全基因组范围代谢网络的构建和最小基因组研究

全基因组范围代谢网络（genome-scale metabolic network,GSMN）的构建是合成生物学研究的一个重要研究手段。通过整合各种组学数据和借助计算机进行模拟分析,将基因型与表型的关系进行定量关联,从而为从全局的角度探索和揭示生物代谢机制,进而对生物进行合理的重新设计和工程改造提供了有效的框架。该方法在最小基因组研究中也有着突出的优势,通过计算机辅助的基因组最小化模拟与分析,能够系统鉴定微生物基因组基因的必需性。迄今为止,已有近百个基因组范围的代谢网络发表,覆盖的生物包括原核生物、真核生物和古生生物,并广泛应用于医药、能源、环境、工业和农业等多个领域,展现出了广阔的应用前景。将对全基因组范围代谢网络构建的方法、应用,特别是其在最小基因组研究中的应用作简要的综述。     

大肠杆菌最小基因组分析和删减进展

大肠杆菌是基础研究最透彻、应用广泛的微生物,构建含减小甚至是最小基因组的大肠杆菌将为合成生物学的研究和应用提供理想的底盘生物。介绍了大肠杆菌最小基因组的生长与繁殖必需基因的生物信息学分析和实验鉴定,基因组敲除技术,以及删减基因组的大肠杆菌菌株的构建和应用等方面的研究进展。     

最小基因组研究进展

随着许多生物体全基因组测序的完成,兴起了最小基因组的研究,即一个能营独立生活的生物体最少需要多少个基因。已知最小细胞支原体基因组是研究最小基因组的重要内容,还通过比较多种已测序基因组COG分析最小基因组,目前通过转座子插入基因突主为和同源重组删除基因的分析,进行最小基因组研究。     

大肠杆菌基因组减小研究进展

大肠杆菌是遗传重组领域广泛应用的宿主之一,用于生产重组蛋白、氨基酸和其他化学品。基因组减小可以减少代谢调节网络中的冗余,提高其预测性和可控性。我们介绍了最小基因组的研究策略、应用无痕敲除技术减小大肠杆菌基因组的方法,以及基因组减小后对菌体生长特性、附加体稳定性、重组蛋白表达和代谢的影响。     

基于同源双交换的聚球藻PCC 7942基因组大片段无标记删除

在蓝藻合成生物学中,对大片段进行无标记删除可以加快基因组简化的进程.研究以聚球藻PCC 7942为材料,基于同源重组技术对基因组中大于10 kb的3个非必需区域进行无标记删除.构建带有两侧同源片段的不可在聚球藻复制的质粒,利用接合转移将其导入藻细胞获得同源单交换株,再借助于条件致死基因sacB筛选第二步交换的克隆,获得...     

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。     

微生物基因组简化的研究进展

微生物基因组简化是合成生物学研究热点之一。基因组的适度精简可使细胞代谢途径得以优化,改善细胞对底物、能量的利用效率,大大提高细胞生理性能的预测性和可控性。基因组简化细胞将为生物技术的应用提供理想的底盘细胞。同顾了构建基因组简化细胞的研究策略、研究方法及一些模式生物相关研究进展,总结了基因组简化研究所面临的问题及解决办法,对基因组减小化研究发展趋势前景进行了展望。     

细菌必需基因、最小基因组和合成细胞

单细胞原核生物是原始的细胞生命形式,确定细菌必需基因和最小基因组对理解生命的本质、细胞生命的起源和进化有非常重要的意义。文中简要介绍近年来有关细菌的必需基因、最小基因组和合成细胞的研究方法、理论和进展。还特别介绍人工建立最小细菌基因组的策略以及应用前景。     

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。     

基因组尺度集成细胞网络模型研究进展

细胞网络研究是系统生物学的一个研究热点,通过结合计算机模型和实验技术,从系统角度分析复杂的生物系统,可以为生物实验提供指导和预测。近十年来,国内外许多研究团队致力于基因组规模代谢网络、基因调控网络和信号转导网络模型的构建和分析,并取得了一定成果。而不同类型网络的集成和分析是当前生物网络研究中一个新的方向,并带来了诸多新的挑战。在本文中,主要对基因组尺度集成细胞网络模型的研究进展,特别是对代谢网络和转录网络的集成进行了详细论述,着重于集成网络的构建和分析方法,最后对该领域研究前景进行了展望。     

Robustness analysis of the Escherichia coli metabolic network

Genomic, biochemical, and strain-specific data can be assembled to define an in silico representation of the metabolic network for a select group of single cellular organisms. Flux-balance analysis and phenotypic phase planes derived therefrom have been developed and applied to analyze the metabolic capabilities and characteristics of Escherichia coli K-12. These analyses have shown the existence of seven essential reactions in the central metabolic pathways (glycolysis, pentose phosphate pathway, tricarboxylic acid cycle) for the growth in glucose minimal media. The corresponding seven gene products can be grouped into three categories: (1) pentose phosphate pathway genes, (2) three-carbon glycolytic genes, and (3) tricarboxylic acid cycle genes. Here we develop a procedure that calculates the sensitivity of optimal cellular growth to altered flux levels of these essential gene products. The results indicate that the E. coli metabolic network is robust with respect to the flux levels of these enzymes. The metabolic flux in the transketolase and the tricarboxylic acid cycle reactions can be reduced to 15% and 19%, respectively, of the optimal value without significantly influencing the optimal growth flux. The metabolic network also exhibited robustness with respect to the ribose-5-phosphate isomerase, and the ribose-5-phosephate isomerase flux was reduced to 28% of the optimal value without significantly effecting the optimal growth flux. The metabolic network exhibited limited robustness to the three-carbon glycolytic fluxes both increased and decreased. The development presented another dimension to the use of FBA to study the capabilities of metabolic networks.     

Systematic analysis of conservation relations in Escherichia coli genome-scale metabolic network reveals novel growth media

A biochemical species is called producible in a constraints-based metabolic model if a feasible steady-state flux configuration exists that sustains its nonzero concentration during growth. Extreme semipositive conservation relations (ESCRs) are the simplest semipositive linear combinations of species concentrations that are invariant to all metabolic flux configurations. In this article, we outline a fundamental relationship between the ESCRs of a metabolic network and the producibility of a biochemical species under a nutrient media. We exploit this relationship in an algorithm that systematically enumerates all minimal nutrient sets that render an objective species weakly producible (i.e., producible in the absence of thermodynamic constraints) through a simple traversal of ESCRs. We apply our results to a recent genome scale model of Escherichia coli metabolism, in which we traverse the 51 anhydrous ESCRs of the metabolic network to determine all 928 minimal aqueous nutrient media that render biomass weakly producible. Applying irreversibility constraints, we find 287 of these 928 nutrient sets to be thermodynamically feasible. We also find that an additional 365 of these nutrient sets are thermodynamically feasible in the presence of oxygen. Since biomass producibility is commonly used as a surrogate for growth in genome scale metabolic models, our results represent testable hypotheses of alternate growth media derived from in silico analysis of the E. coli genome scale metabolic network.     

Probing the performance limits of the Escherichia coli metabolic network subject to gene additions or deletions.

An optimization-based procedure for studying the response of metabolic networks after gene knockouts or additions is introduced and applied to a linear flux balance analysis (FBA) Escherichia coli model. Both the gene addition problem of optimally selecting which foreign genes to recombine into E. coli, as well as the gene deletion problem of removing a given number of existing ones, are formulated as mixed-integer optimization problems using binary 0-1 variables. The developed modeling and optimization framework is tested by investigating the effect of gene deletions on biomass production and addressing the maximum theoretical production of the 20 amino acids for aerobic growth on glucose and acetate substrates. In the gene deletion study, the smallest gene set necessary to achieve maximum biomass production in E. coli is determined for aerobic growth on glucose. The subsequent gene knockout analysis indicates that biomass production decreases monotonically, rendering the metabolic network incapable of growth after only 18 gene deletions. In the gene addition study, the E. coli flux balance model is augmented with 3,400 non-E. coli reactions from the KEGG database to form a multispecies model. This model is referred to as the Universal model. This study reveals that the maximum theoretical production of six amino acids could be improved by the addition of only one or two genes to the native amino acid production pathway of E. coli, even though the model could choose from 3,400 foreign reaction candidates. Specifically, manipulation of the arginine production pathway showed the most promise with 8.75% and 9.05% predicted increases with the addition of genes for growth on glucose and acetate, respectively. The mechanism of all suggested enhancements is either by: 1) improving the energy efficiency and/or 2) increasing the carbon conversion efficiency of the production route.     

Characterizing the metabolic phenotype: a phenotype phase plane analysis.

Genome-scale metabolic maps can be reconstructed from annotated genome sequence data, biochemical literature, bioinformatic analysis, and strain-specific information. Flux-balance analysis has been useful for qualitative and quantitative analysis of metabolic reconstructions. In the past, FBA has typically been performed in one growth condition at a time, thus giving a limited view of the metabolic capabilities of a metabolic network. We have broadened the use of FBA to map the optimal metabolic flux distribution onto a single plane, which is defined by the availability of two key substrates. A finite number of qualitatively distinct patterns of metabolic pathway utilization were identified in this plane, dividing it into discrete phases. The characteristics of these distinct phases are interpreted using ratios of shadow prices in the form of isoclines. The isoclines can be used to classify the state of the metabolic network. This methodology gives rise to a "phase plane" analysis of the metabolic genotype-phenotype relation relevant for a range of growth conditions. Phenotype phase planes (PhPPs) were generated for Escherichia coli growth on two carbon sources (acetate and glucose) at all levels of oxygenation, and the resulting optimal metabolic phenotypes were studied. Supplementary information can be downloaded from our website (http://epicurus.che.udel.edu).     

Identification of genome-scale metabolic network models using experimentally measured flux profiles

Genome-scale metabolic network models can be reconstructed for well-characterized organisms using genomic annotation and literature information. However, there are many instances in which model predictions of metabolic fluxes are not entirely consistent with experimental data, indicating that the reactions in the model do not match the active reactions in the in vivo system. We introduce a method for determining the active reactions in a genome-scale metabolic network based on a limited number of experimentally measured fluxes. This method, called optimal metabolic network identification (OMNI), allows efficient identification of the set of reactions that results in the best agreement between in silico predicted and experimentally measured flux distributions. We applied the method to intracellular flux data for evolved Escherichia coli mutant strains with lower than predicted growth rates in order to identify reactions that act as flux bottlenecks in these strains. The expression of the genes corresponding to these bottleneck reactions was often found to be downregulated in the evolved strains relative to the wild-type strain. We also demonstrate the ability of the OMNI method to diagnose problems in E. coli strains engineered for metabolite overproduction that have not reached their predicted production potential. The OMNI method applied to flux data for evolved strains can be used to provide insights into mechanisms that limit the ability of microbial strains to evolve towards their predicted optimal growth phenotypes. When applied to industrial production strains, the OMNI method can also be used to suggest metabolic engineering strategies to improve byproduct secretion. In addition to these applications, the method should prove to be useful in general for reconstructing metabolic networks of ill-characterized microbial organisms based on limited amounts of experimental data.     

Structural analysis of metabolic networks based on flux centrality

Metabolic reactions are fundamental to living organisms, and a large number of reactions simultaneously occur at a given time in living cells transforming diverse metabolites into each other. There has been an ongoing debate on how to classify metabolites with respect to their importance for metabolic performance, usually based on the analysis of topological properties of genome scale metabolic networks. However, none of these studies have accounted quantitatively for flux in metabolic networks, thus lacking an important component of a cell’s biochemistry.We therefore analyzed a genome scale metabolic network of Escherichia coli by comparing growth under 19 different growth conditions, using flux balance analysis and weighted network centrality investigation. With this novel concept of flux centrality we generated metabolite rankings for each particular growth condition. In contrast to the results of conventional analysis of genome scale metabolic networks, different metabolites were top-ranking dependent on the growth condition. At the same time, several metabolites were consistently among the high ranking ones. Those are associated with pathways that have been described by biochemists as the most central part of metabolism, such as glycolysis, tricarboxylic acid cycle and pentose phosphate pathway. The values for the average path length of the analyzed metabolite networks were between 10.5 and 12.6, supporting recent findings that the metabolic network of E. coli is not a small-world network.     

Minimal reaction sets for Escherichia coli metabolism under different growth requirements and uptake environments

A computational procedure for identifying the minimal set of metabolic reactions capable of supporting various growth rates on different substrates is introduced and applied to a flux balance model of the Escherichia coli metabolic network. This task is posed mathematically as a generalized network optimization problem. The minimal reaction sets capable of supporting specified growth rates are determined for two different uptake conditions: (i) limiting the uptake of organic material to a single organic component (e.g., glucose or acetate) and (ii) allowing the importation of any metabolite with available cellular transport reactions. We find that minimal reaction network sets are highly dependent on the uptake environment and the growth requirements imposed on the network. Specifically, we predict that the E. coli network, as described by the flux balance model, requires 224 metabolic reactions to support growth on a glucose-only medium and 229 for an acetate-only medium, while only 122 reactions enable growth on a specially engineered growth medium.     

Elucidating and reprogramming Escherichia coli metabolisms for obligate anaerobic n-butanol and isobutanol production

Elementary mode (EM) analysis based on the constraint-based metabolic network modeling was applied to elucidate and compare complex fermentative metabolisms of Escherichia coli for obligate anaerobic production of n-butanol and isobutanol. The result shows that the n-butanol fermentative metabolism was NADH-deficient, while the isobutanol fermentative metabolism was NADH redundant. E. coli could grow and produce n-butanol anaerobically as the sole fermentative product but not achieve the maximum theoretical n-butanol yield. In contrast, for the isobutanol fermentative metabolism, E. coli was required to couple with either ethanol- or succinate-producing pathway to recycle NADH. To overcome these "defective" metabolisms, EM analysis was implemented to reprogram the native fermentative metabolism of E. coli for optimized anaerobic production of n-butanol and isobutanol through multiple gene deletion (~8-9 genes), addition (~6-7 genes), up- and downexpression (~6-7 genes), and cofactor engineering (e.g., NADH, NADPH). The designed strains were forced to couple both growth and anaerobic production of n-butanol and isobutanol, which is a useful characteristic to enhance biofuel production and tolerance through metabolic pathway evolution. Even though the n-butanol and isobutanol fermentative metabolisms were quite different, the designed strains could be engineered to have identical metabolic flux distribution in "core" metabolic pathways mainly supporting cell growth and maintenance. Finally, the model prediction in elucidating and reprogramming the native fermentative metabolism of E. coli for obligate anaerobic production of n-butanol and isobutanol was validated with published experimental data.     

Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli

The identification of genetic targets that are effective in bringing about a desired phenotype change is still an open problem. While random gene knockouts have yielded improved strains in certain cases, it is also important to seek the guidance of cell-wide stoichiometric constraints in identifying promising gene knockout targets. To investigate these issues, we undertook a genome-wide stoichiometric flux balance analysis as an aid in discovering putative genes impacting network properties and cellular phenotype. Specifically, we calculated metabolic fluxes such as to optimize growth and then scanned the genome for single and multiple gene knockouts that yield improved product yield while maintaining acceptable overall growth rate. For the particular case of lycopene biosynthesis in Escherichia coli, we identified such targets that we subsequently tested experimentally by constructing the corresponding single, double and triple gene knockouts. While such strains are suggested (by the stoichiometric calculations) to increase precursor availability, this beneficial effect may be further impacted by kinetic and regulatory effects not captured by the stoichiometric model. For the case of lycopene biosynthesis, the so identified knockout targets yielded a triple knockout construct that exhibited a nearly 40% increase over an engineered, high producing parental strain.