, Hwang S, Park S, et al. A unified framework for tumor proliferation score prediction in breast histopathology. In: Cardoso M. et al. (eds) Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. DLMIA 2017, ML-CDS 2017. Springer, Cham. Lect Notes Comput Sci, 2017, 10553: 231-239. |
| [88] | Chin H, Kim J, Kim Y, et al. Explicit content detection in music lyrics using machine learning. In: 2018 IEEE International Conference on Big Data and Smart Computing (BigComp). 2018: 517-521. |
| [89] | Kim GB, Kim WJ, Kim HU, et al. Machine learning applications in systems metabolic engineering. Curr Opin Biotechnol, 2020, 64: 1-9. |
| [90] | Clauwaert J, Menschaert G, Waegeman W. DeepRibo: a neural network for precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patterns. Nucleic Acids Res, 2019, 47(6): e36. DOI:10.1093/nar/gkz061 |
| [91] | Ryu JY, Kim HU, Lee SY. Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers. Proc Natl Acad Sci USA, 2019, 116(28): 13996-14001. DOI:10.1073/pnas.1821905116 |
| [92] | Segler MHS, Preuss M, Waller MP. Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 2018, 555(7698): 604-610. DOI:10.1038/nature25978 |
| [93] | Faulon JL, Misra M, Martin S, et al. Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor. Bioinformatics, 2008, 24(2): 225-233. DOI:10.1093/bioinformatics/btm580 |
| [94] | Mellor J, Grigoras I, Carbonell P, et al. Semisupervised Gaussian process for automated enzyme search. ACS Synth Biol, 2016, 5(6): 518-528. DOI:10.1021/acssynbio.5b00294 |
| [95] | Fox RJ, Davis SC, Mundorff EC, et al. Improving catalytic function by ProSAR-driven enzyme evolution. Nat Biotechnol, 2007, 25(3): 338-344. DOI:10.1038/nbt1286 |
| [96] | Saito Y, Oikawa M, Nakazawa H, et al. Machine-learning-guided mutagenesis for directed evolution of fluorescent proteins. ACS Synth Biol, 2018, 7(9): 2014-2022. DOI:10.1021/acssynbio.8b00155 |
| [97] | Romero PA, Krause A, Arnold FH. Navigating the protein fitness landscape with Gaussian processes. Proc Natl Acad Sci USA, 2013, 110(3): E193-E201. DOI:10.1073/pnas.1215251110 |
| [98] | Lu PL, Min D, DiMaio F, et al. Accurate computational design of multipass transmembrane proteins. Science, 2018, 359(6379): 1042-1046. DOI:10.1126/science.aaq1739 |
| [99] | Polizzi NF, DeGrado WF. A defined structural unit enables de novo design of small-molecule-binding proteins. Science, 2020, 369(6508): 1227-1233. DOI:10.1126/science.abb8330 |
| [100] | Basanta B, Bick MJ, Bera AK, et al. An enumerative algorithm for de novo design of proteins with diverse pocket structures. Proc Natl Acad Sci USA, 2020, 117(36): 22135-22145. DOI:10.1073/pnas.2005412117 |
| [101] | Meng H, Wang J, Xiong Z, et al. Quantitative design of regulatory elements based on high-precision strength prediction using artificial neural network. PLoS ONE, 2013, 8(4): e60288. DOI:10.1371/journal.pone.0060288 |
| [102] | Jervis AJ, Carbonell P, Taylor S, et al. SelProm: a queryable and predictive expression vector selection tool for Escherichia coli. ACS Synth Biol, 2019, 8(7): 1478-1483. DOI:10.1021/acssynbio.8b00399 |
| [103] | Groher AC, Jager S, Schneider C, et al. Tuning the performance of synthetic riboswitches using machine learning. ACS Synth Biol, 2019, 8(1): 34-44. DOI:10.1021/acssynbio.8b00207 |
| [104] | Jervis AJ, Carbonell P, Vinaixa M, et al. Machine learning of designed translational control allows predictive pathway optimization in Escherichia coli. ACS Synth Biol, 2019, 8(1): 127-136. DOI:10.1021/acssynbio.8b00398 |
| [105] | HamediRad M, Chao R, Weisberg S, et al. Towards a fully automated algorithm driven platform for biosystems design. Nat Commun, 2019, 10(1): 5150. DOI:10.1038/s41467-019-13189-z |
| [106] | Zhang J, Petersen SD, Radivojevic T, et al. Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism. Nat Commun, 2020, 11(1): 4880. DOI:10.1038/s41467-020-17910-1 |
| [107] | Kim HK, Min S, Song M, et al. Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity. Nat Biotechnol, 2018, 36(3): 239-241. DOI:10.1038/nbt.4061 |
| [108] | Wang GK, Bj?rk SM, Huang MT, et al. RNAi expression tuning, microfluidic screening, and genome recombineering for improved protein production in Saccharomyces cerevisiae. Proc Natl Acad Sci USA, 2019, 116(19): 9324-9332. DOI:10.1073/pnas.1820561116 |
| [109] | Coleman MC, Buck KKS, Block DE. An integrated approach to optimization of Escherichia coli fermentations using historical data. Biotechnol Bioeng, 2003, 84(3): 274-285. DOI:10.1002/bit.10719 |
| [110] | Pappu SMJ, Gummadi SN. Artificial neural network and regression coupled genetic algorithm to optimize parameters for enhanced xylitol production by Debaryomyces nepalensis in bioreactor. Biochem Eng J, 2017, 120: 136-145. DOI:10.1016/j.bej.2017.01.010 |
| [111] | Shao JW, Xue S, Yu GL, et al. Smartphone-controlled optogenetically engineered cells enable semiautomatic glucose homeostasis in diabetic mice. Sci Transl Med, 2017, 9(387): eaal2298. DOI:10.1126/scitranslmed.aal2298 |
| [112] | Mehr SHM, Craven M, Leonov AI, et al. A universal system for digitization and automatic execution of the chemical synthesis literature. Science, 2020, 370(6512): 101-108. DOI:10.1126/science.abc2986 |
| [113] | Carbonell P, Le Feuvre R, Takano E, et al. In silico design and automated learning to boost next-generation smart biomanufacturing. Synth Biol, 2020, 5(1): ysaa020. DOI:10.1093/synbio/ysaa020 |
| [114] | Carbonell P, Radivojevic T, García Martín H. Opportunities at the intersection of synthetic biology, machine learning, and automation. ACS Synth Biol, 2019, 8(7): 1474-1477. DOI:10.1021/acssynbio.8b00540 |
