基于酶约束的代谢网络模型研究进展及其应用

基因组尺度代谢网络模型已经成功地应用于指导代谢工程改造,但由于传统通量平衡分析法仅考虑化学计量学和反应方向约束,模拟得到的是理论最优结果,对一些现象如代谢溢流、底物层级利用等无法准确描述。近年来人们通过在代谢网络模型中引入新的蛋白量、热力学等约束发展了新的约束优化计算方法,可以更准确真实地模拟细胞在不同条件下的代谢行为。文中主要对近年来提出的多种酶约束模型进行评述,对酶约束引入的基本思路、酶约束的数学方程表示及优化目标设定、引入酶约束后对代谢通量计算结果的影响及酶约束模型在代谢工程菌种改造中的应用等进行了全面深入的介绍,并提出了已有各种方法存在的主要问题,展望了相关方法的未来发展方向。通过引入新的约束,代谢网络模型能够更精确模拟和预测细胞在环境和基因扰动下的代谢行为,为代谢工程菌种改造提供更准确可靠的指导。     

基于约束的基因组规模代谢网络模型构建方法研究进展

基因组规模代谢网络模型(Genome-scale metabolic network model,GSMM)正成为细胞代谢特性研究的重要工具,经过多年发展相关理论方法取得了诸多进展。近年来,在基础GSMM模型基础上,通过整合基因组、转录组、蛋白组和热力学数据,实现基于各种约束的GSMM构建,在基因靶点识别、系统代谢工程、药物发现、人类疾病机理研究等多个方面取得了进一步的发展和理论突破。文中重点综述包括转录组约束、蛋白组约束、以及热力学约束条件在GSMM中的实施方法、相应方法的不足及应用限制等。最后介绍了如何综合运用转录、蛋白及热力学约束,实现GSMM的全整合模型及其细化,并对基于约束的GSMM构建及应用前景进行了展望。     

数字细胞模型的研究及应用

组学分析技术的发展推动生物学逐渐成为一门以数据分析为中心的科学。依托生物数据在细胞整体系统水平建立数字细胞模型,对于理解细胞系统组织原理和生命产生进化规律,预测各种环境和基因扰动对细胞功能的影响并指导设计人工生命具有重要意义,因此数字细胞的构建模拟设计已成为合成生物学的核心研究内容与底层支撑技术。本文重点对天津工业生物技术研究所创立十年来在数字细胞研究方面的进展进行回顾介绍,重点包括基因组尺度代谢网络模型的构建、质控以及其在途径设计和指导菌种代谢工程改造方面的应用,进一步结合近年来细胞模型研究的前沿趋势,对整合多种约束的模型的构建和分析研究方面的最新成果进行了介绍,最后对数字细胞研究的未来发展方向进行展望。数字细胞技术将与基因组测序、合成和编辑等合成生物学前沿技术一起提升人们对生命进行读写改创的能力。     

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。     

应用代谢网络模型解析工业微生物胞内代谢

为了快速、高效地理解工业微生物胞内代谢特征,寻找潜在的代谢工程改造靶点,基因组规模代谢网络模型(GSMM)作为一种系统生物学工具越来越受到人们的关注。文中在回顾GSMM 20年发展历程的基础上,分析了当前GSMM的研究现状,总结了GSMM的构建及分析方法,从预测细胞表型和指导代谢工程两个方面阐述了GSMM在解析工业微生物胞内代谢中的应用,并展望了GSMM未来的发展趋势。     

乳酸乳球菌NZ9000基因组规模代谢网络模型的构建与验证

随着后基因组时代的到来,工业微生物的代谢工程改造在工业生产上发挥着越来越重要的作用。而基因组规模代谢网络模型(Genome-scalemetabolicmodel,GSMM)将生物体体内所有已知代谢信息进行整合,为全局理解生物体的代谢状态、理性指导代谢工程改造提供了最佳的平台。乳酸乳球菌NZ9000(Lactococcuslactis NZ9000)作为工业发酵领域的重要菌株之一,由于其遗传背景清晰且几乎不分泌蛋白,是基因工程改造和外源蛋白表达的理想模式菌株。文中基于基因组功能注释和比较基因组学构建了L.lactisNZ9000的首个基因组规模代谢网络模型iWK557,包含557个基因、668个代谢物、840个反应,并进一步在定性和定量两个层次验证了iWK557的准确性,以期为理性指导L. lactis NZ9000代谢工程改造提供良好工具。     

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。     

MDMPR:小鼠发育代谢表型库

小鼠发育代谢表型库(Mouse Developmental and Metabolic Phenotype Repository,MDMPR)是一个致力于小鼠资源和表型数据实时共享的开放性平台,它依托于科技部重点研发计划“发育编程及其代谢调节”专项项目“建立小鼠发育代谢表型库”。该项目预计在5年内完成500个发育代谢相关小鼠敲除模型的建立,并对其表型数据进行标准化的解析、建立表型数据库。MDMPR作为一个资源及数据集成的库,由多个子系统作为支撑,包括ES细胞数据库、项目管理系统、繁育管理系统、精子库管理系统、表型分析系统,信息化管理深入到项目中每个环节,从基因突变ES细胞制备、基因突变小鼠制备、小鼠繁育,精子冻存到最终的表型分析、数据处理及展示,保证了MDMPR产生数据的真实性及实时性。MDMPR除了不断地推进项目进行,增加自身产生的数据外,也在积极的整合其他的资源及数据,如人特异性基因敲除ES细胞库、蛋白相互作用数据库(STRING)、核心转录调节环路(dbCoRc)和Enhancer-Indel数据库,今后还将进一步整合,帮助发育代谢及其他领域的研究人员能够一站式的获取所需资源和数据、加快研究进程,最终服务于全人类的医疗事业。     

淀粉蔗糖酶AcAS的工作机理研究与稳定性改造

目的:对新发现的一种新型淀粉蔗糖酶AcAS的结构功能进行深入讨论。方法:用同源模建方法构建AcAS的三维结构;用高斯网络模型和各项异性网络模型,对其功能型运动和工作机理进行预测;利用迭代高斯网络模型方法对其去折叠路径进行预测;根据去折叠路径预测及折叠自由能计算结果设计突变体。结果:模建结果表明,AcAS结构与淀粉蔗糖酶NpAS的结构更相似;AcAS有扭转运动的趋势,其中AcAS的N/C结构域运动性较强,而催化核心的运动性较弱;根据去折叠路径预测,发现N、B和C结构域较易去折叠;通过自由能计算,针对上述3个结构域设计了5株突变体。结论:构建了AcAS的三维结构模型并根据模型探讨了其工作机理;根据去折叠路径预测及折叠自由能计算结果,对AcAS的稳定性改造提出了有益的建议。     

Integrating thermodynamic and enzymatic constraints into genome-scale metabolic models

Stoichiometric genome-scale metabolic network models (GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric ratios, other constraints such as enzyme availability and thermodynamic feasibility can also limit the phenotype solution space. Extended GEM models considering either enzymatic or thermodynamic constraints have been shown to improve prediction accuracy. In this paper, we propose a novel method that integrates both enzymatic and thermodynamic constraints in a single Pyomo modeling framework (ETGEMs). We applied this method to construct the EcoETM (E. coli metabolic model with enzymatic and thermodynamic constraints). Using this model, we calculated the optimal pathways for cellular growth and the production of 22 metabolites. When comparing the results with those of iML1515 and models with one of the two constraints, we observed that many thermodynamically unfavorable and/or high enzyme cost pathways were excluded from EcoETM. For example, the synthesis pathway of carbamoyl-phosphate (Cbp) from iML1515 is both thermodynamically unfavorable and enzymatically costly. After introducing the new constraints, the production pathways and yields of several Cbp-derived products (e.g. L-arginine, orotate) calculated using EcoETM were more realistic. The results of this study demonstrate the great application potential of metabolic models with multiple constraints for pathway analysis and phenotype prediction.     

Genome-scale metabolic models as platforms for strain design and biological discovery

Genome-scale metabolic models (GEMs) have been developed and used in guiding systems’ metabolic engineering strategies for strain design and development. This strategy has been used in fermentative production of bio-based industrial chemicals and fuels from alternative carbon sources. However, computer-aided hypotheses building using established algorithms and software platforms for biological discovery can be integrated into the pipeline for strain design strategy to create superior strains of microorganisms for targeted biosynthetic goals. Here, I described an integrated workflow strategy using GEMs for strain design and biological discovery. Specific case studies of strain design and biological discovery using Escherichia coli genome-scale model are presented and discussed. The integrated workflow presented herein, when applied carefully would help guide future design strategies for high-performance microbial strains that have existing and forthcoming genome-scale metabolic models.     

Systematic assignment of thermodynamic constraints in metabolic network models

Background  

The availability of genome sequences for many organisms enabled the reconstruction of several genome-scale metabolic network models. Currently, significant efforts are put into the automated reconstruction of such models. For this, several computational tools have been developed that particularly assist in identifying and compiling the organism-specific lists of metabolic reactions. In contrast, the last step of the model reconstruction process, which is the definition of the thermodynamic constraints in terms of reaction directionalities, still needs to be done manually. No computational method exists that allows for an automated and systematic assignment of reaction directions in genome-scale models.     

Genome-scale analysis of Mannheimia succiniciproducens metabolism

Mannheimia succiniciproducens MBEL55E isolated from bovine rumen is a capnophilic gram-negative bacterium that efficiently produces succinic acid, an industrially important four carbon dicarboxylic acid. In order to design a metabolically engineered strain which is capable of producing succinic acid with high yield and productivity, it is essential to optimize the whole metabolism at the systems level. Consequently, in silico modeling and simulation of the genome-scale metabolic network was employed for genome-scale analysis and efficient design of metabolic engineering experiments. The genome-scale metabolic network of M. succiniciproducens consisting of 686 reactions and 519 metabolites was constructed based on reannotation and validation experiments. With the reconstructed model, the network structure and key metabolic characteristics allowing highly efficient production of succinic acid were deciphered; these include strong PEP carboxylation, branched TCA cycle, relative weak pyruvate formation, the lack of glyoxylate shunt, and non-PTS for glucose uptake. Constraints-based flux analyses were then carried out under various environmental and genetic conditions to validate the genome-scale metabolic model and to decipher the altered metabolic characteristics. Predictions based on constraints-based flux analysis were mostly in excellent agreement with the experimental data. In silico knockout studies allowed prediction of new metabolic engineering strategies for the enhanced production of succinic acid. This genome-scale in silico model can serve as a platform for the systematic prediction of physiological responses of M. succiniciproducens to various environmental and genetic perturbations and consequently for designing rational strategies for strain improvement.     

Genome-scale modeling of human metabolism – a systems biology approach

Altered metabolism is linked to the appearance of various human diseases and a better understanding of disease-associated metabolic changes may lead to the identification of novel prognostic biomarkers and the development of new therapies. Genome-scale metabolic models (GEMs) have been employed for studying human metabolism in a systematic manner, as well as for understanding complex human diseases. In the past decade, such metabolic models – one of the fundamental aspects of systems biology – have started contributing to the understanding of the mechanistic relationship between genotype and phenotype. In this review, we focus on the construction of the Human Metabolic Reaction database, the generation of healthy cell type- and cancer-specific GEMs using different procedures, and the potential applications of these developments in the study of human metabolism and in the identification of metabolic changes associated with various disorders. We further examine how in silico genome-scale reconstructions can be employed to simulate metabolic flux distributions and how high-throughput omics data can be analyzed in a context-dependent fashion. Insights yielded from this mechanistic modeling approach can be used for identifying new therapeutic agents and drug targets as well as for the discovery of novel biomarkers. Finally, recent advancements in genome-scale modeling and the future challenge of developing a model of whole-body metabolism are presented. The emergent contribution of GEMs to personalized and translational medicine is also discussed.     

Genome-scale model for Clostridium acetobutylicum: Part II. Development of specific proton flux states and numerically determined sub-systems

A regulated genome-scale model for Clostridium acetobutylicum ATCC 824 was developed based on its metabolic network reconstruction. To aid model convergence and limit the number of flux-vector possible solutions (the size of the phenotypic solution space), modeling strategies were developed to impose a new type of constraint at the endo-exo-metabolome interface. This constraint is termed the specific proton flux state, and its use enabled accurate prediction of the extracellular medium pH during vegetative growth of batch cultures. The specific proton flux refers to the influx or efflux of free protons (per unit biomass) across the cell membrane. A specific proton flux state encompasses a defined range of specific proton fluxes and includes all metabolic flux distributions resulting in a specific proton flux within this range. Effective simulation of time-course batch fermentation required the use of independent flux balance solutions from an optimum set of specific proton flux states. Using a real-coded genetic algorithm to optimize temporal bounds of specific proton flux states, we show that six separate specific proton flux states are required to model vegetative-growth metabolism and accurately predict the extracellular medium pH. Further, we define the apparent proton flux stoichiometry per weak acids efflux and show that this value decreases from approximately 3.5 mol of protons secreted per mole of weak acids at the start of the culture to approximately 0 at the end of vegetative growth. Calculations revealed that when specific weak acids production is maximized in vegetative growth, the net proton exchange between the cell and environment occurs primarily through weak acids efflux (apparent proton flux stoichiometry is 1). However, proton efflux through cation channels during the early stages of acidogenesis was found to be significant. We have also developed the concept of numerically determined sub-systems of genome-scale metabolic networks here as a sub-network with a one-dimensional null space basis set. A numerically determined sub-system was constructed in the genome-scale metabolic network to study the flux magnitudes and directions of acetylornithine transaminase, alanine racemase, and D-alanine transaminase. These results were then used to establish additional constraints for the genome-scale model.