In VivoDynamics of the Pentose Phosphate Pathway inSaccharomyces cerevisiae

Vaseghi, Sam; Baumeister, Anja; Rizzi, Manfred; Reuß, Matthias

doi:10.1006/mben.1998.0110

Cited by 140 publications

(136 citation statements)

References 31 publications

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“…These methods have been widely used in the extraction of metabolites from animal and plant tissues (Shryock et al, 1986;Kopka et al, 1995;Chen et al, 1998;Adams et al, 1999;Bouchereau et al, 2000;Conneely et al, 2002) and microorganisms (Cordeiro and Freire, 1996;Weuster-Botz, 1997;Hajjaj et al, 1998;Schaefer et al, 1999;Vaseghi et al, 1999;Letisse and Lindley, 2000;Martins et al, 2001;Buchholz et al, 2002;Buziol et al, 2002;Maharjan and Ferenci, 2003). Acidic extraction is commonly used for the extraction of nucleotides and water-soluble metabolites (Vaseghi et al, 1999;Martins et al, 2001;Buchholz et al, 2002), but for metabolome studies it is evident that not all metabolites are stable at the extreme pH, and destruction of some nucleotides has been reported during acidic extractions (Hajjaj et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Global metabolite analysis of yeast: evaluation of sample preparation methods

Villas‐Bôas

Højer-Pedersen

Åkesson

et al. 2005

Yeast

364

363

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Sample preparation is considered one of the limiting steps in microbial metabolome analysis. Eukaryotes and prokaryotes behave very differently during the several steps of classical sample preparation methods for analysis of metabolites. Even within the eukaryote kingdom there is a vast diversity of cell structures that make it imprudent to blindly adopt protocols that were designed for a specific group of microorganisms. We have therefore reviewed and evaluated the whole sample preparation procedures for analysis of yeast metabolites. Our focus has been on the current needs in metabolome analysis, which is the analysis of a large number of metabolites with very diverse chemical and physical properties. This work reports the leakage of intracellular metabolites observed during quenching yeast cells with cold methanol solution, the efficacy of six different methods for the extraction of intracellular metabolites, and the losses noticed during sample concentration by lyophilization and solvent evaporation. A more reliable procedure is suggested for quenching yeast cells with cold methanol solution, followed by extraction of intracellular metabolites by pure methanol. The method can be combined with reduced pressure solvent evaporation and therefore represents an attractive sample preparation procedure for high-throughput metabolome analysis of yeasts.

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Section: Introductionmentioning

confidence: 99%

Global metabolite analysis of yeast: evaluation of sample preparation methods

Villas‐Bôas

Højer-Pedersen

Åkesson

et al. 2005

Yeast

364

363

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“…These #ux distributions represent phenotypes which may be exhibited by a reconstructed metabolic network. di$cult to obtain under all possible environmental conditions, although some progress has been made (Vaseghi et al, 1999). For this reason, none of the above models has been able to be adopted on a scale large enough to represent the entire metabolism of an organism, as recently observed (Bailey, 2001), and thus the systemic regulatory/ metabolic properties of an entire organism have yet to be analysed.…”

Section: Introductionmentioning

confidence: 99%

Regulation of Gene Expression in Flux Balance Models of Metabolism

Covert

Schilling

Palsson

2001

Journal of Theoretical Biology

386

350

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Genome-scale metabolic networks can now be reconstructed based on annotated genomic data augmented with biochemical and physiological information about the organism. Mathematical analysis can be performed to assess the capabilities of these reconstructed networks. The constraints-based framework, with #ux balance analysis (FBA), has been used successfully to predict time course of growth and by-product secretion, e!ects of mutation and knock-outs, and gene expression pro"les. However, FBA leads to incorrect predictions in situations where regulatory e!ects are a dominant in#uence on the behavior of the organism. Thus, there is a need to include regulatory events within FBA to broaden its scope and predictive capabilities. Here we represent transcriptional regulatory events as time-dependent constraints on the capabilities of a reconstructed metabolic network to further constrain the space of possible network functions. Using a simpli"ed metabolic/regulatory network, growth is simulated under various conditions to illustrate systemic e!ects such as catabolite repression, the aerobic/anaerobic diauxic shift and amino acid biosynthesis pathway repression. The incorporation of transcriptional regulatory events in FBA enables us to interpret, analyse and predict the e!ects of transcriptional regulation on cellular metabolism at the systemic level.

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“…A tremendous amount of experimental data has been and continues to be generated for S. cerevisiae (17)(18)(19). A number of databases have been dedicated to store and update yeast experimental data sets, and several mathematical models are available that use kinetic information to capture cell behaviors (20)(21)(22)(23). Limited availability of kinetic information restricts such models to a subset of the whole cell.…”

mentioning

confidence: 99%

Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network

Famili

Förster

Nielsen

et al. 2003

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

336

244

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Full genome sequences of prokaryotic organisms have led to reconstruction of genome-scale metabolic networks and in silico computation of their integrated functions. The first genome-scale metabolic reconstruction for a eukaryotic cell, Saccharomyces cerevisiae, consisting of 1,175 metabolic reactions and 733 metabolites, has appeared. A constraint-based in silico analysis procedure was used to compute properties of the S. cerevisiae metabolic network. The computed number of ATP molecules produced per pair of electrons donated to the electron transport system (ETS) and energy-maintenance requirements were quantitatively in agreement with experimental results. Computed whole-cell functions of growth and metabolic by-product secretion in aerobic and anaerobic culture were consistent with experimental data, and thus mRNA expression profiles during metabolic shifts were computed. The computed consequences of gene knockouts on growth phenotypes were consistent with experimental observations. Thus, constraint-based analysis of a genome-scale metabolic network for the eukaryotic S. cerevisiae allows for computation of its integrated functions, producing in silico results that were consistent with observed phenotypic functions for Ϸ70 -80% of the conditions considered. S ystems biology is commonly viewed as a four-step procedure (1-3): (i) the enumeration of the biological components that make up the biological process of interest, (ii) the reconstruction of the network of interactions among these components, (iii) the in silico simulation of the network function, and (iv) the comparison of computed network properties with actual phenotypic observations. A wealth of available biological data for Saccharomyces cerevisiae (4-7) led to the establishment of the first genome-scale reconstruction of the metabolic network in a eukaryotic cell (8). The initial completion of the first two steps of the systems biology procedure for yeast metabolism has been achieved. Steps iii and iv have not yet been carried out for a eukaryotic organism.Integrated functions of reconstructed metabolic networks can be determined in silico by using a number of analytical approaches (9-11). The relatively young constraint-based approach differs fundamentally from the more traditional kinetic theory-based approaches in that it is not aimed at finding the solution or behavior of the network under certain conditions but rather at eliminating solutions (behaviors) that the network cannot exhibit (Fig. 1). By using this approach, a network of interactions is successively constrained by defining the stoichiometry of the interacting components, the direction of network reactions, and the maximum allowable throughput. In this way, candidate solutions to the network equations are systematically eliminated by the successive application of the governing constraints, i.e., stoichiometry, thermodynamics, and maximal enzymatic rates (12). One thus can define the range of capabilities of the reconstructed network and then, through the use of optimization procedur...

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In VivoDynamics of the Pentose Phosphate Pathway inSaccharomyces cerevisiae

Cited by 140 publications

References 31 publications

Global metabolite analysis of yeast: evaluation of sample preparation methods

Global metabolite analysis of yeast: evaluation of sample preparation methods

Regulation of Gene Expression in Flux Balance Models of Metabolism

Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network

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