Reconstruction of methanol and formate metabolic pathway in non-native host for biosynthesis of chemicals and biofuels

One-carbon feedstock such as methanol and formate has attracted much attention as carbon substrate of industrial biotechnology for production of value-added chemicals and biofuels. Productivity improvement of natural one-carbon metabolic pathways in native hosts such as methanotrophs is somewhat difficult due to inefficient genetic tools and low specific growth rate. As an alternative, metabolic engineering can create new and efficient metabolic pathways of one-carbon substrate that can be readily transferred to non-native hosts. In this paper, recent progresses in protein and metabolic engineering for creation of methanol and formate-utilizing synthetic pathways based on RuMP cycle and formolase are reviewed. Perspectives on one-carbon metabolic pathway engineering in non-native host are also discussed.     

Systems biology based metabolic engineering for non-natural chemicals

Production of chemicals in microorganisms is no longer restricted to products arising from native metabolic potential. In this review, we highlight the evolution of metabolic engineering studies, from the production of natural chemicals fermented from biomass hydrolysates, to the engineering of microorganisms for the production of non-natural chemicals. Advances in synthetic biology are accelerating the successful development of microbial cell factories to directly produce value-added chemicals. Here we outline the emergence of novel computational tools for the creation of synthetic pathways, for designing artificial enzymes for non-natural reactions and for re-wiring host metabolism to increase the metabolic flux to products. We also highlight exciting opportunities for applying directed evolution of enzymes, dynamic control of growth and production, growth-coupling strategies as well as decoupled strategies based on orthogonal pathways in the context of non-natural chemicals.     

Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization

The advent of genome-scale models of metabolism has laid the foundation for the development of computational procedures for suggesting genetic manipulations that lead to overproduction. In this work, the computational OptKnock framework is introduced for suggesting gene deletion strategies leading to the overproduction of chemicals or biochemicals in E. coli. This is accomplished by ensuring that a drain towards growth resources (i.e., carbon, redox potential, and energy) must be accompanied, due to stoichiometry, by the production of a desired product. Computational results for gene deletions for succinate, lactate, and 1,3-propanediol (PDO) production are in good agreement with mutant strains published in the literature. While some of the suggested deletion strategies are straightforward and involve eliminating competing reaction pathways, many others suggest complex and nonintuitive mechanisms of compensating for the removed functionalities. Finally, the OptKnock procedure, by coupling biomass formation with chemical production, hints at a growth selection/adaptation system for indirectly evolving overproducing mutants.     

Strategies for systems-level metabolic engineering

Bio-based production of chemicals, fuels and materials is becoming more and more important due to the increasing environmental problems and sharply increasing oil price. To make these biobased processes economically competitive, the biotechnology industry explores new ways to improve the performance of microbial strains in fermentation processes. In contrast to the random mutagenesis and/or intuitive local metabolic engineering practiced in the past, we are now moving towards global-scale metabolic engineering, aided by various experimental and computational tools. This has recently led to some remarkable achievements for the overproduction of valueadded products. In this review, we highlight several relevant gene manipulation tools and computational tools using genome-scale stoichiometric models, and provide useful strategies for successful metabolic engineering along with selected exemplary studies.     

An integrated computational and experimental study for overproducing fatty acids in Escherichia coli

Increasing demands for petroleum have stimulated sustainable ways to produce chemicals and biofuels. Specifically, fatty acids of varying chain lengths (C₆–C₁₆) naturally synthesized in many organisms are promising starting points for the catalytic production of industrial chemicals and diesel-like biofuels. However, bio-production of fatty acids from plants and other microbial production hosts relies heavily on manipulating tightly regulated fatty acid biosynthetic pathways. In addition, precursors for fatty acids are used along other central metabolic pathways for the production of amino acids and biomass, which further complicates the engineering of microbial hosts for higher yields. Here, we demonstrate an iterative metabolic engineering effort that integrates computationally driven predictions and metabolic flux analysis techniques to meet this challenge. The OptForce procedure was used for suggesting and prioritizing genetic manipulations that overproduce fatty acids of different chain lengths from C₆ to C₁₆ starting with wild-type E. coli. We identified some common but mostly chain-specific genetic interventions alluding to the possibility of fine-tuning overproduction for specific fatty acid chain lengths. In accordance with the OptForce prioritization of interventions, fabZ and acyl-ACP thioesterase were upregulated and fadD was deleted to arrive at a strain that produces 1.70 g/L and 0.14 g fatty acid/g glucose (～39% maximum theoretical yield) of C_14–16 fatty acids in minimal M9 medium. These results highlight the benefit of using computational strain design and flux analysis tools in the design of recombinant strains of E. coli to produce free fatty acids.     

Systems metabolic engineering of microorganisms for natural and non-natural chemicals

Growing concerns over limited fossil resources and associated environmental problems are motivating the development of sustainable processes for the production of chemicals, fuels and materials from renewable resources. Metabolic engineering is a key enabling technology for transforming microorganisms into efficient cell factories for these compounds. Systems metabolic engineering, which incorporates the concepts and techniques of systems biology, synthetic biology and evolutionary engineering at the systems level, offers a conceptual and technological framework to speed the creation of new metabolic enzymes and pathways or the modification of existing pathways for the optimal production of desired products. Here we discuss the general strategies of systems metabolic engineering and examples of its application and offer insights as to when and how each of the different strategies should be used. Finally, we highlight the limitations and challenges to be overcome for the systems metabolic engineering of microorganisms at more advanced levels.     

Microbial pathway engineering for industrial processes: evolution, combinatorial biosynthesis and rational design

Microbial pathway engineering has made significant progress in multiple areas. Many examples of successful pathway engineering for specialty and fine chemicals have been reported in the past two years. Novel carotenoids and polyketides have been synthesized using molecular evolution and combinatorial strategies. In addition, rational design approaches based on metabolic control have been reported to increase metabolic flux to specific products. Experimental and computational tools have been developed to aid in design, reconstruction and analysis of non-native pathways. It is expected that a hybrid of evolutionary, combinatorial and rational design approaches will yield significant advances in the near future.     

Microbial 1-butanol production: Identification of non-native production routes and in silico engineering interventions

The potential of engineering microorganisms with non-native pathways for the synthesis of long-chain alcohols has been identified as a promising route to biofuels. We describe computationally derived predictions for assembling pathways for the production of biofuel candidate molecules and subsequent metabolic engineering modifications that optimize product yield. A graph-based algorithm illustrates that, by culling information from BRENDA and KEGG databases, all possible pathways that link the target product with metabolites present in the production host are identified. Subsequently, we apply our recent OptForce procedure to pinpoint reaction modifications that force the imposed product yield in Escherichia coli. We demonstrate this procedure by suggesting new pathways and genetic interventions for the overproduction of 1-butanol using the metabolic model for Escherichia coli. The graph-based search method recapitulates all recent discoveries based on the 2-ketovaline intermediate and hydroxybutyryl-CoA but also pinpointes one novel pathway through thiobutanoate intermediate that to the best of our knowledge has not been explored before.     

Microbial production of lactate-containing polyesters

Due to our increasing concerns on environmental problems and limited fossil resources, biobased production of chemicals and materials through biorefinery has been attracting much attention. Optimization of the metabolic performance of microorganisms, the key biocatalysts for the efficient production of the desired target bioproducts, has been achieved by metabolic engineering. Metabolic engineering allowed more efficient production of polyhydroxyalkanoates, a family of microbial polyesters. More recently, non-natural polyesters containing lactate as a monomer have also been produced by one-step fermentation of engineered bacteria. Systems metabolic engineering integrating traditional metabolic engineering with systems biology, synthetic biology, protein/enzyme engineering through directed evolution and structural design, and evolutionary engineering, enabled microorganisms to efficiently produce natural and non-natural products. Here, we review the strategies for the metabolic engineering of microorganisms for the in vivo biosynthesis of lactate-containing polyesters and for the optimization of whole cell metabolism to efficiently produce lactate-containing polyesters. Also, major problems to be solved to further enhance the production of lactate-containing polyesters are discussed.     

OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions

Computational procedures for predicting metabolic interventions leading to the overproduction of biochemicals in microbial strains are widely in use. However, these methods rely on surrogate biological objectives (e.g., maximize growth rate or minimize metabolic adjustments) and do not make use of flux measurements often available for the wild-type strain. In this work, we introduce the OptForce procedure that identifies all possible engineering interventions by classifying reactions in the metabolic model depending upon whether their flux values must increase, decrease or become equal to zero to meet a pre-specified overproduction target. We hierarchically apply this classification rule for pairs, triples, quadruples, etc. of reactions. This leads to the identification of a sufficient and non-redundant set of fluxes that must change (i.e., MUST set) to meet a pre-specified overproduction target. Starting with this set we subsequently extract a minimal set of fluxes that must actively be forced through genetic manipulations (i.e., FORCE set) to ensure that all fluxes in the network are consistent with the overproduction objective. We demonstrate our OptForce framework for succinate production in Escherichia coli using the most recent in silico E. coli model, iAF1260. The method not only recapitulates existing engineering strategies but also reveals non-intuitive ones that boost succinate production by performing coordinated changes on pathways distant from the last steps of succinate synthesis.     

Generation of an atlas for commodity chemical production in Escherichia coli and a novel pathway prediction algorithm,GEM-Path

The production of 75% of the current drug molecules and 35% of all chemicals could be achieved through bioprocessing (Arundel and Sawaya, 2009). To accelerate the transition from a petroleum-based chemical industry to a sustainable bio-based industry, systems metabolic engineering has emerged to computationally design metabolic pathways for chemical production. Although algorithms able to provide specific metabolic interventions and heterologous production pathways are available, a systematic analysis for all possible production routes to commodity chemicals in Escherichia coli is lacking. Furthermore, a pathway prediction algorithm that combines direct integration of genome-scale models at each step of the search to reduce the search space does not exist. Previous work (Feist et al., 2010) performed a model-driven evaluation of the growth-coupled production potential for E. coli to produce multiple native compounds from different feedstocks. In this study, we extended this analysis for non-native compounds by using an integrated approach through heterologous pathway integration and growth-coupled metabolite production design. In addition to integration with genome-scale model integration, the GEM-Path algorithm developed in this work also contains a novel approach to address reaction promiscuity. In total, 245 unique synthetic pathways for 20 large volume compounds were predicted. Host metabolism with these synthetic pathways was then analyzed for feasible growth-coupled production and designs could be identified for 1271 of the 6615 conditions evaluated. This study characterizes the potential for E. coli to produce commodity chemicals, and outlines a generic strain design workflow to design production strains.     

Large-scale bi-level strain design approaches and mixed-integer programming solution techniques

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ～10 days to ～5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering.     

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.     

Butanol production from renewable biomass: rediscovery of metabolic pathways and metabolic engineering

Biofuel from renewable biomass is one of the answers to help solve the problems associated with limited fossil resources and climate change. Butanol has superior liquid-fuel characteristics, with similar properties to gasoline, and thus, has the potential to be used as a substitute for gasoline. Clostridia are recognized as a good butanol producers and are employed in the industrial-scale production of solvents. Due to the difficulty of performing genetic manipulations on clostridia, however, strain improvement has been rather slow. Furthermore, complex metabolic characteristics of acidogenesis followed by solventogenesis in this strain have hampered the development of engineered clostridia strains with highly efficient and selective butanol-production capabilities. In recent years, the butanol-producing characteristics in clostridia have been further characterized and alternative pathways discovered. More recently, systems-level metabolic engineering approaches were taken to develop superior strains. Herein, we review recent discoveries of metabolic pathways for butanol production and the metabolic engineering strategies being developed.     

Large‐scale identification of genetic design strategies using local search

In the past decade, computational methods have been shown to be well suited to unraveling the complex web of metabolic reactions in biological systems. Methods based on flux–balance analysis (FBA) and bi‐level optimization have been used to great effect in aiding metabolic engineering. These methods predict the result of genetic manipulations and allow for the best set of manipulations to be found computationally. Bi‐level FBA is, however, limited in applicability because the required computational time and resources scale poorly as the size of the metabolic system and the number of genetic manipulations increase. To overcome these limitations, we have developed Genetic Design through Local Search (GDLS), a scalable, heuristic, algorithmic method that employs an approach based on local search with multiple search paths, which results in effective, low‐complexity search of the space of genetic manipulations. Thus, GDLS is able to find genetic designs with greater in silico production of desired metabolites than can feasibly be found using a globally optimal search and performs favorably in comparison with heuristic searches based on evolutionary algorithms and simulated annealing.     

透明质酸的生物合成及其代谢工程的研究进展

透明质酸(hyaluronic acid,HA)是广泛存在于生物体内的功能性糖胺高分子聚合物,在日化、医疗和食品领域应用前景广阔.随着基因工程与代谢工程等合成生物学技术的发展,人们对HA的生物合成过程和机理解析越发深入的同时,也伴随一些新的挑战来临.该综述从分子生物学角度总结了HA的关键合成酶基因及合成途径,对不同来源...     

2013合成生物学专刊序言

合成生物学目前在全球得到迅猛发展。在此专刊中,综述了一些相关技术在合成生物学领域的进展,其中有:链霉菌无痕敲除方法、基因合成技术、DNA组装新方法、最小化基因组的方法及分析、合成生物系统的组合优化。也讨论了应用合成生物学策略优化光合蓝细菌底盘、产溶剂梭菌分子遗传操作技术、蛋白质预算(Protein budget)作为合成生物学的成本标尺。最后,用几个例子说明了合成生物学的应用,包括复杂天然产物合成人工生物系统的设计与构建、微生物木糖代谢途径改造制备生物基化学品以及构建酿酒酵母工程菌合成香紫苏醇。     

Dynamic metabolic control: towards precision engineering of metabolism

Advances in metabolic engineering have led to the synthesis of a wide variety of valuable chemicals in microorganisms. The key to commercializing these processes is the improvement of titer, productivity, yield, and robustness. Traditional approaches to enhancing production use the “push–pull-block” strategy that modulates enzyme expression under static control. However, strains are often optimized for specific laboratory set-up and are sensitive to environmental fluctuations. Exposure to sub-optimal growth conditions during large-scale fermentation often reduces their production capacity. Moreover, static control of engineered pathways may imbalance cofactors or cause the accumulation of toxic intermediates, which imposes burden on the host and results in decreased production. To overcome these problems, the last decade has witnessed the emergence of a new technology that uses synthetic regulation to control heterologous pathways dynamically, in ways akin to regulatory networks found in nature. Here, we review natural metabolic control strategies and recent developments in how they inspire the engineering of dynamically regulated pathways. We further discuss the challenges of designing and engineering dynamic control and highlight how model-based design can provide a powerful formalism to engineer dynamic control circuits, which together with the tools of synthetic biology, can work to enhance microbial production.     

An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems

We introduce a computational framework termed OptReg that determines the optimal reaction activations/inhibitions and eliminations for targeted biochemical production. A reaction is deemed up- or downregulated if it is constrained to assume flux values significantly above or below its steady-state before the genetic manipulations. The developed framework is demonstrated by studying the overproduction of ethanol in Escherichia coli. Computational results reveal the existence of synergism between reaction deletions and modulations implying that the simultaneous application of both types of genetic manipulations yields the most promising results. For example, the downregulation of phosphoglucomutase in conjunction with the deletion of oxygen uptake and pyruvate formate lyase yields 99.8% of the maximum theoretical ethanol yield. Conceptually, the proposed strategies redirect both the carbon flux as well as the cofactors to enhance ethanol production in the network. The OptReg framework is a versatile tool for strain design which allows for a broad array of genetic manipulations.     

Systems metabolic engineering design: Fatty acid production as an emerging case study

Increasing demand for petroleum has stimulated industry to develop sustainable production of chemicals and biofuels using microbial cell factories. Fatty acids of chain lengths from C₆ to C₁₆ are propitious intermediates for the catalytic synthesis of industrial chemicals and diesel‐like biofuels. The abundance of genetic information available for Escherichia coli and specifically, fatty acid metabolism in E. coli, supports this bacterium as a promising host for engineering a biocatalyst for the microbial production of fatty acids. Recent successes rooted in different features of systems metabolic engineering in the strain design of high‐yielding medium chain fatty acid producing E. coli strains provide an emerging case study of design methods for effective strain design. Classical metabolic engineering and synthetic biology approaches enabled different and distinct design paths towards a high‐yielding strain. Here we highlight a rational strain design process in systems biology, an integrated computational and experimental approach for carboxylic acid production, as an alternative method. Additional challenges inherent in achieving an optimal strain for commercialization of medium chain‐length fatty acids will likely require a collection of strategies from systems metabolic engineering. Not only will the continued advancement in systems metabolic engineering result in these highly productive strains more quickly, this knowledge will extend more rapidly the carboxylic acid platform to the microbial production of carboxylic acids with alternate chain‐lengths and functionalities. Biotechnol. Biotechnol. Bioeng. 2014;111: 849–857. © 2014 Wiley Periodicals, Inc.