The reconstruction and analysis of tissue specific human metabolic networks

Human tissues have distinct biological functions. Many proteins/enzymes are known to be expressed only in specific tissues and therefore the metabolic networks in various tissues are different. Though high quality global human metabolic networks and metabolic networks for certain tissues such as liver have already been studied, a systematic study of tissue specific metabolic networks for all main tissues is still missing. In this work, we reconstruct the tissue specific metabolic networks for 15 main tissues in human based on the previously reconstructed Edinburgh Human Metabolic Network (EHMN). The tissue information is firstly obtained for enzymes from Human Protein Reference Database (HPRD) and UniprotKB databases and transfers to reactions through the enzyme-reaction relationships in EHMN. As our knowledge of tissue distribution of proteins is still very limited, we replenish the tissue information of the metabolic network based on network connectivity analysis and thorough examination of the literature. Finally, about 80% of proteins and reactions in EHMN are determined to be in at least one of the 15 tissues. To validate the quality of the tissue specific network, the brain specific metabolic network is taken as an example for functional module analysis and the results reveal that the function of the brain metabolic network is closely related with its function as the centre of the human nervous system. The tissue specific human metabolic networks are available at .     

Genome-scale models of bacterial metabolism: reconstruction and applications

Genome-scale metabolic models bridge the gap between genome-derived biochemical information and metabolic phenotypes in a principled manner, providing a solid interpretative framework for experimental data related to metabolic states, and enabling simple in silico experiments with whole-cell metabolism. Models have been reconstructed for almost 20 bacterial species, so far mainly through expert curation efforts integrating information from the literature with genome annotation. A wide variety of computational methods exploiting metabolic models have been developed and applied to bacteria, yielding valuable insights into bacterial metabolism and evolution, and providing a sound basis for computer-assisted design in metabolic engineering. Recent advances in computational systems biology and high-throughput experimental technologies pave the way for the systematic reconstruction of metabolic models from genomes of new species, and a corresponding expansion of the scope of their applications. In this review, we provide an introduction to the key ideas of metabolic modeling, survey the methods, and resources that enable model reconstruction and refinement, and chart applications to the investigation of global properties of metabolic systems, the interpretation of experimental results, and the re-engineering of their biochemical capabilities.     

Metabolic networks: enzyme function and metabolite structure

Metabolism is one of the most complex cellular processes. Connections between biochemical reactions via substrate and product metabolites create complex metabolic networks that may be analyzed using network theory, stoichiometric analysis, and information on protein structure/function and metabolite properties. These frameworks take into consideration different aspects of enzyme chemistry, enzyme structure and metabolite structure, and demonstrate the impact of metabolic biochemistry on the systemic properties of metabolism. The integration of these approaches and the systematic classification of enzyme function and the chemical structure of metabolites will enhance our understanding of metabolism, and could improve our ability to predict enzyme function and novel metabolic pathways.     

Constraints-based genome-scale metabolic simulation for systems metabolic engineering

Random mutagenesis and selection approaches used traditionally for the development of industrial strains have largely been complemented by metabolic engineering, which allows purposeful modification of metabolic and cellular characteristics by using recombinant DNA and other molecular biological techniques. As systems biology advances as a new paradigm of research thanks to the development of genome-scale computational tools and high-throughput experimental technologies including omics, systems metabolic engineering allowing modification of metabolic, regulatory and signaling networks of the cell at the systems-level is becoming possible. In silico genome-scale metabolic model and its simulation play increasingly important role in providing systematic strategies for metabolic engineering. The in silico genome-scale metabolic model is developed using genomic annotation, metabolic reactions, literature information, and experimental data. The advent of in silico genome-scale metabolic model brought about the development of various algorithms to simulate the metabolic status of the cell as a whole. In this paper, we review the algorithms developed for the system-wide simulation and perturbation of cellular metabolism, discuss the characteristics of these algorithms, and suggest future research direction.     

Metabolic network discovery through reverse engineering of metabolome data

Reverse engineering of high-throughput omics data to infer underlying biological networks is one of the challenges in systems biology. However, applications in the field of metabolomics are rather limited. We have focused on a systematic analysis of metabolic network inference from in silico metabolome data based on statistical similarity measures. Three different data types based on biological/environmental variability around steady state were analyzed to compare the relative information content of the data types for inferring the network. Comparing the inference power of different similarity scores indicated the clear superiority of conditioning or pruning based scores as they have the ability to eliminate indirect interactions. We also show that a mathematical measure based on the Fisher information matrix gives clues on the information quality of different data types to better represent the underlying metabolic network topology. Results on several datasets of increasing complexity consistently show that metabolic variations observed at steady state, the simplest experimental analysis, are already informative to reveal the connectivity of the underlying metabolic network with a low false-positive rate when proper similarity-score approaches are employed. For experimental situations this implies that a single organism under slightly varying conditions may already generate more than enough information to rightly infer networks. Detailed examination of the strengths of interactions of the underlying metabolic networks demonstrates that the edges that cannot be captured by similarity scores mainly belong to metabolites connected with weak interaction strength.     

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks

Background  

To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees.     

嗜黏蛋白阿克曼氏菌治疗疾病的潜力与作用机制研究进展

嗜黏蛋白阿克曼氏菌(Akkermansia muciniphila, AKK)可促进肠道黏液分泌,维持肠道黏液动态平衡,调节肠黏膜屏障功能,在机体代谢调节、免疫应答中发挥重要作用。AKK对肠道炎症、神经炎症、机体代谢紊乱和癌症等疾病具有显著改善作用,被视为极具潜力的下一代益生菌。本文分别从消化系统、神经系统、代谢性紊乱和癌症等角度入手,系统概述AKK在疾病治疗中的潜力及作用分子机制。     

PathAligner

MOTIVATION: Analysis of metabolic pathways is a central topic in understanding the relationship between genotype and phenotype. The rapid accumulation of biological data provides the possibility of studying metabolic pathways at both the genomic and the metabolic levels. Retrieving metabolic pathways from current biological data sources, reconstructing metabolic pathways from rudimentary pathway components, and aligning metabolic pathways with each other are major tasks. Our motivation was to develop a conceptual framework and computational system that allows the retrieval of metabolic pathway information and the processing of alignments to reveal the similarities between metabolic pathways. RESULTS: PathAligner extracts metabolic information from biological databases via the Internet and builds metabolic pathways with data sources of genes, sequences, enzymes, metabolites etc. It provides an easy-to-use interface to retrieve, display and manipulate metabolic information. PathAligner also provides an alignment method to compare the similarity between metabolic pathways. AVAILABILITY: PathAligner is available at http://bibiserv.techfak.uni-bielefeld.de/pathaligner.     

Evaluating metabolic stress and plasmid stability in plasmid DNA production by Escherichia coli

In the context of recombinant DNA technology, the development of feasible and high-yielding plasmid DNA production processes has regained attention as more evidence for its efficacy as vectors for gene therapy and DNA vaccination arise. When producing plasmid DNA in Escherichia coli, a number of biological restraints, triggered by plasmid maintenance and replication as well as culture conditions are responsible for limiting final biomass and product yields. This termed "metabolic burden" can also cause detrimental effects on plasmid stability and quality, since the cell machinery is no longer capable of maintaining an active metabolism towards plasmid synthesis and the stress responses elicited by plasmid maintenance can also cause increased plasmid instability. The optimization of plasmid DNA production bioprocesses is still hindered by the lack of information on the host metabolic responses as well as information on plasmid instability. Therefore, systematic and on-line approaches are required not only to characterise this "metabolic burden" and plasmid stability but also for the design of appropriate metabolic engineering and culture strategies. The monitoring tools described to date rapidly evolve from laborious, off-line and at-line monitoring to online monitoring, at a time-scale that enables researchers to solve these bioprocessing problems as they occur. This review highlights major E. coli biological alterations caused by plasmid maintenance and replication, possible causes for plasmid instability and discusses the ability of currently employed bioprocess monitoring techniques to provide information in order to circumvent metabolic burden and plasmid instability, pointing out the possible evolution of these methods towards online bioprocess monitoring.     

Information in morphological characters

The construction of morphological character matrices is central to paleontological systematic study, which extracts paleontological information from fossils. Although the word information has been repeatedly mentioned in a wide array of paleontological systematic studies, its meaning has rarely been clarified nor specifically defined. It is important, however, to establish a standard to measure paleontological information because fossils are hardly complete, rendering the recognition of homologous and homoplastic structures difficult. Here, based on information theory, we show the deep connections between paleontological systematic study and communication system engineering. Information is defined as the decrease of uncertainty and it is the information in morphological characters that allows distinguishing operational taxonomic units (OTUs) and reconstructing evolutionary history. We propose that concepts in communication system engineering such as source coding and channel coding, correspond to the construction of diagnostic features and the entire character matrices in paleontological studies. The two coding strategies should be distinguished following typical communication system engineering, because they serve dual purposes. With character matrices from six different vertebrate groups, we analyzed their information properties including source entropy, mutual information, and channel capacity. Estimation of channel capacity shows character saturation of all matrices in transmitting paleontological information, indicating that, due to the presence of noise, oversampling characters not only increases the burden in character scoring, but also may decrease quality of matrices. We further test the use of information entropy, which measures how informative a variable is, as a character weighting criterion in parsimony‐based systematic studies. The results show high consistency with existing knowledge with both good resolution and interpretability.     

Systems-level analysis of genome-scalein silico metabolic models using MetaFluxNet

The systems-level analysis of microbes with myriad of heterologous data generated by omics technologies has been applied to improve our understanding of cellular function and physiology and consequently to enhance production of various bioproducts. At the heart of this revolution residesin silico genome-scale metabolic model. In order to fully exploit the power of genome-scale model, a systematic approach employing user-friendly software is required. Metabolic flux analysis of genome-scale metabolic network is becoming widely employed to quantify the flux distribution and validate model-driven hypotheses. Here we describe the development of an upgraded MetaFluxNet which allows (1) construction of metabolic models connected to metabolic databases, (2) calculation of fluxes by metabolic flux analysis, (3) comparative flux analysis with flux-profile visualization, (4) the use of metabolic flux analysis markup language to enable models to be exchanged efficiently, and (5) the exporting of data from constraints-based flux analysis into various formats. MetaFluxNet also allows cellular physiology to be predicted and strategies for strain improvement to be developed from genome-based information on flux distributions. This integrated software environment promises to enhance our understanding on metabolic network at a whole organism level and to establish novel strategies for improving the properties of organisms for various biotechnological applications.     

Metabolic network modeling and simulation for drug targeting and discovery

Systems biology has greatly contributed toward the analysis and understanding of biological systems under various genotypic and environmental conditions on a much larger scale than ever before. One of the applications of systems biology can be seen in unraveling and understanding complicated human diseases where the primary causes for a disease are often not clear. The in silico genome-scale metabolic network models can be employed for the analysis of diseases and for the discovery of novel drug targets suitable for treating the disease. Also, new antimicrobial targets can be discovered by analyzing, at the systems level, the genome-scale metabolic network of pathogenic microorganisms. Such applications are possible as these genome-scale metabolic network models contain extensive stoichiometric relationships among the metabolites constituting the organism's metabolism and information on the associated biophysical constraints. In this review, we highlight applications of genome-scale metabolic network modeling and simulations in predicting drug targets and designing potential strategies in combating pathogenic infection. Also, the use of metabolic network models in the systematic analysis of several human diseases is examined. Other computational and experimental approaches are discussed to complement the use of metabolic network models in the analysis of biological systems and to facilitate the drug discovery pipeline.     

油料作物EST资源的生物信息学分析

利用生物信息学方法,收集整理GenBank数据库中截至2008年5月收录的油料作物油菜、花生、芝麻、大豆、向日葵、蓖麻、亚麻、棕榈等八种油料作物的表达序列标签(EST)序列信息,共获得1,185,911条EST序列,使用Crosmatch、RepeatMask-er、Phrap、CAP3、EMBOSS、Blast、EST-pipeline、ORF finder、Interproscan、blast2go、IdentiCS等软件,基于Linux操作系统,进行了综合及分类分析。共获得289,892条UniEST序列,通过以上对EST序列信息的基因注释信息,筛选出与油脂代谢相关的基因信息,并以此为基础构建了油料作物油脂代谢途径比较结构图。本研究为油料作物油脂代谢相关基因数据库的构建和不同油料作物油脂代谢异同的比较打下基础。     

Dynamic modeling of complex biological systems: a link between metabolic and macroscopic description

In this study, a class of dynamic models based on metabolic reaction pathways is analyzed, showing that systems with complex intracellular reaction networks can be represented by macroscopic reactions relating extracellular components only. Based on rigorous assumptions, the model reduction procedure is systematic and allows an equivalent 'input-output' representation of the system to be derived. The procedure is illustrated with a few examples.     

厦门市资源水与虚拟水耦合代谢效率评价

自然水循环与社会水循环的关联研究是制定系统性水代谢对策的前提。从水资源代谢过程与格局角度,用物质流分析法(MFA)剖析了亚热带季风气候雨影区缺水城市枯水年份资源水与虚拟水耦合代谢的路径、数量,提取代谢效率评价所需的过程与结构指标,以社会、经济、生态环境效益最优化为评价原则构建了城市水资源代谢效率评价指标体系,采用层次分析法赋权,评价了近10年来枯水年份厦门市资源水与虚拟水耦合代谢效率。结果表明,厦门水资源代谢效率呈加速提高趋势,主要由用水效率类和水代谢过程与结构类指标驱动。说明提高水资源代谢效率的根本对策在于用水行为和用水过程与结构的改善。采用情景分析法设计主导驱动指标不同组合下水资源代谢效率情景方案,提高了方案的可操作性,可基于这些指标制定水资源管理对策。基于MFA结果提取指标丰富了指标体系构建理论。     

GEDA: new knowledge base of gene expression in drug addiction

Abuse of drugs can elicit compulsive drug seeking behaviors upon repeated administration, and ultimately leads to the phenomenon of addiction. We developed a procedure for the standardization of microarray gene expression data of rat brain in drug addiction and stored them in a single integrated database system, focusing on more effective data processing and interpretation. Another characteristic of the present database is that it has a systematic flexibility for statistical analysis and linking with other databases. Basically, we adopt an intelligent SQL querying system, as the foundation of our DB, in order to set up an interactive module which can automatically read the raw gene expression data in the standardized format. We maximize the usability of this DB, helping users study significant gene expression and identify biological function of the genes through integrated up-to-date gene information such as GO annotation and metabolic pathway. For collecting the latest information of selected gene from the database, we also set up the local BLAST search engine and nonredundant sequence database updated by NCBI server on a daily basis. We find that the present database is a useful query interface and data-mining tool, specifically for finding out the genes related to drug addiction. We apply this system to the identification and characterization of methamphetamine-induced genes' behavior in rat brain.     

The strength of chemical linkage as a criterion for pruning metabolic graphs

MOTIVATION: A metabolic graph represents the connectivity patterns of a metabolic system, and provides a powerful framework within which the organization of metabolic reactions can be analyzed and elucidated. A common practice is to prune (i.e. remove nodes and edges) the metabolic graph prior to any analysis in order to eliminate confounding signals from the representation. Currently, this pruning process is carried out in an ad hoc fashion, resulting in discrepancies and ambiguities across studies. RESULTS: We propose a biochemically informative criterion, the strength of chemical linkage (SCL), for a systematic pruning of metabolic graphs. By analyzing the metabolic graph of Escherichia coli, we show that thresholding SCL is powerful in selecting the conventional pathways' connectivity out of the raw network connectivity when the network is restricted to the reactions collected from these pathways. Further, we argue that the root of ambiguity in pruning metabolic graphs is in the continuity of the amount of chemical content that can be conserved in reaction transformation patterns. Finally, we demonstrate how biochemical pathways can be inferred efficiently if the search procedure is guided by SCL.     

Adipocyte plasma-membrane Gi and Gs in insulinopenic diabetic patients.

The matrix method for calculating the overall sensitivities (including control coefficients) of a metabolic system, described by Crabtree & Newsholme [Biochem. J. 247, 113-129 (1987)], is simplified by a preliminary partitioning of the initial matrix equation. This reduces the size of the matrix to be inverted and thereby removes a major drawback with the original method. The resulting procedure is simpler and more systematic than the alternative methods currently available, especially when the system is extensively branched.     

Pathway analysis in metabolic databases via differential metabolic display (DMD)

MOTIVATION: A number of metabolic databases are available electronically, some with features for querying and visualizing metabolic pathways and regulatory networks. We present a unifying, systematic approach based on PETRI nets for storing, displaying, comparing, searching and simulating such nets from a number of different sources. RESULTS: Information from each data source is extracted and compiled into a PETRI net. Such PETRI nets then allow to investigate the (differential) content in metabolic databases, to map and integrate genomic information and functional annotations, to compare sequence and metabolic databases with respect to their functional annotations, and to define, generate and search paths and pathways in nets. We present an algorithm to systematically generate all pathways satisfying additional constraints in such PETRI nets. Finally, based on the set of valid pathways, so-called differential metabolic displays (DMDs) are introduced to exhibit specific differences between biological systems, i.e. different developmental states, disease states, or different organisms, on the level of paths and pathways. DMDs will be useful for target finding and function prediction, especially in the context of the interpretation of expression data.