Noise effect in metabolic networks

Constraint-based models such as flux balance analysis （FBA） are a powerful tool to study biological metabolic networks. Under the hypothesis that cells operate at an optimal growth rate as the result of evolution and natural selection, this model successfully predicts most cellular behaviours in growth rate. However, the model ignores the fact that cells can change their cellular metabolic states during evolution, leaving optimal metabolic states unstable. Here, we consider all the cellular processes that change metabolic states into a single term‘noise＇, and assume that cells change metabolic states by randomly walking in feasible solution space. By simulating a state of a cell randomly walking in the constrained solution space of metabolic networks, we found that in a noisy environment cells in optimal states tend to travel away from these points. On considering the competition between the noise effect and the growth effect in cell evolution, we found that there exists a trade-off between these two effects. As a result, the population of the cells contains different cellular metabolic states, and the population growth rate is at suboptimal states.

Expanded flux variability analysis on metabolic network of Escherichia coli

Flux balance analysis, based on the mass conservation law in a cellular organism, has been extensively employed to study the interplay between structures and functions of cellular metabolic networks. Consequently, the phenotypes of the metabolism can be well elucidated. In this paper, we introduce the Expanded Flux Variability Analysis (EFVA) to characterize the intrinsic nature of metabolic reactions, such as flexibility, modularity and essentiality, by exploring the trend of the range, the maximum and the minimum flux of reactions. We took the metabolic network of Escherichia coli as an example and analyzed the variability of reaction fluxes under different growth rate constraints. The average variabil-ity of all reactions decreases dramatically when the growth rate increases. Consider the noise effect on the metabolic system, we thus argue that the microorganism may practically grow under a suboptimal state. Besides, under the EFVA framework, the reactions are easily to be grouped into catabolic and anabolic groups. And the anabolic groups can be further assigned to specific biomass constitute. We also discovered the growth rate dependent essentiality of reactions.

Progress in the applications of flux analysis of metabolic networks

The metabolic network has become a hot topic in the area of system biology and flux-based analysis plays a very important role in understanding the characteristics of organism metabolic networks. We review mainly the static methods for analyzing metabolic networks such as flux balance analysis (FBA), minimization of metabolic adjustment (MOMA), regulatory on / off minimization (ROOM), and dynamic flux balance analysis with linear quadratic regulator (DFBA-LQR). Then several kinds of commonly used software for flux analysis are introduced briefly and compared with each other. Finally, we highlight the applications of metabolic network flux analysis, especially its usage combined with other biological characteristics and its usage for drug design. The idea of combining the analysis of metabolic networks and other biochemical data has been gradually promoted and used in several aspects such as the combination of metabolic flux and the regulation of gene expression, the influence of protein evolution caused by metabolic flux, the relationship between metabolic flux and the topological characteristics, the optimization of metabolic engineering. More comprehensive and accurate properties of metabolic networks will be obtained by integrating metabolic flux analysis, network topological characteristics and dynamic modeling.

Constrain-based analysis of gene deletion on the metabolic flux redistribution of Saccharomyces Cerevisiae

Based on the gene-protein-reaction (GPR) model of S. cerevisiae_iND750 and the method of constraint-based analysis, we first calculated the metabolic flux distribution of S. cere-visiae_iND750. Then we calculated the deletion impact of 438 calculable genes, one by one, on the metabolic flux redistribution of S. cere-visiae_iND750. Next we analyzed the correlation between v (describing deletion impact of one gene) and d (connection degree of one gene) and the correlation between v and Vgene (flux sum controlled by one gene), and found that both of them were not of linear relation. Furthermore, we sought out 38 important genes that most greatly affected the metabolic flux distribution, and determined their functional subsystems. We also found that many of these key genes were related to many but not several subsystems. Because the in silico model of S. cere-visiae_iND750 has been tested by many ex-periments, thus is credible, we can conclude that the result we obtained has biological sig-nificance.

A novel hyper-cube shrink algorithm to predict metabolic fluxes based on enzyme activity only

OBJECTIVE One of the long-expected goals of genome-scale metabolic modeling is to evaluate the influence of the perturbed enzymes to the yield of an expected end product.METHDOS Metabolic control analysis(MCA)performs such role to calculate the sensitivity of flux change upon that of enzymes under the framework of ordinary differential equation(ODE)models,which are restricted in small-scale networks and require explicit kinetic parameters.The constraint-based models,like flux balance analysis(FBA),lack of the room of performing MCA because they are parameters-free.In this study,we developed a hyper-cube shrink algorithm(HCSA)to incorporate the enzymatic properties to the FBA model by introducing a pair of parameters for each reaction.Our algorithm was able to handle not only prediction of knockout strains but also strains with an adjustment of expression level of certain enzymes.RESULTS We first showed the concept by applying HCSA to a simplest three-nodes network.Then we show the HCSA possesses Michaelis-Menten like behaviors characterized by steady state of ODE.We obtained good prediction of a synthetic network in Saccharomyces cerevisiae producing voilacein and analogues.Finally we showed its capability of predicting the flux distribution in genome-scale networks by applying it to sporulation in yeast.CONCLUSION We have developed an algorithm the impact on fluxes when certain enzymes were inhibited or activated.It provides us a powerful tool to evaluate the consequences of enzyme inhibitor or activator.

Calculation of Empirical and True Maintenance Coefficients by Flux Balance Analysis

The stoichiometric matrix of a simplified metabolic network inBacillus Subtillis was constructed from the flux balance equations,which were used for reconciliation of the measured rates anddetermination of the inner metabolic rates. Thus more reliableresults of the true and empirical maintenance coefficients wereobtained. The true maintenance coefficient is linearly related to thespecific growth rate and changes with the P/O ratio. The measuredbiomass yield of adenosine triphosphate (ATP) is also linearlyrelated to the P/O ratio.

FluxExplorer: A general platform for modeling and analyses of metabolic net-works based on stoichiometry

Stoichiometry-based analyses of meta- bolic networks have aroused significant interest of systems biology researchers in recent years. It is necessary to develop a more convenient modeling platform on which users can reconstruct their network models using completely graphical operations, and explore them with powerful analyzing modules to get a better understanding of the properties of metabolic systems. Herein, an in silico platform, FluxExplorer, for metabolic modeling and analyses based on stoichiometry has been developed as a publicly available tool for systems biology research. This platform integrates various analytic approaches, in- cluding flux balance analysis, minimization of meta- bolic adjustment, extreme pathways analysis, shadow prices analysis, and singular value decom- position, providing a thorough characterization of the metabolic system. Using a graphic modeling process, metabolic networks can be reconstructed and modi- fied intuitively and conveniently. The inconsistencies of a model with respect to the FBA principles can be proved automatically. In addition, this platform sup- ports systems biology markup language (SBML). FluxExplorer has been applied to rebuild a metabolic network in mammalian mitochondria, producing meaningful results. Generally, it is a powerful and very convenient tool for metabolic network modeling and analysis.

产朊假丝酵母分批发酵生产谷胱甘肽的代谢通量分析

根据产朊假丝酵母利用葡萄糖作为惟一碳源生物合成谷胱甘肽的代谢网络,利用代谢通量分析方法分析了谷胱甘肽分批发酵不同阶段各代谢途径碳通量的分布和变化规律.在此基础上,通过分阶段温度控制和L-半胱氨酸添加等策略,调节谷胱甘肽分批发酵过程中的代谢通量分布,谷胱甘肽生物合成的产量明显提高,最大增加幅度分别达到23%和91%·同时对这些策略下谷胱甘肽的过量合成机理进行了解释.

生物信息学用于代谢网络研究的进展与展望

如何分析基因测序和高通量分析方法所获得的海量数据和信息 ,及由此而得到的复杂生物网络 ,是生物信息学研究者所面临的重要任务 .本文综述了基于基因组的大规模代谢网络重建和分析的进展 ,论述了利用生物信息学方法分析代谢网络结构的主要方法和结果 ;比较了现阶段两种最常用的代谢途径分析方法 ,即基元模式和极端途径的差异 ;列举了这两种方法在代谢网络结构和功能分析、工程菌设计等多方面的重要应用 ;指出了现阶段在途径分析领域存在的问题和应对的策略 .

杂交瘤细胞的代谢流量分析

应用代谢流量平衡模型定量分析了杂交瘤细胞的代谢流量分布。结果表明 ,在连续培养的杂交瘤细胞中 ,当葡萄糖和谷氨酰胺的流加浓度分别为 13 8和 2 6mmol·L-1时 ,86 2 %的葡萄糖通过糖酵解生成乳酸 ,7 5 %的生成脂类 ,进入TCA循环的仅占 0 83% ;谷氨酰胺中的氮有 3%用于核酸的合成 ,5 4 5 %生成氨 ,另有 38 2 %生成非必需氨基酸 ,碳骨架 6 1 6 %生成非必需氨基酸 ,34 1%进入TCA循环。

基于冠心病血瘀证心肌细胞能量代谢网络模型的流平衡分析

目的探讨冠心病(coronary artery heart disease,CHD)急、慢性血瘀证心肌细胞能量代谢的变化特点。方法 (1)运用Flux Explorer软件平台构建起的CHD血瘀证心肌细胞能量代谢网络模型;(2)应用GC-MS技术和血气自动分析仪从健康对照、CHD慢性血瘀证、CHD急性血瘀证三组大鼠模型中取冠状动脉血检测葡萄糖、脂肪酸及血氧含量;(3)检测结果代入网络模型,通过流平衡分析CHD急、慢性血瘀证状态下参数的特点。结果 (1)三组的糖酵解、脂代谢和三羧酸循环均已被激活,但网络运算后各组代谢物的数值则有明显差别,均呈现出健康对照组>CHD慢瘀证组>CHD急瘀证组的趋势。(2)三组心肌细胞能量代谢的血糖、脂肪酸和血氧含量及ATP的产量均呈递减的趋势,这与三组中糖酵解的关键酶(HK、pfk、PK)、三羧酸循环的关键酶(Cs、Icdh、Od)的浓度呈一致性变化。结论 CHD急性血瘀和慢性血瘀不同证候的形成可能与机体内心肌细胞能量代谢的水平密切相关。

高浓度乙醇连续发酵振荡过程中代谢通量分析及诱发机理

在利用普通酿酒酵母进行高浓度乙醇连续发酵的实验中,发现了一种长周期、高振幅的振荡现象。利用流式细胞仪测定了振荡周期不同时点的细胞周期分布,表明这种特殊的振荡现象和酵母细胞周期的同步化不相关。对一个完整振荡周期中不同时点的代谢通量分析,发现胞内碳通量在代谢网络中的分布也呈现出和胞外残糖、乙醇和生物量浓度类似的振荡过程。分析酵母细胞代谢活性与胞外发酵体系乙醇浓度的时程关系,表明酵母细胞对乙醇抑制的延迟反应是诱发这种振荡行为的主要因素,胞内海藻糖积累与乙醇生成同步,进一步支持了这一观点。

活细胞在线监控L-羟脯氨酸补料发酵工艺的研究

为了研究活细胞在线监控补料技术对L-羟脯氨酸发酵的影响,在30 L发酵罐上安装活细胞在线检测仪,实时监测发酵液中活细胞数量,根据活细胞数量调整补料策略,分析活细胞在线监控补料对L-羟脯氨酸发酵补糖速率、产量、糖酸转化率和代谢流量分布的影响。结果表明,采用活细胞在线监控补料发酵工艺生产L-羟脯氨酸的最佳补糖策略是在发酵中后期及时降低补糖速率,最终降到9 g/(L·h),L-羟脯氨酸产量提高了11.39%,乙酸积累量减少了48.69%,糖酸转化率提高了14.92%,糖酵解途径和乙醛酸循环的代谢流分别减少了8.50%和16.89%,磷酸戊糖途径的代谢流增加了14.73%,乙酸合成的代谢流减少了4.97%,L-羟脯氨酸合成的代谢流提高了16.21%。上述结果说明采用活细胞在线监控补料技术生产L-羟脯氨酸,可以有效降低副产物的积累,提高L-羟脯氨酸产量及糖酸转化率。

应用细胞合成计量关系模型分析鱼腥藻7120的混合营养生长

根据文献中鱼腥藻细胞大分子的含量和组成情况 ,计算了细胞合成所需要的小分子单体和前体代谢物的量 ,得到代谢意义上的细胞生物合成计量关系 .利用此计量关系模型 ,对鱼腥藻 712 0细胞在气升式光生物反应器中光自养和混合营养生长的对数生长阶段进行了代谢通量分析 .结果表明 ,葡萄糖的利用影响了细胞初级碳代谢的流动状况 ,混合营养生长过程比光自养生长过程磷酸戊糖途径氧化性分支的反应程度明显加强 .随着培养的进行 ,葡萄糖被细胞用作碳源的比例可能减小 ,而用作能源的比例可能增大 .图 2表 14参

代谢通量分析在酶合成过程中的应用研究进展

代谢通量分析可以有效预测微生物的代谢状况,指导生物合成过程,已在多个领域得到成功应用。本文概括分析了三大代谢通量分析技术(基于物料平衡、基于同位素示踪技术以及基于基因组尺度规模网络)在工业生物技术领域的应用,阐述了相关技术的优势及局限性。在此基础上重点论述了基于物料平衡及同位素示踪这两类通量分析技术在酶合成领域的研究进展,分析了代谢通量分析应用于复杂代谢产物可能存在的问题,指出如何将信号转导、转录调控等信息纳入代谢网络将成为未来酶合成过程代谢分析的研究重点。

杂交瘤细胞代谢流分析模型及其在批式培养中的应用

通过物料衡算和线性规划方法建立了进行代谢流分析的数学模型 ,并首次应用于批式培养过程 .分析结果表明 ,细胞对葡萄糖等底物的代谢效率很低 ,细胞对谷氨酰胺等氨基酸浓度的变化响应迟缓 ,在底物充裕的迟滞期及谷氨酰胺限制条件下的代谢效率高于对数生长期 .另外限制谷氨酰胺等氨基酸进入能量代谢 ,既可减少氨的积累 ,也会减少乳酸的生成 .

代谢工程技术及其在微生物育种的应用

代谢工程(Metabolic Engineering),亦称途径工程(Pathway Engineering),是一门利用分子生物学原理系统地分析细胞代谢网络,并通过DNA重组技术合理设计细胞代谢途径及遗传修饰,进而完成细胞特性改造的应用性学科。代谢工程综合了生物化学、化学工程、数学分析等多学科内容,是当前国内外学者研究热点之一。论述了代谢工程的发展过程、基本原理及特点以及该项技术在微生物育种的最新应用进展,并展望了未来发展前景。

采用代谢流量分析方法评估二噁英对细胞的代谢干扰

以肝癌细胞HepG2为供试细胞,选用5个不同浓度的二噁英(0、0.001、0.01、0.1和1.0 nmol.L-1),探讨2,3,7,8-TCDD对HepG2细胞的毒性效应.采用90%甲醇离心法实现蛋白与代谢产物的分离,建立了高效液相色谱-串联三重四极杆质谱(HPLC-MS/MS)分析胞外液中的23种代谢产物(20种氨基酸、乳酸、甘油和尿素)的方法.建立的HPLC-MS/MS分析方法对各化合物的加标回收率可达91%—105%之间,化合物浓度的标准偏差均小于10%.在此基础上,结合代谢流量分析系统研究了2,3,7,8-TCDD对HepG2细胞内代谢流的影响.结果表明,随着浓度的增高,2,3,7,8-TCDD对HepG2细胞代谢的干扰作用呈增强趋势.2,3,7,8-TCDD降低了细胞对葡萄糖的吸收,并进一步抑制了HepG2细胞糖酵解的速率.此外,2,3,7,8-TCDD导致了HepG2细胞乳酸代谢流的明显增加,由此使细胞产生了过量的乳酸.

葡萄糖浓度对杂交瘤细胞代谢通量分布的影响

将代谢工程理论应用于动物细胞的流加培养过程。通过构建代谢网络模型,研究了杂交瘤细胞从动态到拟稳态过程中物质和能量代谢通量的时间分布图。结果表明:当葡萄糖浓度较高时,葡萄糖在乳酸生成途径和TCA循环的代谢通量分配比例分别为90%和10%,ATP生成的比例分别为20%和60%,说明葡萄糖大部分经糖酵解途径生成乳酸,而进入TCA循环的少量部分则提供大部分的能量生成。当葡萄糖浓度降至其限制细胞生长的浓度时,进入TCA循环的葡萄糖比例逐步提高到100%,葡萄糖的氧化程度从0.6mmol.mmol-1增至4.52mmol.mmol-1,说明葡萄糖代谢逐渐从乳酸生成途径迁移到TCA循环,从无氧代谢逐渐转移到有氧代谢。

代谢网络流分析方法概述

生物体代谢网络模型是系统生物学的重要研究内容之一,代谢网络流分析方法在代谢网络分析中具有重要意义。了解代谢网络流量分析方法有助于理解代谢网络结构和功能的关系,代谢流量与基因表达的关系,在分析疾病,识别药物靶标,制药工程和基因改造方面具有重要现实意义。综述了几种常用代谢网络流分析方法:流平衡分析(FBA)、代谢调节最小化分析(MOMA)、调节开关最小化分析(ROOM)、含有二次线性规划的动态流平衡分析方法(DFBA-LQR)、流量范围分析(FVA)、双层规划策略识别基因敲除(Opt Knock)、基因表达上调下调(OptReg)和重新设计生产系统的计算框架(Opt Strain)等,以期为代谢网络流分析提供参考,并进一步为实验研究提供指导。