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.     

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。     

基于图论和约束优化的异源代谢途径设计方法及应用

构建高产高附加值产品的微生物细胞工厂是代谢工程的研究目标之一,设计高效的产品合成途径是实现这一目标的重要方式.不同微生物底盘因其代谢能力有所差异,故而可以利用的底物和生产的产品范围有限.为了扩大其生产能力,需要进行代谢途径从无到有的设计.传统代谢工程基于经验进行异源途径设计的方式低效且无法确保结果的全面性,而系统生物学...     

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...     

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。     

基因组尺度代谢网络研究进展

基因组尺度代谢网络从基因组序列出发,结合基因、蛋白质、代谢数据库和实验数据,从系统的角度定量研究生命体的代谢过程,了解各个组分之间的相互作用关系。这类网络模型对于生命活动理论研究和优良工程菌的构建都具有重要的理论和实践意义。以下结合作者的实际研究经验,对基因组尺度代谢网络从重构到模拟直至应用进行了较为详细的介绍,并讨论了一些目前存在的难题和未来的研究方向。     

系统生物学中建模方法的研究现状及展望

系统生物学倡导利用系统论的思想和方法,从整体的高度分析、研究生命的复杂特性。这一点与实验生物学仅关注某一个或者某一些生物大分子是迥然不同的。系统生物学既要同时考虑多个层次、多种类型的生物信息,还要考虑时间因素。由于系统特性是由于不同组成部分、不同层次间相互作用而“涌现”出的新性质,因此,如果只是针对组成部分或单一层次的分析并不能真正准确地预测整体或高层次的行为。如何通过研究和整合去发现和理解“涌现”出的新的系统性质,是系统生物学面临的一个根本性的挑战。为了应对这一挑战,系统生物学,特别是计算系统生物学必须建立有效的方法,通过整合系统各个层次的信息,建立可反映该系统目前已知或已可测量的性质的物理、数学模型,并通过这样的模型来研究或预测目前还未知晓的系统性状。可以说：建模是系统生物学的最重要的研究手段之一。目前,生命科学的研究正逐步由对单一现象、单一过程的机械论式的描述型研究转向运用高通量实验技术获取海量生物信息,并在这些生物信息基础上建立物理、数学模型,最终通过建模与实验相接合的研究手段来定量阐述生命现象的本质规律。由于建模方法在系统生物学研究中的重要性,本文将对一些主要的建模类型,如定性建模方法;基于约束的建模方法;基于常微分/偏微分方程的定量建模和基于随机微分方程的定量建模方法等等分别予以简要介绍。     

基于图论的代谢网络中流通代谢物处理新方法

使用图论方法对基因组尺度代谢网络进行途径搜索是目前途径设计中最常用的方法之一.然而,由于流通代谢物(如H+、H2O、CO2和ATP等)的影响,图论搜索得到的途径在生物学上经常是不可行的.虽然前人提出了一些方法对流通代谢物进行处理,但均存在一些问题,目前还没有标准的处理方法.文中基于流通代谢物在反应中作为转移磷酸等功能基...     

MetaGen: 从KEGG建模代谢网络的新工具

为便于大规模代谢网络的计算,发展了一款方便实用的工具:MetaGen,对Kyoto Encyclopedia of Genesand Genomes(KEGG)中物种特异的各层次代谢系统进行建模,生成的代谢网络以酶图和通路图的方式表示.利用该工具,对人类代谢系统的bow-tie结构进行了初步研究,并以此为例展示了该工具广阔的应用前景.MetaGen利用KEGGweb服务保证建模数据的可靠性,依靠本地关系数据库加速网络建模过程并提供更多的数据管理和利用方式,并结合高级JAVA技术提高代码的可扩展性.MetaGen完全开源,可直接从http://bnct.sourceforge.net/下载.     

Applications of genome‐scale metabolic reconstructions

The availability and utility of genome‐scale metabolic reconstructions have exploded since the first genome‐scale reconstruction was published a decade ago. Reconstructions have now been built for a wide variety of organisms, and have been used toward five major ends: (1) contextualization of high‐throughput data, (2) guidance of metabolic engineering, (3) directing hypothesis‐driven discovery, (4) interrogation of multi‐species relationships, and (5) network property discovery. In this review, we examine the many uses and future directions of genome‐scale metabolic reconstructions, and we highlight trends and opportunities in the field that will make the greatest impact on many fields of biology.     

The identification of similarities between biological networks: application to the metabolome and interactome

The increasing interest in systems biology has resulted in extensive experimental data describing networks of interactions (or associations) between molecules in metabolism, protein-protein interactions and gene regulation. Comparative analysis of these networks is central to understanding biological systems. We report a novel method (PHUNKEE: Pairing subgrapHs Using NetworK Environment Equivalence) by which similar subgraphs in a pair of networks can be identified. Like other methods, PHUNKEE explicitly considers the graphical form of the data and allows for gaps. However, it is novel in that it includes information about the context of the subgraph within the adjacent network. We also explore a new approach to quantifying the statistical significance of matching subgraphs. We report similar subgraphs in metabolic pathways and in protein-protein interaction networks. The most similar metabolic subgraphs were generally found to occur in processes central to all life, such as purine, pyrimidine and amino acid metabolism. The most similar pairs of subgraphs found in the protein-protein interaction networks of Drosophila melanogaster and Saccharomyces cerevisiae also include central processes such as cell division but, interestingly, also include protein sub-networks involved in pre-mRNA processing. The inclusion of network context information in the comparison of protein interaction networks increased the number of similar subgraphs found consisting of proteins involved in the same functional process. This could have implications for the prediction of protein function.     

Inferring meaningful pathways in weighted metabolic networks

An approach is presented for computing meaningful pathways in the network of small molecule metabolism comprising the chemical reactions characterized in all organisms. The metabolic network is described as a weighted graph in which all the compounds are included, but each compound is assigned a weight equal to the number of reactions in which it participates. Path finding is performed in this graph by searching for one or more paths with lowest weight. Performance is evaluated systematically by computing paths between the first and last reactions in annotated metabolic pathways, and comparing the intermediate reactions in the computed pathways to those in the annotated ones. For the sake of comparison, paths are computed also in the un-weighted raw (all compounds and reactions) and filtered (highly connected pool metabolites removed) metabolic graphs, respectively. The correspondence between the computed and annotated pathways is very poor (<30%) in the raw graph; increasing to approximately 65% in the filtered graph; reaching approximately 85% in the weighted graph. Considering the best-matching path among the five lightest paths increases the correspondence to 92%, on average. We then show that the average distance between pairs of metabolites is significantly larger in the weighted graph than in the raw unfiltered graph, suggesting that the small-world properties previously reported for metabolic networks probably result from irrelevant shortcuts through pool metabolites. In addition, we provide evidence that the length of the shortest path in the weighted graph represents a valid measure of the "metabolic distance" between enzymes. We suggest that the success of our simplistic approach is rooted in the high degree of specificity of the reactions in metabolic pathways, presumably reflecting thermodynamic constraints operating in these pathways. We expect our approach to find useful applications in inferring metabolic pathways in newly sequenced genomes.     

Redox homeostasis phenotypes in RubisCO-deficient Rhodobacter sphaeroides via ensemble modeling

Photosynthetic bacteria are capable of carrying out the fundamental biological processes of carbon dioxide assimilation and photosynthesis. In this work, ensemble modeling (EM) was used to examine the behavior of mutant strains of the nonsulfur purple photosynthetic bacterium Rhodobacter sphaeroides containing a blockage in the primary CO(2) assimilatory pathway, which is responsible for cellular redox balance. When the Calvin-Benson-Bassham (CBB) pathway is nonfunctional, spontaneous adaptive mutations have evolved allowing for the use of at least two separate alternative redox balancing routes enabling photoheterotrophic growth to occur. The first of these routes expresses the nitrogenase complex, even in the presence of normal repressing ammonia levels, dissipating excess reducing power via its inherent hydrogenase activity to produce large quantities of hydrogen gas. The second of these routes may dissipate excess reducing power through reduction of sulfate by the formation of hydrogen sulfide. EM was used here to investigate metabolism of R. sphaeroides and clearly shows that inactivation of the CBB pathway affects the organism's ability to achieve redox balance, which can be restored via the above-mentioned alternative redox routes. This work demonstrates that R. sphaeroides is capable of adapting alternative ways via mutation to dissipate excess reducing power when the CBB pathway is inactive, and that EM is successful in describing this behavior.     

From components to regulatory motifs in signalling networks.

The developments in biochemistry and molecular biology over the past 30 years have produced an impressive parts list of cellular components. It has become increasingly clear that we need to understand how components come together to form systems. One area where this approach has been growing is cell signalling research. Here, instead of focusing on individual or small groups of signalling proteins, researchers are now using a more holistic perspective. This approach attempts to view how many components are working together in concert to process information and to orchestrate cellular phenotypic changes. Additionally, the advancements in experimental techniques to measure and visualize many cellular components at once gradually grow in diversity and accuracy. The multivariate data, produced by experiments, introduce new and exciting challenges for computational biologists, who develop models of cellular systems made up of interacting cellular components. The integration of high-throughput experimental results and information from legacy literature is expected to produce computational models that would rapidly enhance our understanding of the detail workings of mammalian cells.