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...     

基于约束建模法的结核菌H37Rv代谢网络分析

基于结核分枝杆菌国际标准强毒株H37Rv菌株的基因组尺度代谢网络模型iNJ661进行分析,以寻找代谢网络中培养基的关键成分和必要基因.该研究在Matlab平台上利用COBRA工具箱,采用基于约束的建模方法进行动态生长模拟、解空间抽样在酶活性水平上的具体化和基因删除模拟实验.结果发现培养基成分中铵盐、三价铁盐、磷酸盐、硫酸盐、甘油等可影响H37Rv的生长;培养基中去除磷酸盐后十种酶均在不同程度上受到抑制,其中丙糖磷酸异构酶、3-磷酸甘油醛脱氢酶、磷酸甘油酸变位酶、烯醇酶受限明显.通过基因删除得出188个必要基因以及非必要基因中的16个致死基因对.基于约束建模分析可初步了解结核杆菌H37Rv菌株代谢网络的性质,可为后续相关研究提供参考和借鉴.     

一种基于代谢网络分析最小化基因组的方法及其在大肠杆菌中的应用

最小生命体的合成是合成生物学研究的重要方向。最小化基因组的同时而又不对细胞生长产生影响是代谢工程研究的一个重要目标。文中提出了一种从基因组尺度代谢网络模型出发,通过零通量反应删除及对非必需基因组合删除计算获得基因组最小化代谢网络模型的方法,利用该方法简化了大肠杆菌经典代谢网络模型iAF1260,由起始的1 260个基因简化得到了312个基因,而最优生物质生成速率保持不变。基因组最小化代谢网络模型预测了在细胞正常生长的前提下包含最少基因的代谢途径,为大肠杆菌获得最小基因组的湿实验设计提供了重要参考。     

微生物基因组尺度代谢网络模型构建方法的比较分析

随着后基因时代的到来,微生物的定向菌种改造在生产中发挥着越来越重要的作用.基因组尺度代谢网络模型是微生物定向改造中一种不可缺少的指导性工具,可降低菌种改造的盲目性,增加目的性和成功率.随着研究的深入,基因组尺度代谢网络模型的构建方法也越来越多.究竟选择什么样的方法才能构建出全面准确的基因组尺度代谢网络模型,对于初学者来说是一个大难题.论文结合本课题组的研究,将近年文献报导中出现过的模型构建方法进行了分类和分析,并评述了各种方法的优、缺点,以期为初学者提供参考.具体介绍的方法有:基于基因组注释构建代谢网络模型,基于蛋白组构建代谢网络模型,基于文献挖掘构建代谢网络模型,通过软件和网络平台构建代谢网络模型,基于京都基因与基因组百科全书(KEGG)构建代谢网络模型.五种构建代谢网络模型方法都有其优点,但也有不可避免的缺点,要构建较为准确全面的基因组尺度代谢网络模型,需要将各种方法结合,弥补彼此的不足.图4表0参48     

金黄色葡萄球菌蛋白质相互作用网络及功能

【目的】金黄色葡萄球菌是一种革兰氏阳性菌,是目前最难以对付的病菌之一。它能引起多种感染,特别是在医院环境中。近年来,抗药性金黄色葡萄球菌传染更加严重,已成为公共卫生威胁。由于以前对于金黄色葡萄球菌的实验性研究大都是基于单个基因或者蛋白进行的,为了更好的研究这个物种,有必要从整体上把握金黄色葡萄球菌的蛋白作用机理。【方法】采用系统发生谱、操纵子法、基因融合法、基因邻近法、同源映射法等五种计算方法预测金黄色葡萄球菌蛋白质相互作用网络。【结果】从蛋白组的角度构建了金黄色葡萄球菌蛋白相互作用网络,并对网络进行功能分析。【结论】网络的分析表明金黄色葡萄球菌的蛋白质相互作用网络也服从scale-free属性,发现了SA0939、SA0868、rplD等重要的蛋白。通过对金黄色葡萄球菌的重要的细胞壁合成和信号转导调控蛋白局部网络分析,发现了一些对这两个系统十分重要的蛋白分子,这些信息将为更好的了解金黄色葡萄球菌的致病机理和开发新的药物靶点提供指导。     

细胞代谢复杂网络研究进展

复杂网络理论为细胞代谢网络研究提供了新的工具,基于复杂网络理论的细胞代谢网络研究可称细胞代谢复杂网络研究．先简要介绍了细胞代谢复杂网络的研究背景;随后详细总结和论述了细胞代谢复杂网络在建模、分析和控制三个方面的研究现状;再进一步指出了细胞代谢复杂网络在建模、分析和控制这三个方面研究中所存在的一些问题．为细胞代谢复杂网络领域的研究指出了一些有意义的方向,具有一定的参考价值。     

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。     

金黄色葡萄球菌sdhCAB操纵子影响持留菌形成机制的研究

持留菌是细菌群体中的一小部分细菌,可耐受致死浓度抗生素的处理,是引起慢性感染的重要原因。金黄色葡萄球菌(Staphylococcus aureus,S. aureus)作为常见致病菌,有重要临床意义。分别敲除sdhA和sdhB后,金黄色葡萄球菌持留菌形成水平下降,但sdhCAB操纵子对持留菌形成的作用及机制尚不明确。本研究敲除sdh操纵子,通过酸压力、氧化压力、热压力及抗生素压力实验检测敲除株的持留菌水平,转录组测序检测敲除株的代谢通路变化,高通量微生物细胞表型检测评估敲除株的代谢水平变化。结果显示,敲除sdhCAB或sdhAB后,金黄色葡萄球菌对酸压力、氧化压力的耐受能力均下降;而在抗生素压力、热压力条件下,分别仅sdhCAB敲除株、sdhAB敲除株耐受能力下降。转录组测序发现,sdhCAB敲除后三羧酸循环、甲烷代谢通路及聚合酶Ⅳ等基因表达上调,耐药相关基因、氨基酸代谢基因、糖类代谢基因、卟啉代谢基因及一些转运体基因等表达下调,提示这些通路的基因参与sdhCAB影响持留菌形成的过程。此外,高通量微生物细胞表型检测发现,敲除sdhCAB可降低金黄色葡萄球菌对琥珀酸、柠檬酸、糖原、L-天冬氨酸等64种碳源的代谢。结果提示,sdhCAB操纵子对金黄色葡萄球菌持留菌形成水平有重要影响。本研究初步阐明了sdhCAB操纵子影响持留菌形成的可能机制,为研究和治疗金黄色葡萄球菌慢性感染提供了新的思路。     

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。     

金黄色葡萄球菌核酸酶基因缺失突变株RN4220△nuc1的构建

金黄色葡萄球菌存在两个核酸酶编码基因,一个是葡萄球菌核酸酶(Staphylococcal nuclease,SNase),命名为nuc1,另一个是耐热核酸酶(Thermonuclease,TNase),命名为nuc2,nuc2是一个新的候选基因,以往认为金黄色葡萄球菌中的核酸酶只源于一个编码基因nuc1,为了进一步研究nuc2基因的功能,首先要将金黄色葡萄球菌nuc1基因缺失.研究目的就是通过构建同源重组质粒pBT2莫玭uc1,将其电转入金黄色葡萄球菌菌株RN4220中,获得nuc1基因缺失突变株.经过了7轮培养和筛选,同源重组几率为2%(7/345),筛选出的nuc1突变株用PCR方法和RT-PCR进行了验证,从而获得了nuc1基因缺失突变株RN4220△nuc1.     

A survey on methods for modeling and analyzing integrated biological networks

Understanding how cellular systems build up integrated responses to their dynamically changing environment is one of the open questions in Systems Biology. Despite their intertwinement, signaling networks, gene regulation and metabolism have been frequently modeled independently in the context of well-defined subsystems. For this purpose, several mathematical formalisms have been developed according to the features of each particular network under study. Nonetheless, a deeper understanding of cellular behavior requires the integration of these various systems into a model capable of capturing how they operate as an ensemble. With the recent advances in the "omics" technologies, more data is becoming available and, thus, recent efforts have been driven toward this integrated modeling approach. We herein review and discuss methodological frameworks currently available for modeling and analyzing integrated biological networks, in particular metabolic, gene regulatory and signaling networks. These include network-based methods and Chemical Organization Theory, Flux-Balance Analysis and its extensions, logical discrete modeling, Petri Nets, traditional kinetic modeling, Hybrid Systems and stochastic models. Comparisons are also established regarding data requirements, scalability with network size and computational burden. The methods are illustrated with successful case studies in large-scale genome models and in particular subsystems of various organisms.     

Controllability in Cancer Metabolic Networks According to Drug Targets as Driver Nodes

Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.     

Metabolic systems cost-benefit analysis for interpreting network structure and regulation

MOTIVATION: Interpretation of bioinformatics data in terms of cellular function is a major challenge facing systems biology. This question is complicated by robust metabolic networks filled with structural features like parallel pathways and isozymes. Under conditions of nutrient sufficiency, metabolic networks are well known to be regulated for thermodynamic efficiency however; efficient biochemical pathways are anabolically expensive to construct. While parameters like thermodynamic efficiency have been extensively studied, a systems-based analysis of anabolic proteome synthesis 'costs' and the cellular function implications of these costs has not been reported. RESULTS: A cost-benefit analysis of an in silico Escherichia coli network revealed the relationship between metabolic pathway proteome synthesis requirements, DNA-coding sequence length, thermodynamic efficiency and substrate affinity. The results highlight basic metabolic network design principles. Pathway proteome synthesis requirements appear to have shaped biochemical network structure and regulation. Under conditions of nutrient scarcity and other general stresses, E. coli expresses pathways with relatively inexpensive proteome synthesis requirements instead of more efficient but also anabolically more expensive pathways. This evolutionary strategy provides a cellular function-based explanation for common network motifs like isozymes and parallel pathways and possibly explains 'overflow' metabolisms observed during nutrient scarcity. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

A flexible state-space approach for the modeling of metabolic networks II: advanced interrogation of hybridoma metabolism

Having previously introduced the mathematical framework of topological metabolic analysis (TMA) - a novel optimization-based technique for modeling metabolic networks of arbitrary size and complexity - we demonstrate how TMA facilitates unique methods of metabolic interrogation. With the aid of several hybridoma metabolic investigations as case-studies (Bonarius et al., 1995, 1996, 2001), we first establish that the TMA framework identifies biologically important aspects of the metabolic network under investigation. We also show that the use of a structured weighting approach within our objective provides a substantial modeling benefit over an unstructured, uniform, weighting approach. We then illustrate the strength of TAM as an advanced interrogation technique, first by using TMA to prove the existence of (and to quantitatively describe) multiple topologically distinct configurations of a metabolic network that each optimally model a given set of experimental observations. We further show that such alternate topologies are indistinguishable using existing stoichiometric modeling techniques, and we explain the biological significance of the topological variables appearing within our model. By leveraging the manner in which TMA implements metabolite inputs and outputs, we also show that metabolites whose possible metabolic fates are inadequately described by a given network reconstruction can be quickly identified. Lastly, we show how the use of the TMA aggregate objective function (AOF) permits the identification of modeling solutions that can simultaneously consider experimental observations, underlying biological motivations, or even purely engineering- or design-based goals.     

FluxAnalyzer: exploring structure,pathways, and flux distributions in metabolic networks on interactive flux maps

MOTIVATION: The analysis of structure, pathways and flux distributions in metabolic networks has become an important approach for understanding the functionality of metabolic systems. The need of a user-friendly platform for stoichiometric modeling of metabolic networks in silico is evident. RESULTS: The FluxAnalyzer is a package for MATLAB and facilitates integrated pathway and flux analysis for metabolic networks within a graphical user interface. Arbitrary metabolic network models can be composed by instances of four types of network elements. The abstract network model is linked with network graphics leading to interactive flux maps which allow for user input and display of calculation results within a network visualization. Therein, a large and powerful collection of tools and algorithms can be applied interactively including metabolic flux analysis, flux optimization, detection of topological features and pathway analysis by elementary flux modes or extreme pathways. The FluxAnalyzer has been applied and tested for complex networks with more than 500,000 elementary modes. Some aspects of the combinatorial complexity of pathway analysis in metabolic networks are discussed. AVAILABILITY: Upon request from the corresponding author. Free for academic users (license agreement). Special contracts are available for industrial corporations. SUPPLEMENTARY INFORMATION: http://www.mpi-magdeburg.mpg.de/projects/fluxanalyzer.     

Topology of gene expression networks as revealed by data mining and modeling

MOTIVATION: Interpretation of high-throughput gene expression profiling requires a knowledge of the design principles underlying the networks that sustain cellular machinery. Recently a novel approach based on the study of network topologies has been proposed. This methodology has proven to be useful for the analysis of a variety of biological systems, including metabolic networks, networks of protein-protein interactions, and gene networks that can be derived from gene expression data. In the present paper, we focus on several important issues related to the topology of gene expression networks that have not yet been fully studied. RESULTS: The networks derived from gene expression profiles for both time series experiments in yeast and perturbation experiments in cell lines are studied. We demonstrate that independent from the experimental organism (yeast versus cell lines) and the type of experiment (time courses versus perturbations) the extracted networks have similar topological characteristics suggesting together with the results of other common principles of the structural organization of biological networks. A novel computational model of network growth that reproduces the basic design principles of the observed networks is presented. Advantage of the model is that it provides a general mechanism to generate networks with different types of topology by a variation of a few parameters. We investigate the robustness of the network structure to random damages and to deliberate removal of the most important parts of the system and show a surprising tolerance of gene expression networks to both kinds of disturbance.     

Basic concepts and principles of stoichiometric modeling of metabolic networks

Metabolic networks supply the energy and building blocks for cell growth and maintenance. Cells continuously rewire their metabolic networks in response to changes in environmental conditions to sustain fitness. Studies of the systemic properties of metabolic networks give insight into metabolic plasticity and robustness, and the ability of organisms to cope with different environments. Constraint-based stoichiometric modeling of metabolic networks has become an indispensable tool for such studies. Herein, we review the basic theoretical underpinnings of constraint-based stoichiometric modeling of metabolic networks. Basic concepts, such as stoichiometry, chemical moiety conservation, flux modes, flux balance analysis, and flux solution spaces, are explained with simple, illustrative examples. We emphasize the mathematical definitions and their network topological interpretations.     

Integrating gene expression and metabolic profiles

Recent advances in high throughput technologies have generated an abundance of biological information, such as gene expression, protein-protein interaction, and metabolic data. These various types of data capture different aspects of the cellular response to environmental factors. Integrating data from different measurements enhances the ability of modeling frameworks to predict cellular function more accurately and can lead to a more coherent reconstruction of the underlying regulatory network structure. Different techniques, newly developed and borrowed, have been applied for the purpose of extracting this information from experimental data. In this study, we developed a framework to integrate metabolic and gene expression profiles for a hepatocellular system. Specifically, we applied genetic algorithm and partial least square analysis to identify important genes relevant to a specific cellular function. We identified genes 1) whose expression levels quantitatively predict a metabolic function and 2) that play a part in regulating a hepatocellular function and reconstructed their role in the metabolic network. The framework 1) preprocesses the gene expression data using statistical techniques, 2) selects genes using a genetic algorithm and couples them to a partial least squares analysis to predict cellular function, and 3) reconstructs, with the assistance of a literature search, the pathways that regulate cellular function, namely intracellular triglyceride and urea synthesis. This provides a framework for identifying cellular pathways that are active as a function of the environment and in turn helps to uncover the interplay between gene and metabolic networks.