Dealing with complexity: evolutionary engineering and genome shuffling

Comparative analysis of the growing number of microbial genome sequences has shown a high plasticity of genomes and several mechanisms for the adaptation of microbial cells to changing environmental conditions have been discovered. By contrast, the underlying metabolic networks of microorganisms are under strict control and relatively rigid, which poses a significant challenge for rational metabolic engineering approaches. Recursive shuffling of whole genomes has recently been demonstrated as an effective new evolutionary whole-cell engineering approach for the rapid improvement of industrially important microbial phenotypes.     

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.     

利用代谢工程技术提高工业微生物对胁迫的抗性

代谢工程是工业微生物菌种改造的平台技术,不仅可用于改变微生物细胞内的代谢流向,也可以用于改善工业微生物的生理功能。在工业生产过程中,微生物细胞会面临多种胁迫作用,这些胁迫诱导的基因调节作用,都有可能影响细胞的许多重要生理功能,从而影响生物转化过程的效率。从工业应用的观点出发,选择生产性能良好、对发酵过程中的主要胁迫因素有较强耐受性的菌株至关重要。以下评述了借鉴传统代谢工程技术和反向代谢工程技术来提高工业微生物对胁迫抗性的若干研究策略,提出了该领域目前存在的问题,以及利用代谢工程技术改善微生物胁迫抗性——即微生物生理功能工程的发展方向。     

Computational design of auxotrophy-dependent microbial biosensors for combinatorial metabolic engineering experiments

Combinatorial approaches in metabolic engineering work by generating genetic diversity in a microbial population followed by screening for strains with improved phenotypes. One of the most common goals in this field is the generation of a high rate chemical producing strain. A major hurdle with this approach is that many chemicals do not have easy to recognize attributes, making their screening expensive and time consuming. To address this problem, it was previously suggested to use microbial biosensors to facilitate the detection and quantification of chemicals of interest. Here, we present novel computational methods to: (i) rationally design microbial biosensors for chemicals of interest based on substrate auxotrophy that would enable their high-throughput screening; (ii) predict engineering strategies for coupling the synthesis of a chemical of interest with the production of a proxy metabolite for which high-throughput screening is possible via a designed bio-sensor. The biosensor design method is validated based on known genetic modifications in an array of E. coli strains auxotrophic to various amino-acids. Predicted chemical production rates achievable via the biosensor-based approach are shown to potentially improve upon those predicted by current rational strain design approaches. (A Matlab implementation of the biosensor design method is available via http://www.cs.technion.ac.il/~tomersh/tools).     

Needs for new plant-derived pharmaceuticals in the post-genome era: an industrial view in drug research and development

Plant metabolites have been the successful source of drugs and provided considerable value not only to the pharmaceutical industry but also to human health problems. Although pharmaceutical companies significantly decreased their activities in natural product discovery during the past few decades, various multidisciplinary approaches have been made to create new opportunities for finding innovative plant derived pharmaceuticals in post-genome era. Strategies to integrate the knowledge on medicinal plants into rational drug screening, the unique biodiversity of plant metabolites into random drug screening, and the chemical diversity of plant metabolites into combinatorial chemistry have been reviewed with concrete examples. Innovative biotechnologies in plant cell and tissue cultures, and the latest achievements in metabolic engineering and genetic modification should significantly improve the production sustainability and efficiency of plant-derived pharmaceuticals.     

Evolutionary programming as a platform for <Emphasis Type="Italic">in silico</Emphasis> metabolic engineering

Background  

Through genetic engineering it is possible to introduce targeted genetic changes and hereby engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, owing to the complexity of metabolic networks, both in terms of structure and regulation, it is often difficult to predict the effects of genetic modifications on the resulting phenotype. Recently genome-scale metabolic models have been compiled for several different microorganisms where structural and stoichiometric complexity is inherently accounted for. New algorithms are being developed by using genome-scale metabolic models that enable identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem, and it is therefore interesting to develop new faster algorithms.     

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.     

Engineering of microorganisms for the production of biofuels and perspectives based on systems metabolic engineering approaches

The increasing oil price and environmental concerns caused by the use of fossil fuel have renewed our interest in utilizing biomass as a sustainable resource for the production of biofuel. It is however essential to develop high performance microbes that are capable of producing biofuels with very high efficiency in order to compete with the fossil fuel. Recently, the strategies for developing microbial strains by systems metabolic engineering, which can be considered as metabolic engineering integrated with systems biology and synthetic biology, have been developed. Systems metabolic engineering allows successful development of microbes that are capable of producing several different biofuels including bioethanol, biobutanol, alkane, and biodiesel, and even hydrogen. In this review, the approaches employed to develop efficient biofuel producers by metabolic engineering and systems metabolic engineering approaches are reviewed with relevant example cases. It is expected that systems metabolic engineering will be employed as an essential strategy for the development of microbial strains for industrial applications.     

Differential Bees Flux Balance Analysis with OptKnock for In Silico Microbial Strains Optimization

Microbial strains optimization for the overproduction of desired phenotype has been a popular topic in recent years. The strains can be optimized through several techniques in the field of genetic engineering. Gene knockout is a genetic engineering technique that can engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, the complexities of the metabolic networks have made the process to identify the effects of genetic modification on the desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to the combinatorial problem in obtaining optimal gene deletion strategy. Basically, the size of a genome-scale metabolic model is usually large. As the size of the problem increases, the computation time increases exponentially. In this paper, we propose Differential Bees Flux Balance Analysis (DBFBA) with OptKnock to identify optimal gene knockout strategies for maximizing the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by improving the performance of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by hybridizing Differential Evolution (DE) algorithm into neighborhood searching strategy of BAFBA. In addition, DBFBA is integrated with OptKnock to validate the results for improving the reliability the work. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as the model organisms, DBFBA has shown a better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes compared to the methods used in previous works.     

Reconstruction of Danio rerio Metabolic Model Accounting for Subcellular Compartmentalisation

Plant and microbial metabolic engineering is commonly used in the production of functional foods and quality trait improvement. Computational model-based approaches have been used in this important endeavour. However, to date, fish metabolic models have only been scarcely and partially developed, in marked contrast to their prominent success in metabolic engineering. In this study we present the reconstruction of fully compartmentalised models of the Danio rerio (zebrafish) on a global scale. This reconstruction involves extraction of known biochemical reactions in D. rerio for both primary and secondary metabolism and the implementation of methods for determining subcellular localisation and assignment of enzymes. The reconstructed model (ZebraGEM) is amenable for constraint-based modelling analysis, and accounts for 4,988 genes coding for 2,406 gene-associated reactions and only 418 non-gene-associated reactions. A set of computational validations (i.e., simulations of known metabolic functionalities and experimental data) strongly testifies to the predictive ability of the model. Overall, the reconstructed model is expected to lay down the foundations for computational-based rational design of fish metabolic engineering in aquaculture.     

Application of new metabolic engineering tools for Clostridium acetobutylicum

The renewed interests in clostridial acetone-butanol-ethanol (ABE) fermentation as a next-generation biofuel source led to significantly intensified research in the past few years. This mini-review focuses on the current status of metabolic engineering techniques available for the model organism of ABE fermentation, Clostridium acetobutylicum. A comprehensive survey of various application examples covers two general issues related to both basic and applied research questions: (i) how to improve biofuel production and (ii) what information can be deduced from respective genotype/phenotype manipulations. Recently developed strategies to engineer C. acetobutylicum are summarized including the current portfolio of altered gene expression methodologies, as well as systematic (rational) and explorative (combinatorial) metabolic engineering approaches.     

Genome shuffling: Progress and applications for phenotype improvement

Although rational method and global technique have been successfully applied in strain improvement respectively, the demand for engineering complex phenotypes required combinatorial approach. The technology of genome shuffling has been presented as a novel whole genome engineering approach for the rapid improvement of cellular phenotypes. This approach using recursive protoplast fusion with multi-parental strains offers the advantage of recombination throughout the entire genome without the necessity for genome sequence data or network information. Genome shuffling has been demonstrated as an effective method, which is not only for producing improved strain but also for providing information on complex phenotype. In this review we attempt to present the advantage of genome shuffling, introduce the procedure of this technology, summarize the applications of this approach for phenotype improvement and then give perspective on the development of this method in the future.     

Systems metabolic engineering: Genome-scale models and beyond

The advent of high throughput genome-scale bioinformatics has led to an exponential increase in available cellular system data. Systems metabolic engineering attempts to use data-driven approaches – based on the data collected with high throughput technologies – to identify gene targets and optimize phenotypical properties on a systems level. Current systems metabolic engineering tools are limited for predicting and defining complex phenotypes such as chemical tolerances and other global, multigenic traits. The most pragmatic systems-based tool for metabolic engineering to arise is the in silico genome-scale metabolic reconstruction. This tool has seen wide adoption for modeling cell growth and predicting beneficial gene knockouts, and we examine here how this approach can be expanded for novel organisms. This review will highlight advances of the systems metabolic engineering approach with a focus on de novo development and use of genome-scale metabolic reconstructions for metabolic engineering applications. We will then discuss the challenges and prospects for this emerging field to enable model-based metabolic engineering. Specifically, we argue that current state-of-the-art systems metabolic engineering techniques represent a viable first step for improving product yield that still must be followed by combinatorial techniques or random strain mutagenesis to achieve optimal cellular systems.     

酿酒酵母代谢工程技术

自20世纪90年代初期诞生以来,代谢工程历经了30年的快速发展。作为代谢工程的首选底盘细胞之一,酿酒酵母细胞工厂已被广泛应用于大量大宗化学品和新型高附加值生物活性物质的生物制造,在能源、医药和环境等领域取得了巨大的突破。近年来,合成生物学、生物信息学以及机器学习等相关技术也极大地促进了代谢工程的技术发展和应用。文中回顾了近30年来酿酒酵母代谢工程重要的技术发展,首先总结了经典代谢工程的常用方法和策略,以及在此基础上发展而来的系统代谢工程和合成生物学驱动的代谢工程技术。最后结合最新技术发展趋势,展望了未来酿酒酵母代谢工程发展的新方向。     

Engineering of secondary metabolite pathways

Nature produces an astonishing wealth of secondary metabolites with important biological functions. To access this diversity of structurally complex chemical compounds for industrial and biomedical applications, cells have been engineered to produce higher levels and/or novel compounds that were previously inaccessible. Recent examples of metabolic and combinatorial engineering illustrate different strategies for the production of secondary metabolites in microbial cells.     

Genome-scale metabolic model in guiding metabolic engineering of microbial improvement

In the past few decades, despite all the significant achievements in industrial microbial improvement, the approaches of traditional random mutation and selection as well as the rational metabolic engineering based on the local knowledge cannot meet today’s needs. With rapid reconstructions and accurate in silico simulations, genome-scale metabolic model (GSMM) has become an indispensable tool to study the microbial metabolism and design strain improvements. In this review, we highlight the application of GSMM in guiding microbial improvements focusing on a systematic strategy and its achievements in different industrial fields. This strategy includes a repetitive process with four steps: essential data acquisition, GSMM reconstruction, constraints-based optimizing simulation, and experimental validation, in which the second and third steps are the centerpiece. The achievements presented here belong to different industrial application fields, including food and nutrients, biopharmaceuticals, biopolymers, microbial biofuel, and bioremediation. This strategy and its achievements demonstrate a momentous guidance of GSMM for metabolic engineering breeding of industrial microbes. More efforts are required to extend this kind of study in the meantime.     

超突变细胞在进化工程中的开发与应用

通过进化工程技术改造微生物细胞的生理表型是生物技术和生物炼制领域的重要研究方向,但是现阶段的各种进化工程技术面临效率低或连续性差的问题。超突变细胞能够进行自发、连续的胞内诱变,将其应用于进化工程技术能够实现连续、高效的菌种改造。本文详细介绍了自然界中超突变细胞产生的遗传机制和相应的人工超突变细胞的构建策略,以及应用此类超突变系统在微生物细胞生理性能改造和蛋白质突变体文库高效构建上取得的进展。随着相关领域认识和技术的加强,未来人工超突变细胞的构建将继续向着不同维度上可控性、靶向性不断提高的方向发展,从而为细胞和蛋白的改造提供更强更优的进化动力。     

Comparing methods for metabolic network analysis and an application to metabolic engineering

Bioinformatics tools have facilitated the reconstruction and analysis of cellular metabolism of various organisms based on information encoded in their genomes. Characterization of cellular metabolism is useful to understand the phenotypic capabilities of these organisms. It has been done quantitatively through the analysis of pathway operations. There are several in silico approaches for analyzing metabolic networks, including structural and stoichiometric analysis, metabolic flux analysis, metabolic control analysis, and several kinetic modeling based analyses. They can serve as a virtual laboratory to give insights into basic principles of cellular functions. This article summarizes the progress and advances in software and algorithm development for metabolic network analysis, along with their applications relevant to cellular physiology, and metabolic engineering with an emphasis on microbial strain optimization. Moreover, it provides a detailed comparative analysis of existing approaches under different categories.