Metabolic capacity ofBacillus subtilis for the production of purine nucleosides, riboflavin, and folic acid

Sauer, Uwe; Cameron, Douglas C.; Bailey, James E.

doi:10.1002/(sici)1097-0290(19980720)59:2<227::aid-bit10>3.0.co;2-b

Cited by 86 publications

(21 citation statements)

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“…The net result was a biochemically and genetically detailed in silico model that consists of 1020 metabolic reactions that are catalyzed by 844 ORFs (ϳ20% of the genes in B. subtilis). For B. subtilis, a stoichiometric model consisting of around 35 reactions in the central metabolism and the purine metabolism has been constructed previously (13,14). However, with the genome-scale model, it was possible to compute not only quantitatively aerobic growth of B. subtilis, but also qualitatively its phenotypic behaviors for substrate utilization in 74% agreement (200 in 271 cases) and the outcome of single-gene deletions with 94% agreement (720 in 766 cases).…”

Section: Discussionmentioning

confidence: 99%

“…E. coli (11)), and eukarya (i.e. Saccharomyces cerevisiae (12)).For B. subtilis, a stoichiometric model consisting of around 35 reactions in the central metabolism and the purine metabolism has been constructed previously (13,14). The model has been used to obtain information on the intracellular flux distribution based on fractional carbon isotope labeling (15) and for the estimation of the P/O ratio (16) and the prediction of maximum theoretical yields of commercial biochemical products (14).…”

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confidence: 99%

“…The model has been used to obtain information on the intracellular flux distribution based on fractional carbon isotope labeling (15) and for the estimation of the P/O ratio (16) and the prediction of maximum theoretical yields of commercial biochemical products (14). Here we describe the reconstruction of a highly detailed and validated genome-scale metabolic network for B. subtilis, which reconciles established genomic and physiological data with high-throughput phenotyping data generated herein.…”

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Genome-scale Reconstruction of Metabolic Network in Bacillus subtilis Based on High-throughput Phenotyping and Gene Essentiality Data

Oh¹,

Palsson²,

Park

et al. 2007

Journal of Biological Chemistry

396

356

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In this report, a genome-scale reconstruction of Bacillus subtilis metabolism and its iterative development based on the combination of genomic, biochemical, and physiological information and high-throughput phenotyping experiments is presented. The initial reconstruction was converted into an in silico model and expanded in a four-step iterative fashion. First, network gap analysis was used to identify 48 missing reactions that are needed for growth but were not found in the genome annotation. Second, the computed growth rates under aerobic conditions were compared with high-throughput phenotypic screen data, and the initial in silico model could predict the outcomes qualitatively in 140 of 271 cases considered. Detailed analysis of the incorrect predictions resulted in the addition of 75 reactions to the initial reconstruction, and 200 of 271 cases were correctly computed. Third, in silico computations of the growth phenotypes of knock-out strains were found to be consistent with experimental observations in 720 of 766 cases evaluated. Fourth, the integrated analysis of the large-scale substrate utilization and gene essentiality data with the genomescale metabolic model revealed the requirement of 80 specific enzymes (transport, 53; intracellular reactions, 27) that were not in the genome annotation. Subsequent sequence analysis resulted in the identification of genes that could be putatively assigned to 13 intracellular enzymes. The final reconstruction accounted for 844 open reading frames and consisted of 1020 metabolic reactions and 988 metabolites. Hence, the in silico model can be used to obtain experimentally verifiable hypothesis on the metabolic functions of various genes.Bacillus subtilis has been the organism of choice for the production of several important industrial products, including antibiotics, enzymes, nucleosides, and vitamins. Several aspects of the biochemistry, genetics, and physiology of B. subtilis have been studied extensively making B. subtilis the best characterized prokaryote second only to Escherichia coli (1, 2). In addition various "-omics" data sets such as transcriptomic (3), proteomic (4), and metabolomic (5) are available for B. subtilis. This wealth of experimental data enables the development of a genome-scale metabolic in silico model that can be used not only for quantitative interpretation and structured integration of such extensive data sets but also as a tool for hypothesis generation and engineering of B. subtilis metabolism.To date, constraint-based reconstruction and analysis (COBRA) 4 of cellular metabolism has been employed successfully to develop organism-specific genome-scale in silico models that have enabled numerous applications (6). Unlike other modeling strategies such as kinetic (7), stochastic (8), and cybernetic (9) methods, the COBRA approach does not attempt to compute precisely what a biochemical network does; rather, it seeks to distinguish between the network states that are achievable from those that are not, based on a detailed reconstruction of...

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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Genome-scale Reconstruction of Metabolic Network in Bacillus subtilis Based on High-throughput Phenotyping and Gene Essentiality Data

Oh¹,

Palsson²,

Park

et al. 2007

Journal of Biological Chemistry

396

356

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“…The steady-state analysis is based on the constraints imposed on the metabolic network by the stoichiometry of the metabolic reactions, which basically represent mass balance constraints. The steady-state analysis of metabolic networks based on the mass balance constraints is known as FBA (7,18,19). This analysis differs from detailed kinetic modeling of cellular processes, in that it does not attempt to predict the exact behavior of metabolic networks.…”

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The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities

Edwards

Palsson

2000

Proc. Natl. Acad. Sci. U.S.A.

807

683

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The Escherichia coli MG1655 genome has been completely sequenced. The annotated sequence, biochemical information, and other information were used to reconstruct the E. coli metabolic map. The stoichiometric coefficients for each metabolic enzyme in the E. coli metabolic map were assembled to construct a genomespecific stoichiometric matrix. The E. coli stoichiometric matrix was used to define the system's characteristics and the capabilities of E. coli metabolism. The effects of gene deletions in the central metabolic pathways on the ability of the in silico metabolic network to support growth were assessed, and the in silico predictions were compared with experimental observations. It was shown that based on stoichiometric and capacity constraints the in silico analysis was able to qualitatively predict the growth potential of mutant strains in 86% of the cases examined. Herein, it is demonstrated that the synthesis of in silico metabolic genotypes based on genomic, biochemical, and strain-specific information is possible, and that systems analysis methods are available to analyze and interpret the metabolic phenotype.bioinformatics ͉ metabolism ͉ genotype-phenotype relation ͉ flux balance analysis T he complete genome sequence for a number of microorganisms has been established (The Institute for Genomic Research at www.tigr.org). The genome sequencing efforts and the subsequent bioinformatic analyses have defined the molecular ''parts catalogue'' for a number of living organisms. However, it is evident that cellular functions are multigeneic in nature, thus one must go beyond a molecular parts catalogue to elucidate integrated cellular functions based on the molecular cellular components (1). Therefore, to analyze the properties and the behavior of complex cellular networks, one needs to use methods that focus on the systemic properties of the network. Approaches to analyze, interpret, and ultimately predict cellular behavior based on genomic and biochemical data likely will involve bioinformatics and computational biology and form the basis for subsequent bioengineering analysis.In moving toward the goal of developing an integrated description of cellular processes, it should be recognized that there exists a history of studying the systemic properties of metabolic networks (2) and many mathematical methods have been developed to carry out such studies. These methods include approaches such as metabolic control analysis (3, 4), flux balance analysis (FBA) (5-7), metabolic pathway analysis (8-11, 69), cybernetic modeling (12), biochemical systems theory (13), temporal decomposition (14), and so on. Although many mathematical methods and approaches have been developed, there are few comprehensive metabolic systems for which detailed kinetic information is available and where such detailed analysis can be carried out (see refs. 15-17 for a few noteworthy exceptions).To analyze, interpret, and predict cellular behavior, each individual step in a biochemical network must be described, normally with a rate equati...

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“…Dynamic batch-culture growth simulations (8,9) have been performed by using constraint-based analysis (10)(11)(12)(13)(14). Constraint-based analysis has been used to assess optimal growth states (15), consequences of gene knockouts (16)(17)(18), endpoints of adaptive evolution (19), synthetic lethals (20), epistatic interactions between metabolic genes (21), and enzyme dispensability (22).…”

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The global transcriptional regulatory network for metabolism inEscherichia coliexhibits few dominant functional states

Barrett

Herring

Reed

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

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A principal aim of systems biology is to develop in silico models of whole cells or cellular processes that explain and predict observable cellular phenotypes. Here, we use a model of a genome-scale reconstruction of the integrated metabolic and transcriptional regulatory networks for Escherichia coli, composed of 1,010 gene products, to assess the properties of all functional states computed in 15,580 different growth environments. The set of all functional states of the integrated network exhibits a discernable structure that can be visualized in 3-dimensional space, showing that the transcriptional regulatory network governing metabolism in E. coli responds primarily to the available electron acceptor and the presence of glucose as the carbon source. This result is consistent with recently published experimental data. The observation that a complex network composed of 1,010 genes is organized to achieve few dominant modes demonstrates the utility of the systems approach for consolidating large amounts of genome-scale molecular information about a genome and its regulation to elucidate an organism's preferred environments and functional capabilities.systems biology ͉ transcriptional regulation T wo prominent paradigms in modern biology about which there has been much discussion are the reductionist and integrative (or systems) approaches to biological discovery and understanding (1-5). The utility of the systems approach is demonstrated when computational models are used to integrate large amounts of molecular data to interpret and predict the range of cellular phenotypes. We investigated the utility of the systems approach by using a genome-scale reconstruction of the integrated transcriptional regulatory and metabolic network for Escherichia coli (iMC1010 v1

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Metabolic capacity ofBacillus subtilis for the production of purine nucleosides, riboflavin, and folic acid

Cited by 86 publications

References 38 publications

Genome-scale Reconstruction of Metabolic Network in Bacillus subtilis Based on High-throughput Phenotyping and Gene Essentiality Data

Genome-scale Reconstruction of Metabolic Network in Bacillus subtilis Based on High-throughput Phenotyping and Gene Essentiality Data

The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities

The global transcriptional regulatory network for metabolism inEscherichia coliexhibits few dominant functional states

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