Use of network analysis of metabolic systems in bioengineering

Schuster, Stefan; Klamt, Steffen; Weckwerth, Wolfram; Moldenhauer, F.

doi:10.1007/s004490100253

Cited by 55 publications

(2 citation statements)

References 45 publications

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“…The first important drawback of EFPs is that they cannot be used in place of EMs in certain applications [9], where the precise flux values are required. For example, in the identification of all pathways with optimal yield [23,39] and in the analysis of control-effective fluxes [27,28,40], the flux values of the respective reactions in the EMs should be taken into account.…”

Section: The State Of the Artmentioning

confidence: 99%

Analysis of Metabolic Subnetworks by Flux Cone Projection

Marashi

Dávid

Bockmayr

2012

Algorithms Mol Biol

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BackgroundAnalysis of elementary modes (EMs) is proven to be a powerful constraint-based method in the study of metabolic networks. However, enumeration of EMs is a hard computational task. Additionally, due to their large number, EMs cannot be simply used as an input for subsequent analysis. One possibility is to limit the analysis to a subset of interesting reactions. However, analysing an isolated subnetwork can result in finding incorrect EMs which are not part of any steady-state flux distribution of the original network. The ideal set to describe the reaction activity in a subnetwork would be the set of all EMs projected to the reactions of interest. Recently, the concept of "elementary flux patterns" (EFPs) has been proposed. Each EFP is a subset of the support (i.e., non-zero elements) of at least one EM.ResultsWe introduce the concept of ProCEMs (Projected Cone Elementary Modes). The ProCEM set can be computed by projecting the flux cone onto a lower-dimensional subspace and enumerating the extreme rays of the projected cone. In contrast to EFPs, ProCEMs are not merely a set of reactions, but projected EMs. We additionally prove that the set of EFPs is included in the set of ProCEM supports. Finally, ProCEMs and EFPs are compared for studying substructures of biological networks.ConclusionsWe introduce the concept of ProCEMs and recommend its use for the analysis of substructures of metabolic networks for which the set of EMs cannot be computed.

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Section: The State Of the Artmentioning

confidence: 99%

Analysis of Metabolic Subnetworks by Flux Cone Projection

Marashi

Dávid

Bockmayr

2012

Algorithms Mol Biol

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“…FBA provides a framework for metabolic reconstruction and constraint-based metabolic flux analysis of an organism without the need for detailed kinetic modelling [38–42]. …”

Section: Modelling Approaches For Metabolic Networkmentioning

confidence: 99%

Unpredictability of metabolism—the key role of metabolomics science in combination with next-generation genome sequencing

Weckwerth

2011

Anal Bioanal Chem

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Next-generation sequencing provides technologies which sequence whole prokaryotic and eukaryotic genomes in days, perform genome-wide association studies, chromatin immunoprecipitation followed by sequencing and RNA sequencing for transcriptome studies. An exponentially growing volume of sequence data can be anticipated, yet functional interpretation does not keep pace with the amount of data produced. In principle, these data contain all the secrets of living systems, the genotype–phenotype relationship. Firstly, it is possible to derive the structure and connectivity of the metabolic network from the genotype of an organism in the form of the stoichiometric matrix N. This is, however, static information. Strategies for genome-scale measurement, modelling and predicting of dynamic metabolic networks need to be applied. Consequently, metabolomics science—the quantitative measurement of metabolism in conjunction with metabolic modelling—is a key discipline for the functional interpretation of whole genomes and especially for testing the numerical predictions of metabolism based on genome-scale metabolic network models. In this context, a systematic equation is derived based on metabolomics covariance data and the genome-scale stoichiometric matrix which describes the genotype–phenotype relationship.

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Metabolic pathway analysis of yeast strengthens the bridge between transcriptomics and metabolic networks

Çakır

Kırdar

Ülgen

2004

Biotech & Bioengineering

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Central carbon metabolism of the yeast Saccharomyces cerevisiae was analyzed using metabolic pathway analysis tools. Elementary flux modes for three substrates (glucose, galactose, and ethanol) were determined using the catabolic reactions occurring in yeast. Resultant elementary modes were used for gene deletion phenotype analysis and for the analysis of robustness of the central metabolism and network functionality. Control-effective fluxes, determined by calculating the efficiency of each mode, were used for the prediction of transcript ratios of metabolic genes in different growth media (glucose-ethanol and galactose-ethanol). A high correlation was obtained between the theoretical and experimental expression levels of 38 genes when ethanol and glucose media were considered. Such analysis was shown to be a bridge between transcriptomics and fluxomics. Control-effective flux distribution was found to be promising in in silico predictions by incorporating functionality and regulation into the metabolic network structure.

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Use of network analysis of metabolic systems in bioengineering

Cited by 55 publications

References 45 publications

Analysis of Metabolic Subnetworks by Flux Cone Projection

Analysis of Metabolic Subnetworks by Flux Cone Projection

Unpredictability of metabolism—the key role of metabolomics science in combination with next-generation genome sequencing

Metabolic pathway analysis of yeast strengthens the bridge between transcriptomics and metabolic networks

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