A modular modulation method for achieving increases in metabolite production

Increasing the production of overproducing strains represents a great challenge. Here, we develop a modular modulation method to determine the key steps for genetic manipulation to increase metabolite production. The method consists of three steps: (i) modularization of the metabolic network into two modules connected by linking metabolites, (ii) change in the activity of the modules using auxiliary rates producing or consuming the linking metabolites in appropriate proportions and (iii) determination of the key modules and steps to increase production. The mathematical formulation of the method in matrix form shows that it may be applied to metabolic networks of any structure and size, with reactions showing any kind of rate laws. The results are valid for any type of conservation relationships in the metabolite concentrations or interactions between modules. The activity of the module may, in principle, be changed by any large factor. The method may be applied recursively or combined with other methods devised to perform fine searches in smaller regions. In practice, it is implemented by integrating to the producer strain heterologous reactions or synthetic pathways producing or consuming the linking metabolites. The new procedure may contribute to develop metabolic engineering into a more systematic practice. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:656–667, 2015     

基于约束的建模方法在代谢网络中的应用研究

代谢网络在各种细胞功能和生命过程中发挥着至关重要的作用。随着细胞网络重建工程的迅速发展,可用的基因组水平代谢网络越来越多,因而计算方法在这些网络的结构功能分析中越来越重要。基于约束的建模方法不像图论方法那样仅考虑代谢模型的纯拓扑结构,也不像各种动力学建模方法那样需求详尽的热力学参数,因而极具优势。采用基于约束的建模方法对一个含619个基因,655个代谢物和743个代谢反应的金黄色葡萄球菌(Staphylococcusaureus)代谢网络进行了分析,主要研究了该模型的网络结构特征,以及其最优生长率、动态生长情况和基因删除学习等。本研究提供了一个对金黄色葡萄球菌代谢网络进行约束建模分析的初步框架。     

基因组尺度代谢网络研究进展

基因组尺度代谢网络从基因组序列出发,结合基因、蛋白质、代谢数据库和实验数据,从系统的角度定量研究生命体的代谢过程,了解各个组分之间的相互作用关系。这类网络模型对于生命活动理论研究和优良工程菌的构建都具有重要的理论和实践意义。以下结合作者的实际研究经验,对基因组尺度代谢网络从重构到模拟直至应用进行了较为详细的介绍,并讨论了一些目前存在的难题和未来的研究方向。     

Metabolic engineering based on systems biology for chemicals production

Microorganisms have been the main sources for the production of chemicals. Production of chemicals requires the development of low-cost and higher-yield processes. Towards this goal, microbial strains with higher levels of production should be first considered. Metabolic engineering has been used extensively over the past two to three decades to increase production of these chemicals. Advances in omics technology and computational simulation are allowing us to perform metabolic engineering at the systems level. By combining the results of omics analyses and computational simulation, systems biology allows us to understand cellular physiology and characteristics, which can subsequently be used for designing strategies. Here, we review the current status of metabolic engineering based on systems biology for chemical production and discuss future prospects.     

Metabolic engineering based on systems biology for chemicals production

Microorganisms have been the main sources for the production of chemicals. Production of chemicals requires the development of low-cost and higher-yield processes. Towards this goal, microbial strains with higher levels of production should be first considered. Metabolic engineering has been used extensively over the past two to three decades to increase production of these chemicals. Advances in omics technology and computational simulation are allowing us to perform metabolic engineering at the systems level. By combining the results of omics analyses and computational simulation, systems biology allows us to understand cellular physiology and characteristics, which can subsequently be used for designing strategies. Here, we review the current status of metabolic engineering based on systems biology for chemical production and discuss future prospects.     

Optimization of activation conditions of Rhodobacter sphaeroides in hydrogen generation process

AIMS: To examine the effects of the culture age, illuminance intensity and changes in these parameters during activation on hydrogen generation process carried out by purple nonsulfur Rhodobacter sphaeroides bacteria. METHODS AND RESULTS: The following parameters were determined in all experiments: the amount of hydrogen evolved (measured using gas chromatography), biomass increase as dry mass, pH values and consumption of organic substance as chemical oxygen demand (COD). The medium used in the process of activation and hydrogen generation contained malic acid (15 mmol) and sodium glutamate (2 mmol). The optimum age of bacteria was 12-24 h and the best intensity of illuminance was found to be 5 cd sr m-2 on activation and 9 cd sr m-2 on hydrogen generation. These conditions provided hydrogen evolution of 1.39 l l-1 of the medium with the highest specific hydrogen production of 0.146 l H2 l-1 medium h-1 g-1 inoculum. An increase in the illuminance intensity resulted in a slight inhibition of the process. CONCLUSIONS: The activation stage of bacteria has a significant effect on the parameters of hydrogen photogeneration. The optimization of the activation stages allowed a shortening of the time of hydrogen generation and of the period after which hydrogen evolution starts. SIGNIFICANCE AND IMPACT OF THE STUDY: An innovative method of bacteria activation before the initiation of the hydrogen generation process has been used to optimize this process. The shortening of the process duration as well as the twice higher hydrogen yield can help in the designing of other systems (including also those operating under solar irradiation) in which R. sphaeroides bacteria are to be applied.     

Systems approaches to succinic acid-producing microorganisms

Succinic acid is a cellular metabolite belonging to the C4-dicarboxylic acid family, and the fermentative production of succinic acid via the use of recombinant microorganisms has recently become the focus of an increasing amount of attention. Considering the difficulty inherent to the direct application of natural succinic acid producers to the industrial process, a variety of systems biology studies have been conducted regarding the development of enhanced succinic acid production systems. This review shows how the metabolic processes of microorganisms, includingEscherichia coli andMannheimia succiniciproducens, have been optimized in order to achieve enhanced succinic acid production. First, their metabolic networks were constructed on the basis of complete genome sequences, after which their metabolic characteristics were estimated viain silico computer modeling. Metabolic engineering strategies were designed in accordance with the results ofin silico modeling and metabolically engineered versions of bothE. coli andM. succiniciproducens have been constructed. The succinic acid productivity and yield obtained using metabolically engineered bacteria was significantly higher than that obtained using wild-type bacteria.     

Applications of genome‐scale metabolic reconstructions

The availability and utility of genome‐scale metabolic reconstructions have exploded since the first genome‐scale reconstruction was published a decade ago. Reconstructions have now been built for a wide variety of organisms, and have been used toward five major ends: (1) contextualization of high‐throughput data, (2) guidance of metabolic engineering, (3) directing hypothesis‐driven discovery, (4) interrogation of multi‐species relationships, and (5) network property discovery. In this review, we examine the many uses and future directions of genome‐scale metabolic reconstructions, and we highlight trends and opportunities in the field that will make the greatest impact on many fields of biology.     

Three-dimensional chromatin ensemble reconstruction via stochastic embedding

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系统生物学和合成生物学研究在生物燃料生产菌株改造中的应用

基于生物质资源生产环境友好的生物燃料,对经济和社会的可持续发展具有重要意义,但其生产成本高的问题十分突出,而高效生产菌株的获得是解决这一问题的根本出路。以下综述了利用系统生物学研究所获得的信息进行菌种改造的过程,重点论述了生产菌株胁迫耐受性方面的研究进展,并讨论了系统生物学、合成生物学和代谢工程技术在改造生物燃料生产菌株中的应用,展望了合成生物学在构建高效生物能源生产菌株方面应用的前景。     

基于代谢网络预测菌种基因改造靶点方法的研究进展

高产特定产品的人工细胞工厂的构建需要对野生菌株进行大量的基因工程改造,近年来随着大量基因组尺度代谢网络模型的构建,人们提出了多种基于代谢网络分析预测基因改造靶点以使某一目标化合物合成最优的方法。这些方法利用基因组尺度代谢网络模型中的反应计量关系约束和反应不可逆性约束等,通过约束优化的方法预测可使产物合成最大化的改造靶点,避免了传统的通过相关途径的直观分析确定靶点的方法的局限性和主观性,为细胞工厂的理性设计提供了新的思路。以下结合作者的实际研究经验,对这些菌种优化方法的原理、优缺点及适用性等进行详细介绍,并讨论了目前存在的主要问题和未来的研究方向,为人们针对不同目标产品选择合适的方法及预测结果的可靠性评估提供了指导。     

利用合成生物学生产药物和燃料

合成生物学是一个快速发展的研究领域,其重要性体现在科学研究和应用开发两方面。它不但加深了我们对复杂的生物过程与机理的理解,而且使得基础生物研究向实际应用的快速转化成为可能。将介绍一些新型高效的合成生物学工具以及如何利用它们开发能从可再生原料生产药物和燃料的上程菌株。     

代谢工程发展20年

代谢工程从上世纪90年代初期发展至今已有20年历史,对微生物发酵工业的发展起到了极大的推动作用。以下回顾了代谢工程发展至今的三个重要阶段,讨论了各阶段中代谢工程在技术方面的进展及其对微生物发酵工业的促进作用。最后还讨论了代谢工程将来发展中的关键问题及解决策略。     

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。