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Constructing efficient cell factories requires the rational design of metabolic pathways, yet quantitatively predicting the potential pathway for breaking stoichiometric yield limit in hosts remains challenging. This leaves it uncertain whether the pathway yield of various products can be enhanced to surpass the stoichiometric yield limit and whether common strategies exist. Here, a high-quality cross-species metabolic network model (CSMN) and a quantitative heterologous pathway design algorithm (QHEPath) are developed to address this challenge. Through systematic calculations using CSMN and QHEPath, 12,000 biosynthetic scenarios are evaluated across 300 products and 4 substrates in 5 industrial organisms, revealing that over 70% of product pathway yields can be improved by introducing appropriate heterologous reactions. Thirteen engineering strategies, categorized as carbon-conserving and energy-conserving, are identified, with 5 strategies effective for over 100 products. A user-friendly web server is developed to quantitatively calculate and visualize the product yields and pathways, which successfully predicts biologically plausible strategies validated in literature for multiple products., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Vanilla flavour is widely used in various industries and is the most broadly used flavouring agent in the food industry. The demand for this flavour is, therefore, extremely high, yet vanilla bean extracts can only meet about 1% of the overall demand. Vanillin, the main constituent of vanilla flavour, can easily be obtained through chemical synthesis. Nonetheless, consumer demands for natural products and environmentally friendly industrial processes drive the development of biotechnological approaches for its production. Some microorganisms can naturally produce vanillin when fed with various substrates, including eugenol, isoeugenol, and ferulic acid. The characterisation of the genes and enzymes involved in these bioconversion pathways, as well as progress in the understanding of vanillin biosynthesis in Vanilla orchids, allowed the development of genetic engineering and synthetic biology approaches to increase vanillin production in naturally vanillin-producing microorganisms, or to implement novel vanillin biosynthetic pathways in microbial chassis. This review summarises and discusses these genetic engineering and synthetic biology approaches for the microbial production of vanillin.

l-Homoserine is a promising C4 platform compound used in the agricultural, cosmetic, and pharmaceutical industries. Numerous works have been conducted to engineer Escherichia coli to be an excellent l-homoserine producer, but it is still unable to meet the industrial-scale demand. Herein, we successfully engineered a plasmid-free and noninducible E. coli strain with highly efficient l-homoserine production through balancing AspC and AspA synthesis pathways. First, an initial strain was constructed by increasing the accumulation of the precursor oxaloacetate and attenuating the organic acid synthesis pathway. To remodel the carbon flux toward l-aspartate, a balanced route prone to high yield based on TCA intensity regulation was designed. Subsequently, the main synthetic pathway and the cofactor system were strengthened to reinforce the l-homoserine synthesis. Ultimately, under two-stage DO control, strain HSY43 showed 125.07 g/L l-homoserine production in a 5 L fermenter in 60 h, with a yield of 0.62 g/g glucose and a productivity of 2.08 g/L/h. The titer, yield, and productivity surpassed the highest reported levels for plasmid-free strains in the literature. The strategies adopted in this study can be applied to the production of other l-aspartate family amino acids.

Developing efficient bioprocesses requires selecting the best biosynthetic pathways, which can be challenging and time-consuming due to the vast amount of data available in databases and literature. The extension of the shikimate pathway for the biosynthesis of commercially attractive molecules often involves promiscuous enzymes or lacks well-established routes. To address these challenges, we developed a computational workflow integrating enumeration/retrosynthesis algorithms, a toolbox for pathway analysis, enzyme selection tools, and a gene discovery pipeline, supported by manual curation and literature review. Our focus has been on implementing biosynthetic pathways for tyrosine-derived compounds, specifically L-3,4-dihydroxyphenylalanine (L-DOPA) and dopamine, with significant applications in health and nutrition. We selected one pathway to produce L-DOPA and two different pathways for dopamine-one already described in the literature and a novel pathway. Our goal was either to identify the most suitable gene candidates for expression in Escherichia coli for the known pathways or to discover innovative pathways. Although not all implemented pathways resulted in the accumulation of target compounds, in our shake-flask experiments we achieved a maximum L-DOPA titer of 0.71 g/L and dopamine titers of 0.29 and 0.21 g/L for known and novel pathways, respectively. In the case of L-DOPA, we utilized, for the first time, a mutant version of tyrosinase from Ralstonia solanacearum . Production of dopamine via the known biosynthesis route was accomplished by coupling the L-DOPA pathway with the expression of DOPA decarboxylase from Pseudomonas putida , resulting in a unique biosynthetic pathway never reported in literature before. In the context of the novel pathway, dopamine was produced using tyramine as the intermediate compound. To achieve this, tyrosine was initially converted into tyramine by expressing TDC from Levilactobacillus brevis , which, in turn, was converted into dopamine through the action of the enzyme encoded by ppoMP from Mucuna pruriens . This marks the first time that an alternative biosynthetic pathway for dopamine has been validated in microbes. These findings underscore the effectiveness of our computational workflow in facilitating pathway enumeration and selection, offering the potential to uncover novel biosynthetic routes, thus paving the way for other target compounds of biotechnological interest., Competing Interests: Authors PV, PM, and RC were employed by company SilicoLife. Some of the authors involved in this manuscript are also inventors on a related patent application concerning the novel biosynthetic pathway disclosed here for dopamine production. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Ferreira, Balola, Sveshnikova, Hatzimanikatis, Vilaça, Maia, Carreira, Stoney, Carbonell, Souza, Correia, Lousa, Soares and Rocha.)

The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures that arise from intermediate accumulation and reduced flux resulting from competing pathways within the host cell. Moreover, the conventional trial and error methods utilized in pathway optimization struggle to fully grasp the intricacies of installed pathways, leading to time-consuming and labor-intensive experiments, ultimately resulting in suboptimal yields. Considering these obstacles, there is a pressing need to explore the enzyme expression landscape and identify the optimal pathway configuration for enhanced production of molecules. This review delves into recent advancements in pathway engineering, with a focus on multiplex experimentation and machine learning techniques. These approaches play a pivotal role in overcoming the limitations of traditional methods, enabling exploration of a broader design space and increasing the likelihood of discovering optimal pathway configurations for enhanced production of molecules. We discuss several tools and strategies for pathway design, construction, and optimization for sustainable and cost-effective microbial production of molecules ranging from bulk to fine chemicals. We also highlight major successes in academia and industry through compelling case studies., (Copyright © 2023 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

As an alternative to petrochemical synthesis, well-established industrial microbes, such as Escherichia coli , are employed to produce a wide range of chemicals, including dicarboxylic acids (DCAs), which have significant potential in diverse areas including biodegradable polymers. The demand for biodegradable polymers has been steadily rising, prompting the development of efficient production pathways on four- (C4) and five-carbon (C5) DCAs derived from central carbon metabolism to meet the increased demand via the biosynthesis. In this context, E. coli is utilized to produce these DCAs through various metabolic engineering strategies, including the design or selection of metabolic pathways, pathway optimization, and enhancement of catalytic activity. This review aims to highlight the recent advancements in metabolic engineering techniques for the production of C4 and C5 DCAs in E. coli .

Preparation of expression vectors using conventional cloning strategies is laborious and not suitable for the design of metabolic pathways or enzyme cascades, which usually requires the preparation of a vector library to identify productive clones. Recently, Modular Cloning as a novel cloning technique in synthetic biology has been developed. Modular Cloning relies on Golden Gate assembly and supports preparation of individual expression vectors in one-step and one-pot reactions, thus allowing rapid generation of vector libraries. A number of Modular Cloning toolkits for specific applications has been established, providing a collection of distinct genetic elements such as promoters, ribosome binding sites and tags, that can be combined individually in one-step using defined fusion sites. Modular Cloning has been successfully applied to generate various strains for producing value-added compounds. This was achieved by orchestrating complex pathways involving up to 20 enzymes. Due to the novelty of the genetic approach, industrial applications are still rare. In addition, some applications are limited due to the lack of high-throughput screening methods. This shifts the bottleneck from library preparation to screening capacity and needs to be addressed by future developments to pave the path for the establishment of Modular Cloning in industrial applications., (Copyright 2023 Zrinka Raguz Nakic, Christin Peters. License: This work is licensed under a Creative Commons Attribution 4.0 International License.)

In vitro biotransformation (ivBT) refers to the use of an artificial biological reaction system that employs purified enzymes for the one-pot conversion of low-cost materials into biocommodities such as ethanol, organic acids, and amino acids. Unshackled from cell growth and metabolism, ivBT exhibits distinct advantages compared with metabolic engineering, including but not limited to high engineering flexibility, ease of operation, fast reaction rate, high product yields, and good scalability. These characteristics position ivBT as a promising next-generation biomanufacturing platform. Nevertheless, challenges persist in the enhancement of bulk enzyme preparation methods, the acquisition of enzymes with superior catalytic properties, and the development of sophisticated approaches for pathway design and system optimization. In alignment with the workflow of ivBT development, this chapter presents a systematic introduction to pathway design, enzyme mining and engineering, system construction, and system optimization. The chapter also proffers perspectives on ivBT development., (© 2023. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

Various omics technologies are changing Biology into a data-driven science subject. Development of data-driven digital cell models is key for understanding system level organization and evolution principles of life, as well as for predicting cellular function under various environmental/genetic perturbations and subsequently for the design of artificial life. Consequently, the construction, analysis and design of digital cell models have become one of the core supporting technologies in synthetic biology. This paper summarized the research progress on digital cell models in the last ten years after the foundation of Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, with a focus on the development and quality control of genome-scale metabolic network for reliable metabolic pathway design and their application in guiding strain metabolic engineering. We also introduced the latest progress on developing cellular models with multiple constraints to improve prediction accuracy. At last, we briefly discussed the current challenges and future directions in digital cell model development. We believe that digital cell technology, along with genome sequencing, genome synthesis and genome editing, will greatly improve our ability in reading, writing, modifying and creating life.

Non-growth-associated bio-production using microorganisms has the potential to achieve a higher target yield than growth-associated production since the latter approach does not waste the substrate for cell growth. We previously proposed a metabolic pathway engineering method (SSDesign) for non-growth-associated target production based on metabolic flux solution space using elementary mode analysis. SSDesign predicts gene knockout combinations for enforcing cells to produce a target compound under non-growing conditions. For succinate production from glucose in Escherichia coli, gene knockouts of pykA-pykF-sfcA-maeB-zwf and pykA-pykF-sfcA-pntAB-sthA were predicted as candidates. In the present study, to verify the predictions of SSDesign, succinate productivities of these multiple knockout strains were evaluated in the stationary phase under microaerobic conditions. Succinate yields of the BW25113ΔpykAΔpykFΔsfcAΔmaeBΔzwf and BW25113ΔpykAΔpykFΔsfcAΔpntABΔsthA strains were 0.48 and 0.52 mol/mol, respectively, and were higher than that of wild type strain (0.20 mol/mol). The succinate yield of BW25113ΔpykAΔpykFΔsfcAΔpntABΔsthA strain was further improved to 0.66 mol/mol by overexpression of phosphoenolpyruvate carboxylase as a potential bottleneck step in the metabolic pathway., (Copyright © 2022 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.)

Objective: Although various pathway design methods recognize patients as stakeholders, an overview of current practice is lacking. This article describes the results of a literature review assessing patient involvement in clinical cancer pathway development, implementation and evaluation., Methods: A scoping review was conducted following PRISMA-ScR. Two databases were searched to identify studies published in English between 2014 and 2021., Results: Of 12841articles identified 22 articles met the inclusion criteria and reported on one or more of the three phases: development phase (N = 2), implementation (N = 4), evaluation (N = 11), development/evaluation (N = 3), and implementation/evaluation (N = 2) of clinical pathways. The numbers of involved patients ranged from 10 to 793, and the reported methods varied considerably., Conclusion: This review presents a synthesis of methods for involving patients in the clinical pathway lifecycle. No relationship was found between methods and the number of involved patients or between pathway complexity and methods. Although patients are seen as valuable stakeholders in the pathway design, to involve them in practice using the best practice can be improved., Practice Implications: The lack of a clear justification for the choice of methods and number of involved patients calls for further research and framework development to inform pathway developers., (Copyright © 2021. Published by Elsevier B.V.)

It is among the goals in metabolic engineering to construct microbial cell factories producing high-yield and high value-added target products, and an important solution is to design efficient synthetic pathway for the target products. However, due to the difference in metabolic capacity among microbial chassises, the available substrate and the yielded products are limited. Therefore, it is urgent to design related metabolic pathways to improve the production capacity. Existing metabolic engineering approaches to designing heterologous pathways are mainly based on biological experience, which are inefficient. Moreover, the yielded results are in no way comprehensive. However, systems biology provides new methods for heterologous pathway design, particularly the graph-based and constraint-based methods. Based on the databases containing rich metabolism information, they search for and uncover possible metabolic pathways with designated strategy (graph-based method) or algorithm (constraint-based method) and then screen out the optimal pathway to guide the modification of strains. In this paper, we reviewed the databases and algorithms for pathway design, and the applications in metabolic engineering and discussed the strengths and weaknesses of existing algorithms in practical application, hoping to provide a reference for the selection of optimal methods for the design of product synthesis pathway.

The compound 3'-phosphoadenosine-5'-phosphosulfate (PAPS) serves as a sulfate group donor in the production of valuable sulfated compounds. However, elevated costs and low conversion efficiency limit the industrial applicability of PAPS. Here, we designed and constructed an efficient and controllable catalytic system for the conversion of adenosine triphosphate (ATP) (disodium salt) into PAPS without inhibition from by-products. In vitro and in vivo testing in Escherichia coli identified adenosine-5'-phosphosulfate kinase from Penicillium chrysogenum (PcAPSK) as the rate-limiting enzyme. Based on analysis of the catalytic steps and molecular dynamics simulations, a mechanism-guided "ADP expulsion" strategy was developed to generate an improved PcAPSK variant (L7), with a specific activity of 48.94 U·mg -1 and 73.27-fold higher catalytic efficiency (kcat/Km) that of the wild-type enzyme. The improvement was attained chiefly by reducing the ADP-binding affinity of PcAPSK, as well as by changing the enzyme's flexibility and lid structure to a more open conformation. By introducing PcAPSK L7 in an in vivo catalytic system, 73.59 mM (37.32 g·L -1 ) PAPS was produced from 150 mM ATP in 18.5 h using a 3-L bioreactor, and achieved titer is the highest reported to date and corresponds to a 98.13% conversion rate. Then, the PAPS catalytic system was combined with the chondroitin 4-sulfotransferase using a one-pot method. Finally, chondroitin sulfate was transformed from chondroitin at a conversion rate of 98.75%. This strategy has great potential for scale biosynthesis of PAPS and chondroitin sulfate., (© 2021 Wiley Periodicals LLC.)

Engineering biotechnological microorganisms to use methanol as a feedstock for bioproduction is a major goal for the synthetic metabolism community. Here, we aim to redesign the natural serine cycle for implementation in E. coli. We propose the homoserine cycle, relying on two promiscuous formaldehyde aldolase reactions, as a superior pathway design. The homoserine cycle is expected to outperform the serine cycle and its variants with respect to biomass yield, thermodynamic favorability, and integration with host endogenous metabolism. Even as compared to the RuMP cycle, the most efficient naturally occurring methanol assimilation route, the homoserine cycle is expected to support higher yields of a wide array of products. We test the in vivo feasibility of the homoserine cycle by constructing several E. coli gene deletion strains whose growth is coupled to the activity of different pathway segments. Using this approach, we demonstrate that all required promiscuous enzymes are active enough to enable growth of the auxotrophic strains. Our findings thus identify a novel metabolic solution that opens the way to an optimized methylotrophic platform., Competing Interests: Declaration of competing interest A.B.E. is co-founder of b.fab, aiming to commercialize microbial growth of C(1) compounds. b.fab was not involved in this study and did not fund it., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

The use of microbial cell factories to achieve efficient conversion of raw materials and synthesis of target substances is one of the important research directions of synthetic biology. Traditional industrial microorganisms have mainly used sugar-based raw materials as fermentation substrates. How to adopt cheaper carbon resources and realize their efficient use has been widely concerned. Formic acid is an important organic one-carbon source and widely used in industrial manufacturing of pesticides, leather, dyes, medicine and rubber. In recent years, due to the demand fluctuation in downstream industries, formic acid production is facing the dilemma of overcapacity, and therefore, requiring new conversion paths for expansion and extension of the related industrial chain. Biological route is one of the important options. However, natural formate-utilizing microorganisms generally grow slowly when metabolizing formic acid, and moreover, are difficult to be artificially modified by the absence of effective genetic tools. Construction of non-natural formate-utilizing microorganisms is another alternative strategy, but still in its infancy and has a huge space for further improvements. Here, we briefly summarize the recent research progress of biological utilization of formic acid, and also propose the future research focus and direction.

Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are required to explore the chemical biosynthetic space. Here, we propose to explore the bioretrosynthesis space using an artificial intelligence based approach relying on the Monte Carlo Tree Search reinforcement learning method, guided by chemical similarity. We implement this method in RetroPath RL, an open-source and modular command line tool. We validate it on a golden data set of 20 manually curated experimental pathways as well as on a larger data set of 152 successful metabolic engineering projects. Moreover, we provide a novel feature that suggests potential media supplements to complement the enzymatic synthesis plan.

The engineering of synthetic metabolic routes can provide valuable lessons on the roles of different biochemical constraints in shaping pathway activity. In this study, we designed and engineered a novel glycerol assimilation pathway in Escherichia coli. While the synthetic pathway was based only on well-characterized endogenous reactions, we were not able to establish robust growth using standard concentrations of glycerol. Long-term evolution failed to improve growth via the pathway, indicating that this limitation was not regulatory but rather relates to fundamental aspects of cellular metabolism. We show that the activity of the synthetic pathway is fully controlled by three key physicochemical constraints: thermodynamics, kinetics and metabolite toxicity. Overcoming a thermodynamic barrier at the beginning of the pathway requires high glycerol concentrations. A kinetic barrier leads to a Monod-like growth dependency on substrate concentration, but with a very high substrate saturation constant. Finally, the flat thermodynamic profile of the pathway enforces a pseudoequilibrium between glycerol and the reactive intermediate dihydroxyacetone, which inhibits growth when the feedstock concentration surpasses 1000 mm. Overall, this study serves to demonstrate the use of synthetic biology to elucidate key design principles of cellular metabolism., (© 2019 The Authors. The FEBS Journal published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Current pathway synthesis tools identify possible pathways that can be added to a host to produce the desired target molecule through the exploration of abstract metabolic and reaction network space. However, not many of these tools explore gene-level information required to physically realize the identified synthesis pathways, and none explore enzyme-host compatibility. Developing tools that address this disconnect between abstract reactions/metabolic design space and physical genetic sequence design space will enable expedited experimental efforts that avoid exploring unprofitable synthesis pathways. This work describes a workflow, termed Probabilistic Pathway Assembly with Solubility Confidence Scores (ProPASS), which links synthesis pathway construction with the exploration of the physical design space as imposed by the availability of enzymes with predicted characterized activities within the host. Predicted protein solubility propensity scores are used as a confidence level to quantify the compatibility of each pathway enzyme with the host Escherichia coli (E. coli). This study also presents a database, termed Protein Solubility Database (ProSol DB), which provides solubility confidence scores in E. coli for 240,016 characterized enzymes obtained from UniProtKB/Swiss-Prot. The utility of ProPASS is demonstrated by generating genetic implementations of heterologous synthesis pathways in E. coli that target several commercially useful biomolecules., (© 2019 Wiley Periodicals, Inc.)

Single enzyme systems or engineered microbial hosts have been used for decades but the notion of assembling multiple enzymes into cell-free synthetic pathways is a relatively new development. The extensive possibilities that stem from this synthetic concept makes it a fast growing and potentially high impact field for biomanufacturing fine and platform chemicals, pharmaceuticals and biofuels. However, the translation of individual single enzymatic reactions into cell-free multi-enzyme pathways is not trivial. In reality, the kinetics of an enzyme pathway can be very inadequate and the production of multiple enzymes can impose a great burden on the economics of the process. We examine here strategies for designing synthetic pathways and draw attention to the requirements of substrates, enzymes and cofactor regeneration systems for improving the effectiveness and sustainability of cell-free biocatalysis. In addition, we comment on methods for the immobilisation of members of a multi-enzyme pathway to enhance the viability of the system. Finally, we focus on the recent development of integrative tools such as in silico pathway modelling and high throughput flux analysis with the aim of reinforcing their indispensable role in the future of cell-free biocatalytic pathways for biomanufacturing., (Copyright © 2018 Elsevier Inc. All rights reserved.)

The vast diversity of plant natural products is a powerful indication of the biosynthetic capacity of plant metabolism. Synthetic biology seeks to capitalize on this ability by understanding and reconfiguring the biosynthetic pathways that generate this diversity to produce novel products with improved efficiency. Here we review the algorithms and databases that presently support the design and manipulation of metabolic pathways in plants, starting from metabolic models of native biosynthetic pathways, progressing to novel combinations of known reactions, and finally proposing new reactions that may be carried out by existing enzymes. We show how these tools are useful for proposing new pathways as well as identifying side reactions that may affect engineering goals., (Published by Elsevier B.V.)

Increasing availability of new genomes and putative biosynthetic gene clusters (BGCs) has extended the opportunity to access novel chemical diversity for agriculture, medicine, environmental and industrial purposes. However, functional characterization of BGCs through heterologous expression is limited because expression may require complex regulatory mechanisms, specific folding or activation. We developed an integrated workflow for BGC characterization that integrates pathway identification, modular design, DNA synthesis, assembly and characterization. This workflow was applied to characterize multiple phenazine-modifying enzymes. Phenazine pathways are useful for this workflow because all phenazines are derived from a core scaffold for modification by diverse modifying enzymes (PhzM, PhzS, PhzH, and PhzO) that produce characterized compounds. We expressed refactored synthetic modules of previously uncharacterized phenazine BGCs heterologously in Escherichia coli and were able to identify metabolic intermediates they produced, including a previously unidentified metabolite. These results demonstrate how this approach can accelerate functional characterization of BGCs.

Computational pathway design tools often face the challenges of balancing the stoichiometry of co-metabolites and cofactors, and dealing with reaction rule utilization in a single workflow. To this end, we provide an overview of two complementary stoichiometry-based pathway design tools optStoic and novoStoic developed in our group to tackle these challenges. optStoic is designed to determine the stoichiometry of overall conversion first which optimizes a performance criterion (e.g. high carbon/energy efficiency) and ensures a comprehensive search of co-metabolites and cofactors. The procedure then identifies the minimum number of intervening reactions to connect the source and sink metabolites. We also further the pathway design procedure by expanding the search space to include both known and hypothetical reactions, represented by reaction rules, in a new tool termed novoStoic. Reaction rules are derived based on a mixed-integer linear programming (MILP) compatible reaction operator, which allow us to explore natural promiscuous enzymes, engineer candidate enzymes that are not already promiscuous as well as design de novo enzymes. The identified biochemical reaction rules then guide novoStoic to design routes that expand the currently known biotransformation space using a single MILP modeling procedure. We demonstrate the use of the two computational tools in pathway elucidation by designing novel synthetic routes for isobutanol., (© 2018 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.)

With the idea of retrosynthetic analysis, which was raised in the 1960s, chemical synthesis analysis and pathway design have been transformed from a complex problem to a regular process of structural simplification. This review aims to summarize the developments of computer-assisted synthetic analysis and design in recent years, and how machine-learning algorithms contributed to them. LHASA system started the pioneering work of designing semi-empirical reaction modes in computers, with its following rule-based and network-searching work not only expanding the databases, but also building new approaches to indicating reaction rules. Programs like ARChem Route Designer replaced hand-coded reaction modes with automatically-extracted rules, and programs like Chematica changed traditional designing into network searching. Afterward, with the help of machine learning, two-step models which combine reaction rules and statistical methods became the main stream. Recently, fully data-driven learning methods using deep neural networks which even do not require any prior knowledge, were applied into this field. Up to now, however, these methods still cannot replace experienced human organic chemists due to their relatively low accuracies. Future new algorithms with the aid of powerful computational hardware will make this topic promising and with good prospects.

Although most in vitro (cell-free) synthetic biology projects are usually used for the purposes of fundamental research or the formation of high-value products, in vitro synthetic biology platform, which can implement complicated biochemical reactions by the in vitro assembly of numerous enzymes and coenzymes, has been proposed for low-cost biomanufacturing of bioenergy, food, biochemicals, and nutraceuticals. In addition to the most important advantage-high product yield, in vitro synthetic biology platform features several other biomanufacturing advantages, such as fast reaction rate, easy product separation, open process control, broad reaction condition, tolerance to toxic substrates or products, and so on. In this article, we present the basic bottom-up design principles of in vitro synthetic pathway from basic building blocks-BioBricks (thermoenzymes and/or immobilized enzymes) to building modules (e.g., enzyme complexes or multiple enzymes as a module) with specific functions. With development in thermostable building blocks-BioBricks and modules, the in vitro synthetic biology platform would open a new biomanufacturing age for the cost-competitive production of biocommodities.

The microbial metabolic versatility found in nature has inspired scientists to create microorganisms capable of producing value-added compounds. Many endeavors have been made to transfer and/or combine pathways, existing or even engineered enzymes with new function to tractable microorganisms to generate new metabolic routes for drug, biofuel, and specialty chemical production. However, the success of these pathways can be impeded by different complications from an inherent failure of the pathway to cell perturbations. Pursuing ways to overcome these shortcomings, a wide variety of strategies have been developed. This chapter will review the computational algorithms and experimental tools used to design efficient metabolic routes, and construct and optimize biochemical pathways to produce chemicals of high interest.

In this protocol, we describe in silico design methods that can assist in the engineering of production pathways that are based on enzymatic transformations. The described protocols are the basis for automated processes to be integrated into an iterative Design-Build-Test-Learn cycle in synthetic biology for chemical production. Selecting the right enzyme sequence for a desired biocatalytic activity from the extensive catalogue of sequences available in databases is challenging and can dramatically influence the success of bioproducing chemical compounds. A method for enzyme selection is presented that helps identifying candidate enzyme sequences through a scoring approach that considers not only sequence homology but also reaction similarity. Selecting a viable biochemical pathway for compound production requires screening large sets of reactions in a process involving combinatorial complexity. A method for pathway design using retrosynthesis is presented. The protocol allows the discovery of alternative chemical pathways leading to the final product by using reaction rules of selectable degree of specificity. The protocols can be reversed through clustering discovery and product identification processes. The integration of these protocols into a general pipeline provides a toolbox for enhanced automated synthetic biology design and metabolic engineering., (© 2018 Elsevier Inc. All rights reserved.)

C2-C4 diols classically derived from fossil resource are very important bulk chemicals which have been used in a wide range of areas, including solvents, fuels, polymers, cosmetics, and pharmaceuticals. Production of C2-C4 diols from renewable resources has received significant interest in consideration of the reducing fossil resource and the increasing environmental issues. While bioproduction of certain diols like 1,3-propanediol has been commercialized in recent years, biosynthesis of many other important C2-C4 diol isomers is highly challenging due to the lack of natural synthesis pathways. Recent advances in synthetic biology have enabled the de novo design of completely new pathways to non-natural molecules from renewable feedstocks. In this study, we review recent advances in bioproduction of C2-C4 diols, focusing on new metabolic pathways and metabolic engineering strategies being developed. We also discuss the challenges and future trends toward the development of economically competitive processes for bio-based diol production.

Production of chemicals via fermentation has been evolving over the past 30 years in search of economically viable systems. Thus far, there have been few industrially relevant chemicals that have seen commercialization, examples being lactic acid and ethanol. Currently, many of these fermentation processes still compete with food sources. In order to reduce this competition fermentation of alternative feedstocks, such as lignocellulosic biomass must to be utilized. Hemicellulosic sugars can be employed effectively for the production of chemicals by incorporating nonphosphorylative metabolism. This review covers nonphosphorylative metabolism, the pathways and enzymes involved, as well as the products that have been produced using nonphosphorylative metabolism.

Drugs that target specific proteins are a major paradigm in cancer research. In this article, we extend a modeling framework for drug sensitivity prediction and combination therapy design based on drug perturbation experiments. The recently proposed target inhibition map approach can infer stationary pathway models from drug perturbation experiments, but the method is limited to a steady-state snapshot of the underlying dynamical model. We consider the inverse problem of possible dynamic models that can generate the static target inhibition map model. From a deterministic viewpoint, we analyze the inference of Boolean networks that can generate the observed binarized sensitivities under different target inhibition scenarios. From a stochastic perspective, we investigate the generation of Markov chain models that satisfy the observed target inhibition sensitivities.