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Open AccessJournal ArticleDOI

A Genome-Scale Metabolic Model of Arabidopsis and Some of Its Properties

TLDR
Using techniques based on linear programming, the construction and analysis of a genome-scale metabolic model of Arabidopsis (Arabidopsis thaliana) primarily derived from the annotations in the Aracyc database is described.
Abstract
We describe the construction and analysis of a genome-scale metabolic model of Arabidopsis (Arabidopsis thaliana) primarily derived from the annotations in the Aracyc database. We used techniques based on linear programming to demonstrate the following: (1) that the model is capable of producing biomass components (amino acids, nucleotides, lipid, starch, and cellulose) in the proportions observed experimentally in a heterotrophic suspension culture; (2) that approximately only 15% of the available reactions are needed for this purpose and that the size of this network is comparable to estimates of minimal network size for other organisms; (3) that reactions may be grouped according to the changes in flux resulting from a hypothetical stimulus (in this case demand for ATP) and that this allows the identification of potential metabolic modules; and (4) that total ATP demand for growth and maintenance can be inferred and that this is consistent with previous estimates in prokaryotes and yeast.

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Journal ArticleDOI

Not just a circle: flux modes in the plant TCA cycle

TL;DR: Alternative, non-cyclic flux modes occur in leaves in the light, in some developing oilseeds, and under specific physiological circumstances such as anoxia.
Journal ArticleDOI

eQuilibrator—the biochemical thermodynamics calculator

TL;DR: The eQuilibrator database couples a comprehensive and accurate database of thermodynamic properties of biochemical compounds and reactions with a simple and powerful online search and calculation interface.
Journal ArticleDOI

Current status and applications of genome-scale metabolic models.

TL;DR: Current reconstructed GEMs are reviewed and discussed, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.
Journal ArticleDOI

Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism

TL;DR: A new algorithm for rapid reconstruction of tissue‐specific genome‐scale models of human metabolism is presented, which is verified using standard cross‐validation procedures, and constructed the first genome-scale stoichiometric model of hepatic metabolism.
Journal ArticleDOI

AraGEM, a Genome-Scale Reconstruction of the Primary Metabolic Network in Arabidopsis

TL;DR: AraGEM is a viable framework for in silico functional analysis and can be used to derive new, nontrivial hypotheses for exploring plant metabolism.
References
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Journal ArticleDOI

A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding

TL;DR: This assay is very reproducible and rapid with the dye binding process virtually complete in approximately 2 min with good color stability for 1 hr with little or no interference from cations such as sodium or potassium nor from carbohydrates such as sucrose.
Journal ArticleDOI

Lipid extraction of tissues with a low-toxicity solvent

TL;DR: An improved method for extracting the lipids from tissues consists of the use of hexane:isopropanol, followed by a wash of the extract with aqueous sodium sulfate to remove nonlipid contaminants.
Book

The Regulation of Cellular Systems

TL;DR: The basic equations of metabolic control analysis are rewritten in terms of co-response coefficients and internal response coefficients to describe the interaction of optimization methods and the interrelation with evolution.
Journal ArticleDOI

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

TL;DR: 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.
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