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。     

地衣芽孢杆菌发酵生产b-甘露聚糖酶的代谢通量分析

基于代谢通量分析理论研究了地衣芽孢杆菌生物合成b-甘露聚糖酶的胞内代谢活动. 首先构建了地衣芽孢杆菌产b-甘露聚糖酶的代谢网络,并依据代谢物质量平衡原则建立了反应网络的代谢流模型;进一步采用线性规划的优化方法分别以b-甘露聚糖酶合成反应通量最大和菌体生长速率最大为目标函数对模型进行求解,由此计算得到b-甘露聚糖的最大理论转化率为57.87%. 最后对代谢网络中的5个关键节点进行了比较分析,得到了2个类似非刚性的代谢节点(5-磷酸核酮糖节点和草酰乙酸节点),为利用基因工程方法提高b-甘露聚糖酶的生产能力提供了理论依据.     

克雷伯氏杆菌发酵生产1,3-丙二醇的代谢通量优化分析

以克雷伯氏杆菌为研究对象,系统研究了1,3-丙二醇厌氧和微氧代谢途径.采用代谢通量分析法建立了代谢通量平衡模型,通过线性规划对1,3-丙二醇厌氧和微氧发酵进行了优化分析,得到其最优代谢通量分布和最大理论得率,并分析了比生长速率、底物浓度以及乙醇生成速率、氧气消耗速率、呼吸商对1,3-丙二醇得率的影响.     

克雷伯氏杆菌代谢灵活性分析

微生物对外界环境的变化具有灵活的适应性,通过代谢网络的通量可变性分析可以对代谢灵活性进行量化,得到影响菌体代谢灵活性的因素。使用代谢流分析软件COBRA Toolbox,对克雷伯氏杆菌基因组尺度代谢网络进行通量平衡分析与通量可变性分析,分别得到内部反应的可变性、交换反应的可变性和生长速率的可变性等3个因素对菌体代谢灵活性的影响程度,其中生长速率的可变性是影响代谢灵活性的主要因素,调控菌体至次优模式下生长可使其具有更大的代谢灵活性。通过进一步研究生长速率对通量可变性的影响,找到提高代谢灵活性的两个必要条件,从而增强菌体应对外界环境改变的能力。     

结合元素组成数据进行细胞生长过程代谢通量分析

将细胞内的生物大分子合成网络简化为对某些中心代谢系统前体的需求速率 ,由此得到一枯草杆菌生长代谢网络 ,并以其为对象结合文献数据对细胞生长过程的通量分析方法进行了研究 .提出了一种利用碳元素平衡和一般化还原度平衡将细胞的元素组成数据与其分子组成数据相结合计算胞内通量分布的最小二乘解的方法 ,可以利用更多的测量数据 ,并保证各测得量间的一致性 ,从而使计算结果更为可靠 .     

多元模块工程在代谢工程中的应用与研究进展

随着代谢工程理论体系的发展,代谢工程的研究方法目前已从对单一途径的调控转变为对整个代谢网络的全局调控。同时,为了在工业微生物领域实现与化学工业生产规模相当的生物炼制过程,代谢工程需要一套通用的菌株优化策略。其中关键问题之一,是解决代谢通量的不平衡。本文介绍了基于传统的理性代谢工程与近年来兴起的组合工程中存在的问题,研究者提出了一种模块化的代谢网络优化策略--多元模块工程（multivariate modular metabolic engineering,MMME）。阐述了多元模块工程的原理和方法,列举了其常用的调控技术和手段,在此基础上综述了近年来模块化策略在代谢工程领域的应用进展,提出了该策略面临的主要问题并展望了其未来的发展方向。     

外部代谢物模拟在代谢工程中的应用指导

外部代谢物是代谢网络模型中通过添加运输反应来平衡其在菌体内的通量的一类代谢物,比如底物、产物或副产物、信号分子等。首次通过对模型中运输反应的模拟来预测大肠杆菌和枯草芽孢杆菌在不同的生长状态下外部代谢物的优先排出顺序。部分模拟结果已经通过实验和文献数据得到证实。这一分析对应用代谢工程技术提高目标产物的产量有很大的指导意义。     

新型生物絮凝剂REA-11的代谢模型建立与代谢网络分析

利用代谢通量分析方法,对谷氨酸棒杆菌Corynebacterium glutamicum CCTCC M201005分批发酵不同阶段和不同溶氧浓度下的代谢网络模型进行了计算,考察了代谢节点对絮凝剂REA-11合成的影响,并对C. glutamicum生长代谢过程中能量和还原力的产生及消耗问题进行分析.结果发现,磷酸戊糖途径(PP)通量在整个发酵过程中始终维持在较高的水平;REA-11合成通量随溶氧浓度的增加而降低,菌体合成通量则随溶氧水平的增加而增加;ATP通量的增加可以促进菌体生长,而与REA-11的合成呈负相关.     

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.     

人工代谢路径适配性的研究进展

微生物细胞工厂以可再生资源为原料,实现了大宗化学品和天然产物的可持续生产,并有望替代石油化工炼制和动植物提取。剪接天然或人工代谢路径是构建微生物细胞工厂的基础。然而,剪接代谢路径造成的代谢流扰动,导致微生物细胞工厂的适配性差,降低了微生物细胞工厂的生产性能。提高人工代谢路径之间的适配性,以及人工代谢路径与底盘微生物细胞之间的适配性,将是改善微生物细胞工厂生产性能的关键。本文从强化与平衡人工代谢路径的代谢通量,解除人工代谢路径与底盘细胞内源代谢路径的交互作用,以及强化人工代谢路径与底盘细胞整体代谢网络的适配性层面,对提高微生物细胞工厂适配性的研究现状进行介绍。开发高效的多重适配性调控策略,在细胞水平重置代谢路径的适配性与提高微生物细胞对代谢产物的适配性,将是未来的研究重点。     

Whole Cell Modeling: From Single Cells to Colonies

A great deal of research over the last several years has focused on how the inherent randomness in movements and reactivity of biomolecules can give rise to unexpected large-scale differences in the behavior of otherwise identical cells. Our own research has approached this problem from two vantage points – a microscopic kinetic view of the individual molecules (nucleic acids, proteins, etc.) diffusing and interacting in a crowded cellular environment; and a broader systems-level view of how enzyme variability can give rise to well-defined metabolic phenotypes. The former led to the development of the Lattice Microbes software – a GPU-accelerated stochastic simulator for reaction-diffusion processes in models of whole cells; the latter to the development of a method we call population flux balance analysis (FBA). The first part of this article reviews the Lattice Microbes methodology, and two recent technical advances that extend the capabilities of Lattice Microbes to enable simulations of larger organisms and colonies. The second part of this article focuses on our recent population FBA study of Escherichia coli, which predicted variability in the usage of different metabolic pathways resulting from heterogeneity in protein expression. Finally, we discuss exciting early work using a new hybrid methodology that integrates FBA with spatially resolved kinetic simulations to study how cells compete and cooperate within dense colonies and consortia.     

Metabolic network modelling: Including stochastic effects

We propose to model the dynamics of metabolic networks from a systems biology point of view by four dynamical structure elements: potential function, translocation matrix, degradation matrix, and stochastic force. These four elements are balanced to determine the network dynamics, which gives arise to a special stochastic differential equation supplemented by a relationship between the stochastic force and the degradation matrix. Important network behaviors can be obtained from the potential function without explicitly solving for the time-dependent solution. The existence of such a potential function suggests a global optimization principle. The existence stochastic force corresponds naturally to the hierarchical structure in metabolic networks. We provide theoretical evidences to justify our proposal by discussing its connections to others large-scale biochemical systems approaches, such as the network thermodynamics theory, biochemical systems theory, metabolic control analysis, and flux balance analysis. Experimental data displaying stochasticity which carries important biological information are also pointed out.     

State of the art in silico tools for the study of signaling pathways in cancer

In the last several years, researchers have exhibited an intense interest in the evolutionarily conserved signaling pathways that have crucial roles during embryonic development. Interestingly, the malfunctioning of these signaling pathways leads to several human diseases, including cancer. The chemical and biophysical events that occur during cellular signaling, as well as the number of interactions within a signaling pathway, make these systems complex to study. In silico resources are tools used to aid the understanding of cellular signaling pathways. Systems approaches have provided a deeper knowledge of diverse biochemical processes, including individual metabolic pathways, signaling networks and genome-scale metabolic networks. In the future, these tools will be enormously valuable, if they continue to be developed in parallel with growing biological knowledge. In this study, an overview of the bioinformatics resources that are currently available for the analysis of biological networks is provided.     

大肠杆菌调控因子工程及其菌株耐受性研究进展

大肠杆菌具有金字塔式的基因表达等级调控网络,调控因子的自动调控、共调控和交叉调控,构成了复杂而又精细的转录调控网络。微生物通过扰动和优化这个高效的调控网络快速地响应环境变化,而适应新的耐受条件。微生物的耐受性是由多基因控制的复杂表型,通过大肠杆菌调控因子工程,可以大尺度重构调控网络,显著改进菌株耐受性,成为了近几年的研究热点。本文总结了大肠杆菌调控因子及其工程方法,综述了通过大肠杆菌调控因子工程重构代谢网络来提高菌株耐受性的最新研究进展,展望了通过大肠杆菌调控因子工程提高菌株鲁棒性的发展方向。     

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.     

Artificial neural network-based prediction of hydrogen content of coal in power station boilers

Artificial neural networks (ANN) are powerful tools that can be used to model and investigate various highly complex and non-linear phenomena. This paper describes the development and training of a feed-forward back-propagation artificial neural network (BPNN), which is used to predict the hydrogen content in coal from proximate analysis. The ultimate objective is to enhance the performance of the combustion control system with the aid of regularly obtained knowledge of the elemental content of coal.In the present work, network modelling was performed using MATLAB with the Levenberg–Marquardt algorithm. Nine-hundred and three sets of data from a diverse range of coals have been used to develop the neural network architecture and topology. Trials were performed using one or two hidden layers with the number of neurons varied from 4 to 30. Validation data has been adopted to evaluate each trial and better model structure is determined to combat the over-fitting problem. As a result, it was found that a 4-12-1 or 4-8-4-1 network could give the most accurate prediction for this particular study. The regression analysis of the model tested gave a 0.937 correlation coefficient and the mean squared error of 0.0087. The average relative error is 5.46%. This has demonstrated that artificial neural networks have good potential for predicting elemental content of coal from frequently available proximate analysis data in power utilities.     

The G Protein-Coupled Receptor Heterodimer Network (GPCR-HetNet) and Its Hub Components

G protein-coupled receptors (GPCRs) oligomerization has emerged as a vital characteristic of receptor structure. Substantial experimental evidence supports the existence of GPCR-GPCR interactions in a coordinated and cooperative manner. However, despite the current development of experimental techniques for large-scale detection of GPCR heteromers, in order to understand their connectivity it is necessary to develop novel tools to study the global heteroreceptor networks. To provide insight into the overall topology of the GPCR heteromers and identify key players, a collective interaction network was constructed. Experimental interaction data for each of the individual human GPCR protomers was obtained manually from the STRING and SCOPUS databases. The interaction data were used to build and analyze the network using Cytoscape software. The network was treated as undirected throughout the study. It is comprised of 156 nodes, 260 edges and has a scale-free topology. Connectivity analysis reveals a significant dominance of intrafamily versus interfamily connections. Most of the receptors within the network are linked to each other by a small number of edges. DRD2, OPRM, ADRB2, AA2AR, AA1R, OPRK, OPRD and GHSR are identified as hubs. In a network representation 10 modules/clusters also appear as a highly interconnected group of nodes. Information on this GPCR network can improve our understanding of molecular integration. GPCR-HetNet has been implemented in Java and is freely available at http://www.iiia.csic.es/~ismel/GPCR-Nets/index.html.     

Application of convolutional neural networks to large-scale naphtha pyrolysis kinetic modeling

System design and optimization problems require large-scale chemical kinetic models. Pure kinetic models of naphtha pyrolysis need to solve a complete set of stiff ODEs and is therefore too computational expensive. On the other hand, artificial neural networks that completely neglect the topology of the reaction networks often have poor generalization. In this paper, a framework is proposed for learning local representations from large-scale chemical reaction networks. At first, the features of naphtha pyrolysis reactions are extracted by applying complex network characterization methods. The selected features are then used as inputs in convolutional architectures. Different CNN models are established and compared to optimize the neural network structure. After the pre-training and fine-tuning step, the ultimate CNN model reduces the computational cost of the previous kinetic model by over 300 times and predicts the yields of main products with the average error of less than 3%. The obtained results demonstrate the high efficiency of the proposed framework.     

Cell-Free Exploration of the Natural Product Chemical Space

Natural products and secondary metabolites comprise an indispensable resource from living organisms that have transformed areas of medicine, agriculture, and biotechnology. Recent advances in high-throughput DNA sequencing and computational analysis suggest that the vast majority of natural products remain undiscovered. To accelerate the natural product discovery pipeline, cell-free metabolic engineering approaches used to develop robust catalytic networks are being repurposed to access new chemical scaffolds, and new enzymes capable of performing diverse chemistries. Such enzymes could serve as flexible biocatalytic tools to further expand the unique chemical space of natural products and secondary metabolites, and provide a more sustainable route to manufacture these molecules. Herein, we highlight select examples of natural product biosynthesis using cell-free systems and propose how cell-free technologies could facilitate our ability to access and modify these structures to transform synthetic and chemical biology.     

Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials

Crystalline porous materials have a variety of uses, such as for catalysis and separation. Identifying suitable materials for a given application can, in principle, be done by screening material databases. Such a screening requires automated high-throughput analysis tools that calculate structural properties for all materials contained in a database so they can be compared with search queries, grouped or classified. One important aspect of the structural analysis of materials such as zeolites and metal organic frameworks is the investigation of the geometrical parameters describing pores. Here, we present algorithms and tools to efficiently calculate some of these important parameters. Our tools are based on the Voronoi decomposition, which for a given arrangement of atoms in a periodic domain provides a graph representation of the void space. The resulting Voronoi network is analyzed to obtain the diameter of the largest included sphere and the largest free sphere, which are two geometrical parameters that are frequently used to describe pore geometry. Accessibility of nodes in the network is also determined for a given guest molecule and the resulting information is later used to retrieve dimensionality of channel systems as well as in Monte Carlo sampling of accessible surfaces and volumes. The presented algorithms are implemented in a software tool, Zeo++, which includes a modified version of the Voro++ library. We present example applications of our algorithms and tools using zeolite frameworks currently listed in the Atlas of Zeolite Frameworks.