METATOOL: for studying metabolic networks.

Pfeiffer, Thomas; Sánchez-Valdenebro, Ignacio; Nuño, Juan Carlos; Montero, Francisco; Schuster, Stefan

doi:10.1093/bioinformatics/15.3.251

Cited by 326 publications

(278 citation statements)

References 23 publications

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“…In 86% of the cases studied, the effects of gene knockouts in E. coli could be shown correctly by growth rate data of E. coli mutants reported in the literature. In a series of related papers, Pfeiffer et al (1999), Schuster (1999) and Schuster et al (1999Schuster et al ( , 2000 described the concept of elementary flux mode. Biochemical pathways were rationalized into thermodynamically and stoichiometrically feasible subsets of enzymes that each generate valid steady states.…”

Section: Modelling Based On Biochemical Stoichiometrymentioning

confidence: 99%

Metabolomics — the link between genotypes and phenotypes

Fiehn

2002

Functional Genomics

1,624

1,337

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Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and 'proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.

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Section: Modelling Based On Biochemical Stoichiometrymentioning

confidence: 99%

Metabolomics — the link between genotypes and phenotypes

Fiehn

2002

Functional Genomics

1,624

1,337

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“…Some of these reactions can be further grouped into enzyme subsets (Table V). Enzyme subsets are groups of reactions that at steady state always operate in fixed flux proportions (Pfeiffer et al, 1999). Prior to the determination of subsets, we removed 202 reactions from bna572 that never carry flux in any simulations within phases II and III.…”

Section: Tag/protein Tradeoff Phase Diagrams Show Three Main Metabolimentioning

confidence: 99%

“…Enzyme subsets (Pfeiffer et al, 1999) were computed using CellNetAnalyzer (Klamt et al, 2007) version 9.8 based on the SBML version of bna572 published earlier (Hay and Schwender, 2011b). Prior to the determination of subsets, 202 reactions that never carry flux in any simulations within phases II and III (Supplemental Table S1) were removed from the network.…”

Section: Computation Of Enzyme Subsetsmentioning

confidence: 99%

Predictive Modeling of Biomass Component Tradeoffs in Brassica napus Developing Oilseeds Based on in Silico Manipulation of Storage Metabolism

Schwender

Hay

2012

Plant Physiology

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Seed oil content is a key agronomical trait, while the control of carbon allocation into different seed storage compounds is still poorly understood and hard to manipulate. Using bna572, a large-scale model of cellular metabolism in developing embryos of rapeseed (Brassica napus) oilseeds, we present an in silico approach for the analysis of carbon allocation into seed storage products. Optimal metabolic flux states were obtained by flux variability analysis based on minimization of the uptakes of substrates in the natural environment of the embryo. For a typical embryo biomass composition, flux sensitivities to changes in different storage components were derived. Upper and lower flux bounds of each reaction were categorized as oil or protein responsive. Among the most oil-responsive reactions were glycolytic reactions, while reactions related to mitochondrial ATP production were most protein responsive. To assess different biomass compositions, a tradeoff between the fractions of oil and protein was simulated. Based on flux-bound discontinuities and shadow prices along the tradeoff, three main metabolic phases with distinct pathway usage were identified. Transitions between the phases can be related to changing modes of the tricarboxylic acid cycle, reorganizing the usage of organic carbon and nitrogen sources for protein synthesis and acetylcoenzyme A for cytosol-localized fatty acid elongation. The phase close to equal oil and protein fractions included an unexpected pathway bypassing a-ketoglutarate-oxidizing steps in the tricarboxylic acid cycle. The in vivo relevance of the findings is discussed based on literature on seed storage metabolism.

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“…METATOOL 4.9.2 in double precision by Pfeiffer et al 27 was downloaded from the website 28 and used to find all elementary flux modes of the more detailed networks. The text input file includes reversibility/ irreversibility conditions of all the reactions as well as that of all the intra/extracellular components shown in Table 3.…”

Section: Elementary Flux Mode Analysismentioning

confidence: 99%

A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

Kim

Varner

Ramkrishna

2008

Biotechnology Progress

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Flux balance analysis (FBA) in combination with the decomposition of metabolic networks into elementary modes has provided a route to modeling cellular metabolism. It is dependent, however, on the availability of external fluxes such as substrate uptake or growth rate before estimates can become available of intracellular fluxes. The framework classically does not allow modeling of metabolic regulation or the formulation of dynamic models except through dynamic measurement of external fluxes. The cybernetic modeling approach of Ramkrishna and coworkers provides a dynamic framework for modeling metabolic systems because of its focus on describing regulatory processes based on cybernetic arguments and hence has the capacity to describe both external and internal fluxes. In this article, we explore the alternative of developing hybrid models combining cybernetic models for the external fluxes with the flux balance approach for estimation of the internal fluxes. The approach has the merit of the simplicity of the early cybernetic models and hence computationally facile while also providing detailed information on intracellular fluxes. The hybrid model of this article is based on elementary mode decomposition of the metabolic network. The uptake rates for the various elementary modes are combined using global cybernetic variables based on maximizing substrate uptake rates. Estimation of intracellular metabolism is based on its stoichiometric coupling with the external fluxes under the assumption of (pseudo-) steady state conditions. The set of parameters of the hybrid model was estimated with the aid of nonlinear optimization routine, by fitting simulations with dynamic experimental data on concentrations of biomass, substrate, and fermentation products. The hybrid model estimations were tested with FBA (based on measured substrate uptake rate) for two different metabolic networks (one is a reduced network which fixes ATP contribution to the biomass and maintenance requirement of ATP, and the other network is a more complex network which has a separate reaction for maintenance.) for the same experiment involving anaerobic growth of E. coli GJT001. The hybrid model estimated glucose consumption and all fermentation byproducts to better than 10%. The FBA makes similar estimations of fermentation products, however, with the exception of succinate. The simulation results show that the global cybernetic variables alone can regulate the metabolic reactions obtaining a very satisfactory fit to the measured fermentation byproducts. In view of the hybrid model's ability to predict biomass growth and fermentation byproducts of anaerobic E. coli GJT001, this reduced order model offers a computationally efficient alternative to more detailed models of metabolism and hence useful for the simulation of bioreactors.

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METATOOL: for studying metabolic networks.

Cited by 326 publications

References 23 publications

Metabolomics — the link between genotypes and phenotypes

Metabolomics — the link between genotypes and phenotypes

Predictive Modeling of Biomass Component Tradeoffs in Brassica napus Developing Oilseeds Based on in Silico Manipulation of Storage Metabolism

A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

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