代谢网络定量分析研究进展

综述了代谢工程中代谢控制分析、代谢通量分析、生化系统理论、途径分析、控制论模型等定量分析方法的基本理论,以实例说明了这些方法的应用,并对代谢分析方法的发展进行了展望。     

Combinatorial complexity of pathway analysis in metabolic networks

Elementary flux mode analysis is a promising approach for a pathway-oriented perspective of metabolic networks. However, in larger networks it is hampered by the combinatorial explosion of possible routes. In this work we give some estimations on the combinatorial complexity including theoretical upper bounds for the number of elementary flux modes in a network of a given size. In a case study, we computed the elementary modes in the central metabolism of Escherichia coli while utilizing four different substrates. Interestingly, although the number of modes occurring in this complex network can exceed half a million, it is still far below the upper bound. Hence, to a certain extent, pathway analysis of central catabolism is feasible to assess network properties such as flexibility and functionality.     

Optimization-based metabolic control analysis

In this work, a novel optimization-based metabolic control analysis (OMCA) method is introduced for reducing data requirement for metabolic control analysis (MCA). It is postulated that using the optimal control approach, the fluxes in a metabolic network are correlated to metabolite concentrations and enzyme activities as a state-feedback control system that is optimal with respect to a homeostasis objective. It is then shown that the optimal feedback gains are directly related to the elasticity coefficients (ECs) of MCA. This approach requires determination of the relative "importance" of metabolites and fluxes for the system, which is possible with significantly reduced experimental data, as compared with typical MCA requirements. The OMCA approach is applied to a top-down control model of glycolysis in hepatocytes. It is statistically demonstrated that the OMCA model is capable of predicting the ECs observed experimentally with few exceptions. Further, an OMCA-based model reconciliation study shows that the modification of four assumed stoichiometric coefficients in the model can explain most of the discrepancies, with the exception of elasticities with respect to the NADH/NAD ratio.     

Physiology of microbial cells and metabolic engineering

This review is devoted to the problems of the physiology and cell biology of microorganisms in relation to metabolic engineering. The latter is considered as a branch of fundamental and applied biotechnology aimed at controlling microbial metabolism by methods of genetic engineering and classical genetics and based on intimate knowledge of cell metabolism. Attention is also given to the problems associated with the metabolic limitation of microbial biosyntheses, analysis and control of metabolic fluxes, rigidity of metabolic pathways, the role of pleiotropic (global) regulatory systems in the control of metabolic fluxes, and prospects of physiological and evolutionary approaches in metabolic engineering.     

Rapid media transition: An experimental approach for steady state analysis of metabolic pathways

Commonly steady state analysis of microbial metabolism is performed under well defined physiological conditions in continuous cultures with fixed external rates. However, most industrial bioprocesses are operated in fed‐batch mode under non‐stationary conditions, which cannot be realized in chemostat cultures. A novel experimental setup—rapid media transition—enables steady state perturbation of metabolism on a time scale of several minutes in parallel to operating bioprocesses. For this purpose, cells are separated from the production process and transferred into a lab‐scale stirred‐tank reactor with modified environmental conditions. This new approach was evaluated experimentally in four rapid media transition experiments with Escherichia coli from a fed‐batch process. We tested the reaction to different carbon sources entering at various points of central metabolism. In all cases, the applied substrates (glucose, succinate, acetate, and pyruvate) were immediately utilized by the cells. Extracellular rates and metabolome data indicate a metabolic steady state during the short‐term cultivation. Stoichiometric analysis revealed distribution of intracellular fluxes, which differs drastically subject to the applied carbon source. For some reactions, the variation of flux could be correlated to changes of metabolite concentrations. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Numerical bias estimation for mass spectrometric mass isotopomer analysis

Mass spectrometric (MS) isotopomer analysis has become a standard tool for investigating biological systems using stable isotopes. In particular, metabolic flux analysis uses mass isotopomers of metabolic products typically formed from ¹³C-labeled substrates to quantitate intracellular pathway fluxes. In the current work, we describe a model-driven method of numerical bias estimation regarding MS isotopomer analysis. Correct bias estimation is crucial for measuring statistical qualities of measurements and obtaining reliable fluxes. The model we developed for bias estimation corrects a priori unknown systematic errors unique for each individual mass isotopomer peak. For validation, we carried out both computational simulations and experimental measurements. From stochastic simulations, it was observed that carbon mass isotopomer distributions and measurement noise can be determined much more precisely only if signals are corrected for possible systematic errors. By removing the estimated background signals, the residuals resulting from experimental measurement and model expectation became consistent with normality, experimental variability was reduced, and data consistency was improved. The method is useful for obtaining systematic error-free data from ¹³C tracer experiments and can also be extended to other stable isotopes. As a result, the reliability of metabolic fluxes that are typically computed from mass isotopomer measurements is increased.     

L-精氨酸高产菌的选育及基于代谢流量分布的育种机制

从分析钝齿棒杆菌(Corynebacterium crenatum)的精氨酸合成途径入手,提出了一种通过选育脯氨酸结构类似物抗性突变株以提高其精氨酸合成能力的育种思路。采用亚硝基胍(NTG)诱变处理出发菌株YD8(His-,SGr1.2 mg/mL,D-Argr15 mg/mL),经含15 mg/mL的脯氨酸结构类似物S-甲基半胱氨酸(S-MC)的抗性筛选获得精氨酸高产突变株YDM403(His-,SGr1.2 mg/mL,D-Argr15 mg/mL,S-MCr15 mg/mL),产酸水平可达29.4 g/L,较出发菌株YD8的产酸高出55.0%。通过代谢流量分布分析了菌株YD8和YDM403代谢网络的变化,结果表明,菌株YD8可能存在顺序反馈抑制作用,出发株YD8解除了Arg对Glu到Arg的反馈抑制,而YDM403又解除了Pro对Glu到Pro的反馈抑制,从而使中间物Glu累积量下降而对-αKG到Glu不再有反馈抑制,其通量提高,与此同时从Glu向Arg的代谢通量也相应增加。     

Mass flux balance-based model and metabolic pathway engineering analysis for serine alkaline protease synthesis by Bacillus licheniformis

A mass flux balance-based stoichiometric model of Bacillus licheniformis for the serine alkaline protease (SAP) fermentation process has been established. The model considers 147 reaction fluxes, and there are 105 metabolites that are assumed to be in pseudo-steady state. Metabolic flux distributions were obtained from the solution of the model based on the minimum SAP accumulation rate assumption in B. licheniformis in combination with the off-line extracellular analyses of the metabolites that were the sole carbon source citrate, dry cell, organic acids, amino acids, and SAP; variations in the intracellular fluxes were demonstrated for the three periods of the batch bioprocess. The flux distribution maps showed that the cells completed the TCA cycle and utilized the gluconeogenesis pathway, pentose phosphate pathway, and anaplerotic reactions throughout the fermentation; however, the glycolysis pathway was inactive in all the periods of the fermentation. The flux values toward SAP increased throughout the bioprocess and slightly decreased in the last period; however, SAP selectivity values were almost the same in Periods II and III and higher than Period I. The diversions in the pathways and certain metabolic reactions depending on the bioprocess periods are also presented and the results indicated that the intracellular amino acid fluxes played an important role in the SAP fermentation process.     

Photobioreactor design for isotopic non‐stationary 13C‐metabolic flux analysis (INST 13C‐MFA) under photoautotrophic conditions

Adaptive metabolic behavior of photoautotrophic microorganisms toward genetic and environmental perturbations can be interpreted in a quantitative depiction of carbon flow through a biochemical reaction network using isotopic non‐stationary ¹³C‐metabolic flux analysis (INST ¹³C‐MFA). To evaluate ¹³C‐metabolic flux maps for Chlamydomonas reinhardtii, an original experimental framework was designed allowing rapid, reliable collection of high‐quality isotopomer data against time. It involved (i) a short‐time ¹³C labeling injection device based on mixing control in a torus‐shaped photobioreactor with plug‐flow hydrodynamics allowing a sudden step‐change in the ¹³C proportion in the substrate feed and (ii) a rapid sampling procedure using an automatic fast filtration method coupled to a manual rapid liquid nitrogen quenching step. ¹³C‐substrate labeling enrichment was controlled through the total dissolved inorganic carbon concentration in the pulsed solution. First results were obtained from steady‐state continuous culture measurements allowing the characterization of the kinetics of label incorporation into light‐limited growing cells cultivated in a photobioreactor operating at the maximal biomass productivity for an incident photon flux density of 200 µmol m^?2 s^?1. ¹³C label incorporation was measured for 21 intracellular metabolites using IC‐MS/MS in 58 samples collected across a labeling experiment duration of 7 min. The fastest labeling rate was observed for 2/3‐phosphoglycerate with an apparent isotopic stationary state reached after 300 s. The labeling rate was consistent with the optimized mixing time of about 4.9 s inside the reactor and the shortest reliable sampling period assessed at 5 s. Biotechnol. Bioeng. 2012; 109: 3030–3040. © 2012 Wiley Periodicals, Inc.     

On‐line adaptive metabolic flux analysis: Application to PHB production by mixed microbial cultures

In this work, an algorithm for on‐line adaptive metabolic flux analysis (MFA) is proposed and applied to polyhydroxybutyrate (PHB) production by mixed microbial cultures (MMC). In this process, population dynamics constitutes an important source of perturbation to MFA calculations because some stoichiometric and energetic parameters of the underlying metabolic network are continuously changing over time. The proposed algorithm is based on the application of the observer‐based estimator (OBE) to the central MFA equation, whereby the role of the OBE is to force the accumulation of intracellular metabolites to converge to zero by adjusting the values of unknown network parameters. The algorithm was implemented in a reactor equipped with on‐line analyses of dissolved oxygen and carbon dioxide through respirometric and titrimetric measurements. The oxygen and carbon dioxide fluxes were measured directly, whereas acetate, PHB, and sludge production fluxes were estimated indirectly using a projection of latent structures model calibrated a priori with off‐line measurements. The algorithm was implemented in a way that the network parameters associated with biosynthesis were adjusted on‐line. The algorithm proofed to converge exponentially with the steady state error always below 1 mmol/L. The estimated fluxes passed the consistency index test for experimental error variances as low as 1%. The comparison of measured and estimated respiratory coefficient and of the theoretical and estimated yield of sludge on acetate further confirmed the metabolic consistency of the parameters that were estimated on‐line. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism

Elementary mode analysis is a useful metabolic pathway analysis tool to identify the structure of a metabolic network that links the cellular phenotype to the corresponding genotype. The analysis can decompose the intricate metabolic network comprised of highly interconnected reactions into uniquely organized pathways. These pathways consisting of a minimal set of enzymes that can support steady state operation of cellular metabolism represent independent cellular physiological states. Such pathway definition provides a rigorous basis to systematically characterize cellular phenotypes, metabolic network regulation, robustness, and fragility that facilitate understanding of cell physiology and implementation of metabolic engineering strategies. This mini-review aims to overview the development and application of elementary mode analysis as a metabolic pathway analysis tool in studying cell physiology and as a basis of metabolic engineering.     

Metabolomics assisted metabolic network modeling and network wide analysis of metabolites in microbiology

Metabolomics is the science of qualitatively and quantitatively analyzing low molecular weight metabolites occur in a given biological system. It provides valuable information to elucidate the functional roles and relations of different metabolites in a metabolic pathway. In recent years, a large amount of research on microbial metabolomics has been conducted. It has become a useful tool for achieving highly efficient synthesis of target metabolites. At the same time, many studies have been conducted over the years in order to integrate metabolomics data into metabolic network modeling, which has yielded many exciting results. Additionally, metabolomics also shows great advantages in analyzing the relationship of metabolites network wide. Integrating metabolomics data into metabolic network construction and applying it in network wide analysis of cell metabolism would further improve our ability to control cellular metabolism and optimize the design of cell factories for the overproduction of valuable biochemicals. This review will examine recent progress in the application of metabolomics approaches in metabolic network modeling and network wide analysis of microbial cell metabolism.     

Dynamic metabolic flux analysis (DMFA): a framework for determining fluxes at metabolic non-steady state

Metabolic flux analysis (MFA) is a key tool for measuring in vivo metabolic fluxes in systems at metabolic steady state. Here, we present a new method for dynamic metabolic flux analysis (DMFA) of systems that are not at metabolic steady state. The advantages of our DMFA method are: (1) time-series of metabolite concentration data can be applied directly for estimating dynamic fluxes, making data smoothing and estimation of average extracellular rates unnecessary; (2) flux estimation is achieved without integration of ODEs, or iterations; (3) characteristic metabolic phases in the fermentation data are identified automatically by the algorithm, rather than selected manually/arbitrarily. We demonstrate the application of the new DMFA framework in three example systems. First, we evaluated the performance of DMFA in a simple three-reaction model in terms of accuracy, precision and flux observability. Next, we analyzed a commercial glucose-limited fed-batch process for 1,3-propanediol production. The DMFA method accurately captured the dynamic behavior of the fed-batch fermentation and identified characteristic metabolic phases. Lastly, we demonstrate that DMFA can be used without any assumed metabolic network model for data reconciliation and detection of gross measurement errors using carbon and electron balances as constraints.     

Modelization and experimental studies on the control of the glycolytic-glycogenolytic pathway in rat liver

In this paper we construct a model of the glycolytic-glycogenolytic converging pathway in rat liver, by integrating experimental data obtained in anin vitro system and information available from the literature. The model takes the mathematical expression of an S-system representation within the power law formalism (Savageau, 1976. Biochemical System Analysis: A study of function and design in Molecular Biology. Addison-Wesley, Reading, Mass.). By using this theoretical framework a model analysis was carried out that allowed us a) the assessment of the quality of the model in terms of its consistency and robustness, b) the steady state analysis and control characterization of the system, and c) the study of the dynamics of the system after changes in the level of two magnitudes of biological significance: the glucose concentration and the phosphofructokinase enzyme activity. Model predictions are compared with experimental measurements referred to Logarithmic Gains through fluxes and substrates concentrations showing that there is a good correlation between the model predictions and the experimentally determined values.     

Insights into pH‐induced metabolic switch by flux balance analysis

Lactate accumulation in mammalian cell culture is known to impede cellular growth and productivity. The control of lactate formation and consumption in a hybridoma cell line was achieved by pH alteration during the early exponential growth phase. In particular, lactate consumption was induced even at high glucose concentrations at pH 6.8, whereas highly increased production of lactate was obtained at pH 7.8. Consequently, constraint‐based metabolic flux analysis was used to examine pH‐induced metabolic states in the same growth state. We demonstrated that lactate influx at pH 6.8 led cells to maintain high fluxes in the TCA cycle and malate‐aspartate shuttle resulting in a high ATP production rate. In contrast, under increased pH conditions, less ATP was generated and different ATP sources were utilized. Gene expression analysis led to the conclusion that lactate formation at high pH was enabled by gluconeogenic pathways in addition to facilitated glucose uptake. The obtained results provide new insights into the influence of pH on cellular metabolism, and are of importance when considering pH heterogeneities typically present in large scale industrial bioreactors. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:347–357, 2015     

高产虾青素红法夫酵母的选育及代谢通量分析

以野生型红法夫酵母As2.1557为出发菌株,依次进行两轮紫外线诱变和亚硝基胍诱变,并以10-4与10-3mol/Lβ-紫罗酮作为筛选剂,得到突变株UV-N2-7,其虾青素产量和含量分别较出发菌株提高81.7%和2.25倍。采用Plackett-Burman设计法及响应面分析法对发酵培养基的组分及发酵条件进行了优化,突变株的虾青素产量由从初始的2.674 mg/L提高到了6.338 mg/L。建立了红法夫酵母的代谢网络,并对比了突变株和野生株间歇培养条件下对数生长期的代谢通量分布。结果表明:突变株与野生株相比,PP途径通量有所减小,而EMP途径和TCA循环有所增强,突变株用于菌体生长的通量有所减小;二者的丙酮酸脱氢酶的活性均较低,分泌至胞外的丙酮酸的代谢通量约为32%。因此预计通过遗传改造和发酵控制提高丙酮酸脱氢酶的活性可能会进一步提高虾青素的产率。     

苹果酸对L-谷氨酸发酵代谢流迁移的影响

为更全面深入地理解细胞内谷氨酸代谢的调控机制,以黄色短杆菌GDK-9为供试菌株,应用MATLAB软件和代谢流分析方法定量研究添加苹果酸后L-谷氨酸发酵中、后期胞内的代谢流迁移。在L-谷氨酸发酵中、后期添加2．0g／L苹果酸后,合成副产物L-丙氨酸和乳酸的代谢流量明显减少,分别降低了22．1％和16．5％,EMP途径和乙醛酸循环的代谢流分别减少了2．26％和9．09％,HMP途径的代谢流增加了2．26％,而L-谷氨酸生物合成的代谢流从73．59％增长至79．92％,较未添加前提高了6．33％。添加适量苹果酸能使关键节点发生代谢流迁移,提高了L-谷氨酸合成中心代谢途径的代谢流量。