Systems and Synthetic Metabolic Engineering

Systems and Synthetic Metabolic Engineering provides an overview of the development of metabolic engineering within medicine that is fueled by systems and synthetic biology. These newly developed, successful strategies of metabolic engineering guide the audience on how to propose and test proper strategies for metabolic engineering research. In addition to introductory, regulatory and challenges in the field, the book also covers dynamic control and autonomous regulation to control cell metabolism, along with computational modeling and industrial applications. The book is written by leaders in the field, making it ideal for synthetic biologists, researchers, students and anyone working in this area.

• Discusses the current progress of metabolic engineering, focusing on systems biology and synthetic biology
• Covers introductory, regulatory, strategies, production and challenges in the field
• Written technically for synthetic biologists, researchers, students, industrialists, policymakers and stakeholders

• Language: English
• Publisher: Academic Press
• Release date: Jul 10, 2020
• ISBN: 9780128217528

Book preview

Systems and Synthetic Metabolic Engineering

Edited by

Long Liu

Professor and Vice Dean, Department of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu province, China

Guocheng Du

Dean of the School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu province, China

Yanfeng Liu

Assoc. Professor, Department of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu province, China

Contents

Cover

Title page

Copyright

Contributors

Chapter 1: Systems biology, synthetic biology, and metabolic engineering

Abstract

1.1. Introduction

1.2. Metabolic engineering

1.3. System biology

1.4. Synthetic biology

1.5. Construct microbial cell factory by combing systems biology, synthetic biology and metabolic engineering

1.6. Conclusions

Chapter 2: Synthetic regulatory elements for fine-tuning gene expression

Abstract

2.1. Introduction

2.2. Synthetic promoters

2.3. Synthetic ribosome binding sites

2.4. Synthetic N-terminal coding sequence and proteolysis tags

2.5. Synthetic terminators

2.6. Conclusions

Chapter 3: Systems and synthetic biology-aided biosynthesis pathway design

Abstract

3.1. Introduction

3.3. Analyses of systems biology data for developing pathway design tools

3.4. Design of engineered enzymes for the synthesis of non-natural products

3.5. Conclusions

Chapter 4: Refactoring and optimization of metabolic network

Abstract

4.1. Introduction

4.2. Refactoring metabolic pathways for enhanced carbon yield

4.3. Synthetic redesign metabolic pathways for improved thermodynamic driving force

4.4. Balancing redox cofactors by metabolic network optimization

4.5. Optimization of energy metabolism in host strains

4.6. Conclusions

Chapter 5: Harnessing the hierarchy of transcriptional regulation: engineering of the gene expression network for efficient production

Abstract

5.1. Introduction

5.2. Fine-tuning biosynthesis by engineering the basic nodes and paths in the gene regulatory network

5.3. Manipulating local transcriptional regulatory network for robust biosynthesis

5.4. Reconstructing gene expression profile through the manipulation of transcription machinery

5.5. Conclusions

Chapter 6: System metabolic engineering strategies for cell factories construction

Abstract

6.1. Introduction

6.2. System-level metabolism analysis for metabolic model construction

6.3. Design and construction of novel biological parts/systems

6.4. Metabolic pathway reconstruction and optimization

6.5. Evolutionary strategies assisted improved cell factories performance

6.6. Conclusions and perspectives

Chapter 7: High-throughput screening for improving cellular and enzymatic properties

Abstract

7.1. Introduction

7.2. Solid phase selection assays used as a pre-screening tool

7.3. Microtiter plate (MTP) screening methods

7.4. FACS screening tool for reaction compartmentalization

7.5. Droplet-based microfluidic tools for directed evolution

7.6. Conclusion and outlook

Chapter 8: In vitro metabolic engineering: current status and recent progress

Abstract

8.1. Introduction

8.2. Enzymatic modules of ivMEBs

8.3. Adaptability among enzymes in the ivMEBs

8.4. Conclusions and perspectives

Chapter 9: Systems and synthetic metabolic engineering for production of biochemicals

Abstract

9.1. Introduction

9.2. N-acetylglucosamine

9.3. N-acetylneuraminic acid

9.4. Hyaluronic acid

9.5. Isoprenoid

9.6. Aromatic compounds

9.7. Vitamin K2

9.8. Conclusions

Chapter 10: Systems and synthetic metabolic engineering: Challenges and prospects

Abstract

10.1. Introduction

10.2. Economic competitiveness of bio-manufacturing

10.3. Genetic and nongenetic heterogeneity of production host during bioproduction

10.4. Big data generated from production host construction and characterization

10.5. Inherent characteristics of microbes and various requirements for different products

10.6. Conclusions

Index

Copyright

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Contributors

Yanting Cao,     Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China

Shuaili Chen

School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, Jiangsu

National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, Wuxi, Jiangsu, China

Xianzhong Chen,     Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China

Guocheng Du,     Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China

Yi-Xin Huo,     Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing, China

Lu Li

Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, People Republic of China

Key Laboratory of Industrial Biocatalysis, Ministry of Education; Tsinghua University, Beijing, China

Center for Synthetic & Systems Biology, Tsinghua University, Beijing, People Republic of China

Long Liu,     Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China

Yanfeng Liu,     Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China

Xueqin Lv,     Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China

Lianjie Ma,     Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing, China

Xiaoyan Ma,     Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing, China

Dongdong Meng,     Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China

Maofang Teng

School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, Jiangsu

National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, Wuxi, Jiangsu, China

Rongzhen Tian,     Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China

Xinlei Wei,     Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China

Yaokang Wu,     Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China

Yuanyuan Xia,     Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China

Xinhui Xing

Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, People Republic of China

Key Laboratory of Industrial Biocatalysis, Ministry of Education; Tsinghua University, Beijing, China

Center for Synthetic & Systems Biology, Tsinghua University, Beijing, People Republic of China

Xianhao Xu,     Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China

Haiquan Yang,     Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China

Xinxin Yin

School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, Jiangsu

National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, Wuxi, Jiangsu, China

Chun You,     Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China

Chong Zhang

Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, People Republic of China

Key Laboratory of Industrial Biocatalysis, Ministry of Education; Tsinghua University, Beijing, China

Center for Synthetic & Systems Biology, Tsinghua University, Beijing, People Republic of China

Guoqiang Zhang

School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi, Jiangsu

National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, Wuxi, Jiangsu, China

Xinrui Zhao,     Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China

Chapter 1

Systems biology, synthetic biology, and metabolic engineering

Xianhao Xu

Yanfeng Liu

Guocheng Du

Long Liu    Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China

Abstract

The traditional metabolic engineering has been used to improve the performance of industrial strains to produce variety of chemicals, such as bulk chemicals, biofuels, and functional nutrition. However, it is time-consuming, low-efficiency, and costly. With the development of system biology and synthetic biology, a wealth of new tools and strategies have been developed to accelerate the design-build-test cycle of metabolic engineering. In this chapter, we will introduce the development, definition, research content and method of metabolic engineering, systems biology and synthetic biology, and the relation of systems biology and synthetic biology with metabolic engineering. Besides, the application of system biology and synthetic biology in metabolic engineering were listed.

Keywords

metabolic engineering

system biology

synthetic biology

metabolic network

regulatory network

1.1. Introduction

At present, we obtain various products for daily life through chemical synthesis or direct extraction from organism. However, as energy crisis and environment deterioration become increasingly severe, microorganisms are regarded as an ideal host for utilizing cheap and recyclable materials to synthesize high-value chemicals, due to their fast growth rates and simple cultivation method, strong robustness and high yield [1–3]. Recently, microbial fermentation has been widely used in chemical, food, pharmaceutical, and other fields, but it still maintains two core problems: how to improve microbial fermentation performance and widen the scope of substrate utilization [4]. In the early stage of microbial fermentation, traditional microbial breeding techniques (natural selection and mutagenesis breeding) and fermentation process optimization played an important role in improving microbial production performance due to their simple operation method and wide variation range [5,6]. However, the limitations of traditional microbial-breeding techniques are also obvious, such as unclear genetic mechanisms, nondirectional mutation, and time-consuming. With the advent of genetic manipulation technology, metabolic engineering has emerged as a new discipline, which uses DNA recombination technology to optimize expression level of enzyme in the metabolic pathway, changes the regulatory network of the cell and promotes the secretion of the products, thereby improving the physiological characteristics and production performance of microorganisms. With the help of metabolic engineering strategies, we can redirect the metabolic flux distribution of the target pathway and competition pathway, promote the efficient accumulation of target products and broaden the substrate utilization spectrum of microorganisms. Therefore, metabolic engineering has been successfully used to increase the production of primary metabolites of microorganism, such as ethanol, butanol, glycerol, and organic acids [7–9]. However, for complex microbial metabolic networks, such as secondary metabolites, traditional metabolic engineering modification of single genes or single metabolic pathway cannot achieve the expected effect or the significant improvement, due to the poor understanding of the physiological characteristics of the microbial genetic background, metabolic network interaction, and its regulatory network. Therefore, for secondary metabolites and natural products with complex synthesis pathways, traditional microbial breeding techniques and fermentation process optimization still are main strategies to improve the strain performance [10–12].

With the rapid development of genomics technologies, such as genomics, transcriptomics, proteomics, and metabolomics, we can analyze the metabolic and regulatory network of microorganism at different levels [13–15]. However, the techniques of omics analysis often focus on a certain level of genetic information and the lack of systematic integration between different levels of genetic information. As a result, the lack of a comprehensive and in-depth analysis of the physiological characteristics of microorganisms makes it difficult to generate ideal metabolic regulation strategies. Therefore, it is necessary to build a research platform that integrates all omics data, analyzes the relationship between omics data and physiological characteristics, and accurately predicts the corresponding physiological phenotype. With the help of various omics technologies, combined with biological information such as transcription regulation and signal transduction, systems biology integrates different levels of omics data information to comprehensively analyze the functions and interactions of various biological genetic information [16–18]. On the basis of comprehensive analysis of microbial omics information, the optimal design, directional reconstruction, and multisite modification of microbial metabolic networks have been achieved at DNA, RNA, and protein levels. It provides a new method for comprehensively, systematically, and integrally reforming the production performance of industrial microorganisms. Therefore, it significantly improved our ability to analyze the metabolic and regulatory network of microorganisms, the efficiency of improving the physiological properties of microorganisms, and promotes the rapid development of metabolic engineering and industrial biotechnology. For example, as a computational tool based on systems biology, the genome-scale metabolic network model (GSMM) takes biochemical metabolic pathways as the core, and combines biochemistry, genetics, and omics data to achieve a deep and comprehensive analysis of the metabolic and regulatory network of the microorganisms. Therefore, GSMM can analyze the microbial metabolic network at the system level, accurately predict the physiological characteristics of cells, and the metabolic response of cells after genetic modification or environmental interference, and select target genes for metabolic engineering [19–21].

With the rapid development of genomic technology, systems biology, computer science, and other disciplines, researchers are no longer satisfied with the modification of existing metabolic pathways, metabolic networks and regulatory networks in microorganisms to obtain the products that exist in nature. Instead, they hope to reprogram existing microbial hosts to obtain intelligent cell factories or de novo building a new life system, which could biosynthesize, the target products with high yield, titer and productivity. Synthetic biology is based on the research of systems biology, and uses problem-oriented and bottom-up engineering design ideas to build standardized elements and modules to obtain microbial chassis cells. Using basic biological elements, researchers have designed and constructed synthetic devices such as gene switches, oscillators, amplifiers, logic gates, and counters to reprogram microorganism, thereby perform special functions [22,23]. Synthetic biology has modularized existing metabolic pathways and integrated them into chassis cells to efficiently synthesize a variety of bulk chemicals and fine chemicals, such as butanol, artemisinin, paclitaxel, protopanaxoside, carotenoids, etc. [24–27]. Using existing synthetic biology tools, researchers can design unnatural metabolic pathways and de novo design enzymes with new catalytic functions to obtain unnatural compounds. In recent years, the developed DNA assembly technology and nucleic acid chemical synthesis methods have been used to synthesize artificial living organisms, such as the genome of bacteriophage, Mycoplasma genitalium and Saccharomyces cerevisiae [28–30]. Besides, the genome editing tools, such as transcriptional activator-like effector (TALE) and clustered regularly interspaced short palindromic repeats/CRISPR associated proteins (CRISPR/Cas) [31,32], have been used to rapidly modify the genome or transcription of microbial host. Moreover, the developed genetic circuits have been used to intelligently control the metabolic pathways and regulatory networks of microbial hosts [33,34]. Therefore, synthetic biology reduces the design-build-test cycle of metabolic engineering, and also has important application potential in the fields of chemical synthesis (including materials, energy, and natural compounds), medicine, agriculture, and environment protection [35,36].

In conclusion, the development of system and synthetic biology significantly accelerates the process the metabolic engineering. The integration of these three disciplines will cause the next technological revolution. Therefore, this chapter will introduce the development, definitions, research content, and significance of systems biology, synthetic biology and metabolic engineering, and the relation of systems biology, and synthetic biology with metabolic engineering. This chapter will also address the remarkable progresses in the field of metabolic engineering fueled by systems biology and synthetic biology.

1.2. Metabolic engineering

Compared to chemical biosynthesis and extract, microbial fermentation is sustainable and environmentally friendly. With the depletion of traditional petroleum resources, microbial fermentation will be an important technological strategy to replace traditional petrochemical industry. As cell factories, microorganisms are the ideal host for the production of a variety of homologous and heterologous chemicals, including fermentation products, amino acids, biofuels, materials, and many secondary metabolites [37–39]. Just from an economic standpoint, how to transform microorganisms to enable them to efficiently produce target chemicals with high-titer, high-yield, and high-productivity, is the main goal of metabolic engineering.

1.2.1. The development of metabolic engineering

In 1972 and 1974, Boyer and Cohen respectively published two articles that used DNA recombination technology to introduce foreign genes into microbial hosts and the gene was successful expressed [40,41]. This made researchers aware of the application prospects of DNA recombination technology, so they successfully synthesized a variety of drugs in microorganisms, such as insulin and interferon. Researchers also believe that it is convenient to introduce different genes into microorganisms to produce many different chemicals. However, researchers quickly discovered that, unlike insulin that could be conveniently synthesized by overexpressing a single gene, overexpression of a simple compound (such as ethanol) requires coordination of the expression of genes in the ethanol synthesis pathway. Therefore, researchers realized that it was necessary to combine DNA recombination technology with chemical reaction engineering to control the expression levels of enzymes and gene in the cell, thereby efficiently synthesize the target compounds. Since then, a variety of complex compounds have been synthesized in microorganisms, such as phenylalanine and guanosine [42,43]. In 1991, Bailey and Stephanopoulos summarized the research carried out by researchers in the 1980s on the design and operation of biochemical reaction systems [44,45]. These two articles laid the scientific foundation of metabolic engineering and marked the emergence of the discipline of metabolic engineering. These two papers summarized the basic theory of metabolic engineering: (1) Make the bacteria obtain heterogeneous activity: Supplementing incomplete metabolic pathways in microorganisms, and integrating heterologous gene and pathway from other species into the microbial host. In this way, a hybrid metabolic network is constructed to enable microorganisms to synthesize new products. The integration of promising natural motifs into microorganisms, which could increase the robustness of the strains, so that enable strain to overcome the limitations of environmental conditions in bioprocessing, such as the introduction of hemoglobin VHb from Vitreoscilla, which enables the strains to overcome the limit of dissolved oxygen during fermentation. The introduction of foreign genes enables strains to utilize low-cost, green and recyclable raw materials to synthesize the target products (2) Change the flow direction of the metabolites: Through the analysis of the rigid metabolic network and the determination of the master node, the metabolic flow of the metabolic network is introduced into the synthesis pathway of the target product, and the flux of the competitive pathway is weakened, so that the strain can efficiently synthesize the corresponding products. (3) Be aware of the metabolic engineering is a design-build-test cycle: the process of metabolic engineering is a design-build-test cycle. We should analyze the result of each round cycle, and find out new modification target, which could further improve the performance of strain. Besides, we should select suitable genetic manipulation tool for strain, analyze the physiological response of each step of pathway, and use the theory of metabolic control analysis to cope with the complexity of the metabolic network, thereby minimize the response cascade.

1.2.2. The definition and research content of metabolic engineering

The metabolic activity of living cells is carried out by a regulated network of thousands of highly coupled enzyme-catalyzed reactions, and a selective membrane transport system. However, metabolic networks evolved in the nature are not optimal for important practical applications genetically. Therefore, the properties of biological processes can be enhanced by genetic modification of cells. Based on such recognition, Bailey defined metabolic engineering in the paper [44]: Metabolic engineering can improve the activity of cells by using DNA recombination technology to catalyze, transport, and regulate the enzymes of cells. In 1998, Stephanopoulos et al. gave the definition of metabolic engineering in a further way: Modifying the special biochemical reactions with DNA recombination technology, introducing new reactions to improve the production of products or properties of cells in a targeted way. The basic characteristics of these definitions are locating the target that needs modification or intending to introduce special biochemical reactions. Once such targets are confirmed, the established molecular biology techniques are used to amplify, inhibit or delete, transfer, or deregulate the corresponding gene or enzyme. To achieve the goal, the generalized recombination DNA technology has been widely used in each step. It should be noted that metabolic engineering is different from traditional genetic engineering in that it involves the entire metabolic system rather than the overexpression of few genes. At present, the main problem to be solved by metabolic engineering is to change the carbon metabolic distribution in metabolic network and change the property of strain by modifying its regulatory network. The typical goal of metabolic engineering is to modify the primary and secondary metabolism, and to introduce the carbon metabolic flow into the biosynthesis pathway of the target product to obtain the maximum titer, yield, and productivity of the product.

The research content of metabolic engineering mainly includes the following aspects: (1) increasing the production of natural products in the existing metabolic pathways of cells; (2) modification of the existing metabolic pathways of the cells to synthesize new products, which can be intermediate metabolites or modified final products; (3) integrating the metabolic pathways of different cells to build a new metabolic pathway, thereby generating new products that the cells themselves cannot synthesize; (4) optimizing the biological characteristics of cells, such as: growth rate, the tolerance of extreme environmental conditions, including temperature, pH, osmotic pressure, and oxidative stress, etc.

1.2.3. The research method of metabolic engineering

(1) Metabolic flux analysis: In MFA, the ¹³C isotopic labeling technology is used to culture cells in the ¹³C-labeled carbon source environment. When the isotopic distribution in the metabolic network reaches a steady state, the isotopic distribution in metabolites is measured by gas chromatography-mass spectrometry (GC-MS) or ¹³C-NMR (Nuclear Magnetic Resonance). Then, based on the measurement results, the intracellular flux is calculated by the stoichiometric model of the main reactions and the mass conservation of metabolites in cells, and the extracellular flux is calculated by the uptake rate of substrates and the secretion rate of products. Finally, the results of flux calculation are presented in the form of metabolic-flux path graph, including the main biochemical reactions and the steady-state flux of each reaction. This information will help researchers perform further analysis, including identifying key nodes in the pathway, discovering new pathways within the host cell, and estimating the maximum theoretical output of the product and the required cofactors and intermediates in the complex network pathway. According to the analysis results, the target of next round of genetic manipulation can be selected, thereby changing the distribution of the metabolic network of the cell, so that the metabolic flux flows more to the target product.

(2) Metabolic control analysis: Because there are many parallel reactions, metabolic cycles, and two-way reactions in the intracellular metabolic network, MFA cannot provide enough accurate measurement. In the late 1990s, a lot of complex algorithms were developed, which made the measurement of intracellular flux more accurate. Theses algorithms also plays a positive role in the development of metabolic control analysis (MCA). As a logical extension of traditional control theory, MCA relies on experiments to accurately measure the flux of metabolic pathway and its response to system disturbance. It can calculate the flux control coefficient (FCC) of each enzyme in the pathway to characterize the control degree of enzyme to intracellular metabolite flux; the concentration control coefficient (CCC) to characterize the control degree of enzyme to the concentration of a specific metabolite; The elastic coefficient (EC) indicates the ability of enzyme to respond to disturbance (such as substrate concentration, inhibitor concentration). FCC and CCC are the characteristics of the whole network, while EC is the function of specific enzyme. In a word, this information can be used to tease out the control structure of the metabolic network and elucidate the effect of relative changes in enzyme activity on pathway flux, so that more flux can be transferred to the target product.

(3) Flux balance analysis: The sequencing of the entire genome of microorganisms and the development of gene function annotation tools have greatly promoted the construction of a genome-level metabolic network model and improved the ability to analyze the structure of metabolic networks. Flux balance analysis (FBA) is a method to construct a genome-scale metabolic network model (GEM) to recognize the complex metabolic network of microorganisms at the system level, and simulate and predict the metabolic response of cells after environmental disturbances or genetic reorganization. The best combination of gene knockout and overexpression is screened to increase the organism’s ability to synthesize the target product.

1.2.4. Inverse metabolic engineering

The metabolic pathway of organisms is a complex network structure. The phenotype of a cell is not controlled by a single genotype, but a specific phenotype of a cell is controlled by multiple genes. Traditional metabolic engineering strategies focus on metabolic analysis and metabolic modification. It depends on the knockout or overexpression of single or multiple specific genes, such as weakening competition pathways, expanding the biosynthesis pathway of target products, and introducing new pathways. However, a clear understanding of the metabolic pathways and networks of a particular metabolite or specific phenotype is a prerequisite for successful modification of strains by using these methods [46]. Due to the complexity of the cellular metabolic system and the poor understanding of the cellular metabolic network, the results of traditional metabolic engineering are often not ideal. In order to solve this problem, in 1996, Bailey proposed a new metabolic engineering strategy, reverse metabolic engineering, which emphasizes the use of reverse thinking to design and optimize metabolic networks. Reverse metabolism engineering mainly includes three steps: First, identifying, constructing or calculating the desired phenotype of the strain. Second, determining the genetic and environmental factors that confer the phenotype. Finally, the phenotype is conferred to other strains through directed genetic manipulation or changing environmental conditions [47–49].

Traditional reverse metabolic engineering techniques include spontaneous mutation, physicochemical mutagenesis, and transposition mutation. The phenomenon of spontaneous mutation has been used as long as thousands of years ago. In recent years, many scholars have used spontaneous mutation to increase the yield of products and the substrate spectrum of strains, such as increasing the yield of 1,3-propanediol in Klebsiella pneumonia, and enhance the xylan utilizing ability of Saccharomyces cerevisiae [50,51]. Physical and chemical mutagenesis is a classic method for breeding strains. This technology uses a suitable mutagen to mutate the strains, combined with effective screening strategies to improve the performance of the strains. Physical and chemical mutagenesis has the advantages of simple operation and low-technical requirements, but it is difficult to know which genes have been mutated. The transposon is a basic element that can be autonomously copied and shifted on the chromosome, which is the root cause of the phenomenon of transposition. Using the transposition system, a variety of mutations, including gene insertions, deletions, and inversions can be generated. Random transposition mutation technology is the most commonly used transposition strategy for isolating and cloning genes in eukaryote. Using transposons, a piece of gene can be randomly inserted into any position in the genome, inactivating the expression of gene and subsequently causing the change of phenotypic [52,53].

Traditional reverse metabolic engineering techniques have achieved great results in industrial breeding, while these methods have long-research cycles, large workloads, and hidden safety hazards during operation. Therefore, it is necessary to find new methods to effectively solve the earlier problems. Recently, a variety of new reverse metabolic engineering techniques have been developed to promote the process of strain breeding, such as follows:

1. Atmospheric and room temperature plasma (ARTP) method: ARTP relies on uniformly distributed high concentrations of neutral active particles to change the genetic characteristics of microorganisms, which can directly change the molecular structure at the nucleotide level, such as causing DNA damage, transversion, conversion, transfer, insertion and deletion, or making circular plasmids open, break, or even break, or cause double- or single-strand breaks in DNA [54–56]. ARTP can also indirectly affect intracellular genetic material, using the damage of the entire cell to initiate multiple repair mechanisms of DNA, such as SOS repair mechanisms. These imprecise repairs will trigger complex regulatory networks in biologically reactive cells, causing changes in genetic material and metabolic pathways. Therefore, the mutant strain obtained by ARTP technology has good genetic stability. In addition, the atmospheric and room temperature effects of ARTP can alter the permeability of the plasma membrane of strain. It makes the cell matrix produce a large amount of reactive oxygen to enter the cell and interact with biological macromolecules, thereby changing the macromolecule conformation and activity. Therefore, the degree of damage to DNA by ARTP technology is significantly higher than other physical and chemical mutagenesis methods.

2. Global transcription machinery engineering, gTME: gTME was proposed by Alper in 2006, which is a new technology that can globally change the transcription level of cells [57]. gTME mutates the key transcriptional elements in the transcription complex, and combine high-throughput screening technology to obtain strains with enhanced target performance. Theoretically, there are many transcriptional elements that can be modified. RNA polymerase (RNAP) was originally selected as the target of mutation modification. As we all know, gene transcription is the process of synthesizing mRNA using DNA as a template under the extension of RNAP. RNAP is a key element in the transcription process. Mutations to RNAP may cause fluctuations in the transcription levels of many genes on a global scale. As a result, a global transcriptional rearrangement of the genome occurs, and the cell phenotype of interest is screened. In addition, there are many other transcriptional elements that can be used as transformation targets, such as cAMP receptor protein, H-NS, Hha, and β subunits [58–60]. gTME is universally applicable to various Gram-positive bacteria, negative bacteria, yeasts, and other eukaryotic microorganisms, which is an important new technology of reverse metabolic engineering. It provides new ideas and methods for the modification of strains in industrial microbial breeding, and also makes it possible to quickly and efficiently obtain a globally optimal phenotype with stable genotype.

3. Artificial transcription factor engineering: Zinc finger structure is a highly specific DNA binding domain in zinc finger protein, which could recognize the specific sequence of DNA and then regulate the expression of the downstream genes [61]. Therefore, zinc finger structure has been found in various regulatory proteins in cells. Park et al. mutated multiple zinc finger structures, constructed an artificial zinc finger library, and then introduced it into yeast, thereby obtained the yeast mutants with improved resistance to osmotic stress and the fungal drug ketoconazole. In addition, because zinc finger proteins can specifically bind to DNA sequences, researchers have coupled them with transcriptional activation or repression domains to obtain libraries of artificial transcription factors, which can regulate gene expression to varying degrees. Finally, the expression efficiency of recombinant proteins in animal cells and Saccharomyces cerevisiae was successfully improved [62].

4. Ribosome engineering: As a protein synthesis machine, the change of ribosomal structure (including ribosomal proteins and rRNA) and functions will directly affect protein synthesis ability. Besides, ribosome play a key role in the regulation of secondary metabolites of microorganisms. Many antibiotics inhibit protein synthesis mainly by combining with the certain parts of ribosome. Therefore, changes in the resistance of microorganisms to certain antibiotics usually reflect mutations in the ribosome. By using various antibiotics as selection markers, strains with ribosomal mutations can be obtained, and indirect selection of mutant strains with increased secondary metabolite production. Commonly used antibiotics for ribosome engineering include streptomycin (Str), gentamicin (Gen), rifampicin (Rif), and thiostrepton (Tsp) [63–65].

1.3. System biology

With the rapid development of omics technology in recent years, a large amount of genetic information, biological, and physiological data contained in microorganisms have been continuously discovered [63]. How to use bioinformatics and computer science technology to simulate and integrate these data into a complete system to reveal the complex metabolic pathways, networks and regulatory pathways in microorganisms, and finally to apply these information to the construction of strains and optimization of fermentation process is the main research content of systems biology [66–68]. Using systems biology technology, analysis of strains’ genome, transcriptome, proteome, and metabolome changes under different environments can systematically reveal the response of intracellular metabolism and regulatory networks. This provides theoretical guidance for the rational design and global optimization of the metabolic networks of strain.

1.3.1. The development of system biology

Modern biological research is mainly based on molecular biology and cell biology. The typical reductionism method is used in the research. So far, reductionism has made a lot of achievements, and has a very specific understanding of organisms at the cellular or even molecular level, but it is difficult to give a systematic and satisfactory explanation of the overall behavior of organisms. Biological science is still in the stage of experimental science, and has not formed a complete set of theories to describe how the organism can realize its function and behavior as a whole. Although it is very important to study the individual genes and proteins of a complex biological system, and it will be the basis of our system biology, but these alone cannot fully reveal all the information of a biological system. The results of these studies are only limited to explain the micro or local phenomenon of biological system, and cannot explain the integrated function of the system as a whole, cannot fully reveal the information of a biological system. Besides, it ignores the interaction, support, integration and other functions of all levels of the system, which limits the development of biological research. In this situation, after the completion of the human genome project at the end of the 20th century, scientists in the field of biology are considering a question: What is the future direction of biological research? The success of the genome project has given us insight into the genetic makeup of all model organisms including E. coli, yeast, nematodes, fruit flies, mice, and humans [69–71]. The development of proteomics has given us a deeper understanding of the composition and interactions of all proteins in biological systems [72,73]. The high-throughput methods in genomics and proteomics have provided a large amount of data for the development of systems biology. Computational biology has become an indispensable and powerful tool for the development of systems biology through data processing, model construction, and theoretical analysis.

A living body is a complex system composed of a large number of elements with different structures and functions, and the complex functions and behaviors are generated by the selective and non-linear interaction of these elements. The complexity of organisms and the non-linear dynamic characteristics of a large number of processes require the establishment of a multi-layered omics technology platform to study and identify all molecules in the body and their functions and interactions. The important task of systems biology is to obtain as much information as possible and integrate them. The multidisciplinary integration of engineering and computers in the field of biology has greatly promoted the wide application of experimental technology in the study of the characteristics and functions of gene products (particularly proteins). High-throughput methods in genomics and proteomics provide a wealth of data for the development of systems biology. The development of biology will mainly face the following issues in the future: (1) how to figure out a single biological reaction network, including the relationship between reaction molecules and the reaction mode. (2) how to study the relationship between biological reaction networks, including quantifying biological. (3) How to use computer information and biological engineering technology to reconstruct the biological reaction, biological reaction network, and even organism.

1.3.2. The definition and research content of system biology

In 2004, Hood pointed out the concept of systems biology [74]: systems biology is the research of the composition of all components (genes, mRNAs, proteins, etc.) in a biological system, and the relationship between these components under specific conditions. Then, computational biology is used to build a mathematical model to quantitatively describe and predict the biological functions, phenotypes, and behaviors of organism. Systems biology is a new field of biology, the purpose of which is to understand organisms at the system level, and mainly solves the following problems: (1) Exposition of system structure. (2) Analysis of system behavior. (3) Method of system control. (4) How to design the system.

The primary task of system biology is to describe the state and structure of the system, including the definition of the elements of the system and the environment in which the system is located, as well as the in-depth analysis of the interaction between the elements of the system and the interaction between the environment and the system. For example, the quantity relationship between the reaction components, the spatial position, and the causal relationship between the reaction components, especially the feedback regulation, variable control, and other issues related to the whole reaction system. Second, the evolution of the system should be analyzed dynamically, including the steady-state characteristics, bifurcation behavior, phase diagram, and so on. Mastering the basic evolution mechanism of the system, making the system objective and operable, and making it evolve in the direction we expect, will also help us rebuild or repair