共查询到20条相似文献,搜索用时 15 毫秒
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Richard A Notebaart Frank HJ van Enckevort Christof Francke Roland J Siezen Bas Teusink 《BMC bioinformatics》2006,7(1):296
Background
The genomic information of a species allows for the genome-scale reconstruction of its metabolic capacity. Such a metabolic reconstruction gives support to metabolic engineering, but also to integrative bioinformatics and visualization. Sequence-based automatic reconstructions require extensive manual curation, which can be very time-consuming. Therefore, we present a method to accelerate the time-consuming process of network reconstruction for a query species. The method exploits the availability of well-curated metabolic networks and uses high-resolution predictions of gene equivalency between species, allowing the transfer of gene-reaction associations from curated networks. 相似文献2.
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《Current opinion in biotechnology》2013,24(2):271-277
Highlights► Recent advances have been made on plant genome-scale metabolic reconstruction. ► Cellular compartmentation makes plant genome-scale reconstruction challenging. ► Current reconstructions capture important features of plant metabolism. ► The models have been used to study isolated tissues and tissue interaction. ► We review the challenges and potential of plant reconstruction and modelling. 相似文献
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Background
Biochemically detailed stoichiometric matrices have now been reconstructed for various bacteria, yeast, and for the human cardiac mitochondrion based on genomic and proteomic data. These networks have been manually curated based on legacy data and elementally and charge balanced. Comparative analysis of these well curated networks is now possible. Pairs of metabolites often appear together in several network reactions, linking them topologically. This co-occurrence of pairs of metabolites in metabolic reactions is termed herein "metabolite coupling." These metabolite pairs can be directly computed from the stoichiometric matrix, S. Metabolite coupling is derived from the matrix ŜŜ T, whose off-diagonal elements indicate the number of reactions in which any two metabolites participate together, where Ŝ is the binary form of S. 相似文献6.
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Development of genome-scale metabolic models and various constraints-based flux analyses have enabled more sophisticated examination of metabolism. Recently reported metabolite essentiality studies are also based on the constraints-based modeling, but approaches metabolism from a metabolite-centric perspective, providing synthetic lethal combination of reactions and clues for the rational discovery of antibacterials. In this study, metabolite essentiality analysis was applied to the genome-scale metabolic models of four microorganisms: Escherichia coli, Helicobacter pylori, Mycobacterium tuberculosis and Staphylococcus aureus. Furthermore, chokepoints, metabolites surrounded by enzymes that uniquely consume and/or produce them, were also calculated based on the network properties of the above organisms. A systematic drug targeting strategy was developed by combining information from these two methods. Final drug target metabolites are presented and examined with knowledge from the literature. 相似文献
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In the post-genomic era, the biochemical information for individual compounds, enzymes, reactions to be found within named organisms has become readily available. The well-known KEGG and BioCyc databases provide a comprehensive catalogue for this information and have thereby substantially aided the scientific community. Using these databases, the complement of enzymes present in a given organism can be determined and, in principle, used to reconstruct the metabolic network. However, such reconstructed networks contain numerous properties contradicting biological expectation. The metabolic networks for a number of organisms are reconstructed from KEGG and BioCyc databases, and features of these networks are related to properties of their originating database. 相似文献
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Matthew DeJongh Kevin Formsma Paul Boillot John Gould Matthew Rycenga Aaron Best 《BMC bioinformatics》2007,8(1):139
Background
Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process. 相似文献11.
In the last decade, reconstruction and applications of genome-scale metabolic models have greatly influenced the field of systems biology by providing a platform on which high-throughput computational analysis of metabolic networks can be performed. The last two years have seen an increase in volume of more than 33% in the number of published genome-scale metabolic models, signifying a high demand for these metabolic models in studying specific organisms. The diversity in modeling different types of cells, from photosynthetic microorganisms to human cell types, also demonstrates their growing influence in biology. Here we review the recent advances and current state of genome-scale metabolic models, the methods employed towards ensuring high quality models, their biotechnological applications, and the progress towards the automated reconstruction of genome-scale metabolic models. 相似文献
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A genome-scale metabolic model of the Gram-positive bacteria Corynebacterium glutamicum ATCC 13032 was constructed comprising 446 reactions and 411 metabolites, based on the annotated genome and available biochemical information. The network was analyzed using constraint based methods. The model was extensively validated against published flux data, and flux distribution values were found to correlate well between simulations and experiments. The split pathway of the lysine synthesis pathway of C. glutamicum was investigated, and it was found that the direct dehydrogenase variant gave a higher lysine yield than the alternative succinyl pathway at high lysine production rates. The NADPH demand of the network was not found to be critical for lysine production until lysine yields exceeded 55% (mmol lysine (mmol glucose)(-1)). The model was validated during growth on the organic acids acetate and lactate. Comparable flux values between in silico model and experimental values were seen, although some differences in the phenotypic behavior between the model and the experimental data were observed. 相似文献
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高通量数据的产出为基因组尺度代谢网络的构建提供了基础,但同时也对网络构建和分析方法的改进提出了挑战。随着数据量的不断增大,耗时耗力的人工构建及分析已经无法满足模型发展的需要,因而各种自动化的方法应运而生。模型构建和分析的自动化不仅能够大幅度提高模型构建和解析的速度,同时对于模型构建和分析方法的标准化和程序化也有着不可替代的作用。文中结合作者的实际研究经验,对基因组尺度代谢网络构建的自动化进程和主要的代谢网络分析工具进行了较为详细的介绍,总结了代谢网络自动重构的流程,并提出了目前面对的主要问题和未来的研究方向。 相似文献
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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. 相似文献
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The integration of various types of genomic data into predictive models of biological networks is one of the main challenges currently faced by computational biology. Constraint-based models in particular play a key role in the attempt to obtain a quantitative understanding of cellular metabolism at genome scale. In essence, their goal is to frame the metabolic capabilities of an organism based on minimal assumptions that describe the steady states of the underlying reaction network via suitable stoichiometric constraints, specifically mass balance and energy balance (i.e. thermodynamic feasibility). The implementation of these requirements to generate viable configurations of reaction fluxes and/or to test given flux profiles for thermodynamic feasibility can however prove to be computationally intensive. We propose here a fast and scalable stoichiometry-based method to explore the Gibbs energy landscape of a biochemical network at steady state. The method is applied to the problem of reconstructing the Gibbs energy landscape underlying metabolic activity in the human red blood cell, and to that of identifying and removing thermodynamically infeasible reaction cycles in the Escherichia coli metabolic network (iAF1260). In the former case, we produce consistent predictions for chemical potentials (or log-concentrations) of intracellular metabolites; in the latter, we identify a restricted set of loops (23 in total) in the periplasmic and cytoplasmic core as the origin of thermodynamic infeasibility in a large sample (10(6)) of flux configurations generated randomly and compatibly with the prior information available on reaction reversibility. 相似文献
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