Modeling with a view to target identification in metabolic engineering: A critical evaluation of the available tools

The state of the art tools for modeling metabolism, typically used in the domain of metabolic engineering, were reviewed. The tools considered are stoichiometric network analysis (elementary modes and extreme pathways), stoichiometric modeling (metabolic flux analysis, flux balance analysis, and carbon modeling), mechanistic and approximative modeling, cybernetic modeling, and multivariate statistics. In the context of metabolic engineering, one should be aware that the usefulness of these tools to optimize microbial metabolism for overproducing a target compound depends predominantly on the characteristic properties of that compound. Because of their shortcomings not all tools are suitable for every kind of optimization; issues like the dependence of the target compound's synthesis on severe (redox) constraints, the characteristics of its formation pathway, and the achievable/desired flux towards the target compound should play a role when choosing the optimization strategy. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

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.     

大肠杆菌整体代谢网络调控基因(csrA)的敲除及其对苯丙氨酸生物合成的影响

csrA基因产物是大肠杆菌芳香族氨基酸生物合成途径中碳中心代谢有关的一种全局性调控蛋白质。采用Red敲除系统介导的同源重组的方法定位缺失大肠杆菌染色体csrA基因,经PCR、DNA测序等多种方法证实了基因重组缺失的可靠性。csrA基因缺失后,缺失菌株较对照菌株,糖酸转化率有所提高,发酵生产苯丙氨酸的能力也得到一定的提高,产酸提高约13%。     

Metabolic adaptation of Escherichia coli during temperature-induced recombinant protein production: 2. Redirection of metabolic fluxes

The impact of temperature-induced synthesis of human basic fibroblast growth factor (hFGF-2) in high-cell-density cultures of recombinant Escherichia coli was studied by estimating metabolic flux variations. Metabolic flux distributions in E. coli were calculated by means of a stoichiometric network and linear programming. After the temperature upshift, a substantially elevated energy demand for synthesis of hFGF-2 and heat shock proteins resulted in a redirection of metabolic fluxes. Catabolic pathways like the Embden-Meyerhof-Parnas pathway and the tricarboxylic acid (TCA) cycle showed significantly enhanced activities, leading to reduced flux to growth-associated pathways like the pentose phosphate pathway and other anabolic pathways. Upon temperature upshift, an excess of NADPH was produced in the TCA cycle by isocitrate dehydrogenase. The metabolic model predicted the involvement of a transhydrogenase generating additional NADH from NADPH, thereby increasing ATP regeneration in the respiratory chain. The influence of the temperature upshift on the host's metabolism was investigated by means of a control strain harboring the "empty" parental expression vector. The metabolic fluxes after the temperature upshift were redirected similarly to the production strain; the effects, however, were observed to a lesser extent and with different time profiles.     

A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data

In contrast to stoichiometric-based models, the development of large-scale kinetic models of metabolism has been hindered by the challenge of identifying kinetic parameter values and kinetic rate laws applicable to a wide range of environmental and/or genetic perturbations. The recently introduced ensemble modeling (EM) procedure provides a promising remedy to address these challenges by decomposing metabolic reactions into elementary reaction steps and incorporating all phenotypic observations, upon perturbation, in its model parameterization scheme. Here, we present a kinetic model of Escherichia coli core metabolism that satisfies the fluxomic data for wild-type and seven mutant strains by making use of the EM concepts. This model encompasses 138 reactions, 93 metabolites and 60 substrate-level regulatory interactions accounting for glycolysis/gluconeogenesis, pentose phosphate pathway, TCA cycle, major pyruvate metabolism, anaplerotic reactions and a number of reactions in other parts of the metabolism. Parameterization is performed using a formal optimization approach that minimizes the discrepancies between model predictions and flux measurements. The predicted fluxes by the model are within the uncertainty range of experimental flux data for 78% of the reactions (with measured fluxes) for both the wild-type and seven mutant strains. The remaining flux predictions are mostly within three standard deviations of reported ranges. Converting the EM-based parameters into a Michaelis–Menten equivalent formalism revealed that 35% of K_m and 77% of k_cat parameters are within uncertainty range of the literature-reported values. The predicted metabolite concentrations by the model are also within uncertainty ranges of metabolomic data for 68% of the metabolites. A leave-one-out cross-validation test to evaluate the flux prediction performance of the model showed that metabolic fluxes for the mutants located in the proximity of mutations used for training the model can be predicted more accurately. The constructed model and the parameterization procedure presented in this study pave the way for the construction of larger-scale kinetic models with more narrowly distributed parameter values as new metabolomic/fluxomic data sets are becoming available for E. coli and other organisms.     

基元模式分析在生物网络和途径分析中的应用

基元模式分析是应用最广泛的代谢途径分析方法。基元模式分析的研究对象从代谢网络发展到信号传导网络;研究尺度从细胞到生物反应器,甚至生态系统;数学描述从稳态分解到动态解析;研究领域从微生物代谢到人类疾病。以下综述了基元模式分析的算法和软件开发现状,以及其在代谢途径与鲁棒性、代谢通量分解、稳态代谢通量分析、动态模型与生物过程模拟、网络结构与调控、菌株设计和信号传导网络等方面的应用。开发新的算法解决组合爆炸问题,探索基元模式与代谢调控的关系以及提高菌株设计算法效率是今后基元模式的重要发展方向。     

Prediction of dynamic behavior of mutant strains from limited wild-type data

Metabolic engineering is the field of introducing genetic changes in organisms so as to modify their function towards synthesizing new products of high impact to society. However, engineered cells frequently have impaired growth rates thus seriously limiting the rate at which such products are made. The problem is attributable to inadequate understanding of how a metabolic network functions in a dynamic sense. Predictions of mutant strain behavior in the past have been based on steady state theories such as flux balance analysis (FBA), minimization of metabolic adjustment (MOMA), and regulatory on/off minimization (ROOM). Such predictions are restricted to product yields and cannot address productivity, which is of focal interest to applications. We demonstrate that our framework ( [Song and Ramkrishna, 2010] and [Song and Ramkrishna, 2011]), based on a “cybernetic” view of metabolic systems, makes predictions of the dynamic behavior of mutant strains of Escherichia coli from a limited amount of data obtained from the wild-type. Dynamic frameworks must necessarily address the issue of metabolic regulation, which the cybernetic approach does by postulating that metabolism is an optimal dynamic response of the organism to the environment in driving reactions towards ensuring survival. The predictions made in this paper are without parallel in the literature and lay the foundation for rational metabolic engineering.     

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。     

Development of a software tool for in silico simulation of Escherichia coli using a visual programming environment

This study describes the development of a software tool, EcoSim, to assist users in implementing quantitative in silico simulation easily. It consists of four parts: extracellular environment and constraints setting mode, table for optimal metabolic flux distribution and chart for changes of substrate concentration, dynamic flux distribution viewer and dynamic hierarchical regulatory network viewer. Representation of a hierarchical regulatory network was constructed with defined modeling symbols and weight in the central Escherichia coli metabolism. All programming procedures for EcoSim were accomplished in a visual programming environment (LabVIEW). To illustrate quantitative in silico simulation with EcoSim, this program was performed on E. coli using glucose and acetate as carbon sources. The simulation results were in agreement with the experimental data obtained from the literature. EcoSim can be used to assist biologists and engineers in predicting and interpreting dynamic behaviors of E. coli under a variety of environmental conditions.     

Metabolic modeling of Saccharomyces cerevisiae using the optimal control of homeostasis: a cybernetic model definition

A model is presented to describe the observed behavior of microorganisms that aim at metabolic homeostasis while growing and adapting to their environment in an optimal way. The cellular metabolism is seen as a network with a multiple controller system with both feedback and feedforward control, i.e., a model based on a dynamic optimal metabolic control. The dynamic network consists of aggregated pathways, each having a control setpoint for the metabolic states at a given growth rate. This set of strategies of the cell forms a true cybernetic model with a minimal number of assumptions. The cellular strategies and constraints were derived from metabolic flux analysis using an identified, biochemically relevant, stoichiometry matrix derived from experimental data on the cellular composition of continuous cultures of Saccharomyces cerevisiae. Based on these data a cybernetic model was developed to study its dynamic behavior. The growth rate of the cell is determined by the structural compounds and fluxes of compounds related to central metabolism. In contrast to many other cybernetic models, the minimal model does not consist of any assumed internal kinetic parameters or interactions. This necessitates the use of a stepwise integration with an optimization of the fluxes at every time interval. Some examples of the behavior of this model are given with respect to steady states and pulse responses. This model is very suitable for describing semiquantitatively dynamics of global cellular metabolism and may form a useful framework for including structured and more detailed kinetic models.     

大肠杆菌PTS系统改造及重组菌生长性能测定

利用Red重组系统对野生大肠杆菌Escherichia coli磷酸烯醇式丙酮酸-糖磷酸转移酶系统(Phosphoenolpyruvate:carbohydrate phosphotransferase system,PTS)进行修饰改造,敲除PTS系统中关键组分EⅡCBGlc的编码基因(ptsG),磷酸组氨酸搬运蛋白HPr的编码基因(ptsI),同时敲入来源于运动发酵单胞菌Zymomonas mobilis的葡萄糖易化体(Glucose facilitator)编码基因(glf),构建重组大肠杆菌,比较测定并系统评价了基因敲除和敲入对细胞的生长、葡萄糖代谢和乙酸积累的影响。敲除基因ptsG和ptsI造成大肠杆菌PTS系统部分功能缺失,细胞生长受到一定限制,敲入glf基因后,重组大肠杆菌能够利用Glf-Glk(葡萄糖易化体-葡萄糖激酶)途径,消耗ATP将葡萄糖进行磷酸化并转运进入细胞。通过该途径转运葡萄糖能够提高葡萄糖利用效率,降低副产物乙酸生成,同时能够使更多的碳代谢流进入后续相关合成途径,预期能够提高相关产物产量。     

Genetically constrained metabolic flux analysis

Significant progress has been made in using existing metabolic databases to estimate metabolic fluxes. Traditional metabolic flux analysis generally starts with a predetermined metabolic network. This approach has been employed successfully to analyze the behaviors of recombinant strains by manually adding or removing the corresponding pathway(s) in the metabolic map. The current work focuses on the development of a new framework that utilizes genomic and metabolic databases, including available genetic/regulatory network structures and gene chip expression data, to constrain metabolic flux analysis. The genetic network consisting of the sensing/regulatory circuits will activate or deactivate a specific set of genes in response to external stimulus. The activation and/or repression of this set of genes will result in different gene expression levels that will in turn change the structure of the metabolic map. Hence, the metabolic map will automatically "adapt" to the external stimulus as captured by the genetic network. This adaptation selects a subnetwork from the pool of feasible reactions and so performs what we term "environmentally driven dimensional reduction." The Escherichia coli oxygen and redox sensing/regulatory system, which controls the metabolic patterns connected to glycolysis and the TCA cycle, was used as a model system to illustrate the proposed approach.     

Increasing galactose consumption by Saccharomyces cerevisiae through metabolic engineering of the GAL gene regulatory network

Increasing the flux through central carbon metabolism is difficult because of rigidity in regulatory structures, at both the genetic and the enzymatic levels. Here we describe metabolic engineering of a regulatory network to obtain a balanced increase in the activity of all the enzymes in the pathway, and ultimately, increasing metabolic flux through the pathway of interest. By manipulating the GAL gene regulatory network of Saccharomyces cerevisiae, which is a tightly regulated system, we produced prototroph mutant strains, which increased the flux through the galactose utilization pathway by eliminating three known negative regulators of the GAL system: Gal6, Gal80, and Mig1. This led to a 41% increase in flux through the galactose utilization pathway compared with the wild-type strain. This is of significant interest within the field of biotechnology since galactose is present in many industrial media. The improved galactose consumption of the gal mutants did not favor biomass formation, but rather caused excessive respiro-fermentative metabolism, with the ethanol production rate increasing linearly with glycolytic flux.     

大肠杆菌细胞工厂的创建技术

大肠杆菌作为一种重要的模式工业微生物,在医药、化工、农业等方面具有广泛的应用。近30年来,多种代谢工程改造的新策略和新技术,被用于设计、构建和优化大肠杆菌化学品细胞工厂,极大地提高了生物法合成化学品的生产速率和产量。文中将从大肠杆菌途径设计、合成途径创建与优化和细胞全局优化三个方面,对大肠杆菌代谢改造起重要推动作用的技术进行综述,并对大肠杆菌代谢工程中关键技术的应用进行了展望。     

Optimization-driven identification of genetic perturbations accelerates the convergence of model parameters in ensemble modeling of metabolic networks

The ensemble modeling (EM) approach has shown promise in capturing kinetic and regulatory effects in the modeling of metabolic networks. Efficacy of the EM procedure relies on the identification of model parameterizations that adequately describe all observed metabolic phenotypes upon perturbation. In this study, we propose an optimization-based algorithm for the systematic identification of genetic/enzyme perturbations to maximally reduce the number of models retained in the ensemble after each round of model screening. The key premise here is to design perturbations that will maximally scatter the predicted steady-state fluxes over the ensemble parameterizations. We demonstrate the applicability of this procedure for an Escherichia coli metabolic model of central metabolism by successively identifying single, double, and triple enzyme perturbations that cause the maximum degree of flux separation between models in the ensemble. Results revealed that optimal perturbations are not always located close to reaction(s) whose fluxes are measured, especially when multiple perturbations are considered. In addition, there appears to be a maximum number of simultaneous perturbations beyond which no appreciable increase in the divergence of flux predictions is achieved. Overall, this study provides a systematic way of optimally designing genetic perturbations for populating the ensemble of models with relevant model parameterizations.