Version 6 of the consensus yeast metabolic network refines biochemical coverage and improves model performance

Heavner, Benjamin D.; Smallbone, Kieran; Price, Nathan D.; Walker, Larry P.

doi:10.1093/database/bat059

Cited by 102 publications

(103 citation statements)

References 24 publications

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“…An alternative ("top-down") strategy is to start with a large structural yeast network [Herrgård et al, 2008, Dobson et al, 2010, Heavner et al, 2012, Heavner et al, 2013, Aung et al, 2013, then add estimated kinetic parameters and, through successive rounds of improvement, incorporate measured parameters [Smallbone et al, 2010, Stanford et al, 2013, in an automated manner where possible [Li et al, 2010, Büchel et al, 2013. Can these two strategies be combined into a more robust and scalable approach?…”

Section: Discussionmentioning

confidence: 99%

Enzyme characterisation and kinetic modelling of the pentose phosphate pathway in yeast

Messiha

Kent

Malys

et al. 2013

Preprint

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We present the quantification and kinetic characterisation of the enzymes of the pentose phosphate pathway in Saccharomyces cerevisiae. The data are combined into a mathematical model that describes the dynamics of this system and allows for predicting changes in metabolite concentrations and fluxes in response to perturbations. We use the model to study the response of yeast to a glucose pulse. We then combine the model with an existing glycolysis one to study the effect of oxidative stress on carbohydrate metabolism. The combination of these two models was made possible by the standardized enzyme kinetic experiments carried out in both studies. This work demonstrates the feasibility of constructing larger network models by merging smaller pathway models.

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Section: Discussionmentioning

confidence: 99%

Enzyme characterisation and kinetic modelling of the pentose phosphate pathway in yeast

Messiha

Kent

Malys

et al. 2013

Preprint

Self Cite

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“…Models for an ever increasing number of bacteria have been published in recent years [58] (examples: [6, 12, 25, 31, 32, 34, 57, 60, 63, 82, 86, 110]) and more papers describing both new reconstructions and improvements upon previous iterations are published regularly. Most reconstructions are now available in a standard format such as the systems biology markup language (SBML) [36].…”

Section: Genome-scale Reconstructionsmentioning

confidence: 99%

Genome-scale modeling for metabolic engineering

Simeonidis

Price

2015

Journal of Industrial Microbiology and Biotechnology

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We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information—an area which we expect will become increasingly important for metabolic engineering—and present recent developments in the field of metabolic and regulatory integration.

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“…The defi nition of a pathway is an example of this [ 29 , 30 ]. Mora and Donaldson [ 29 ] tested on three pathway databases (KEGG, PID [ 65 ] and Reactome) a pathway-based metric to predict which proteins are likely drug targets. The metric is defi ned as the number of pathways in which a protein plays a role.…”

Section: What Is the Breadth And Depth With Which The Metabolic Netwomentioning

confidence: 99%

Metabolic Pathway Databases: A Word of Caution

Stobbe

2015

Methods in Pharmacology and Toxicology

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Metabolic pathway databases collect and organize knowledge on metabolism that has been gathered over the course of decades of research. It is, however, far from trivial to accurately represent all this knowledge in a format suitable for a wide range of computational analyses. Nevertheless, many research groups have taken up this challenge, inspired by the great value and potential of pathway databases. Nowadays, these databases are routinely used in the interpretation of omics data. They have also proven to be powerful resources for various other types of analyses, including the identifi cation of potential drug targets and in silico phenotype prediction. The number of metabolic pathway databases continues to grow, describing the metabolic network for an increasing number of organisms. For Homo sapiens and several other organisms, such as S. cerevisiae and A. thaliana , there are even multiple databases that describe their metabolic network.A word of caution is, however, warranted when working with metabolic pathway databases, because no two are alike. Databases have different ways of representing a metabolic network in a digital format and they defi ne concepts like a pathway differently. Moreover, the breadth and depth with which a network is covered varies. Several comparisons have also shown that the consensus between databases that describe the metabolic network of the same organism is limited. Furthermore, each database offers users a different way to extract the knowledge represented. At the root of these differences lies the main intended use of the database, the strategy used to reconstruct the metabolic network and the degree of curation.Taken together, these differences should be borne in mind when working with metabolic pathway databases, especially when one needs to choose between several databases that describe the metabolic network of the same organism. The choice of a particular database could infl uence (the interpretation of) the results of the analysis at hand. In this chapter, we provide further insights into metabolic pathway databases to understand their individual strengths and limitations, which is important to be aware of for any essential input of an analysis. Ultimately, these databases should be integrated and consolidated to arrive at a more complete and biologically accurate description of the metabolic network. This will, however, require a broad community effort.

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Version 6 of the consensus yeast metabolic network refines biochemical coverage and improves model performance

Cited by 102 publications

References 24 publications

Enzyme characterisation and kinetic modelling of the pentose phosphate pathway in yeast

Enzyme characterisation and kinetic modelling of the pentose phosphate pathway in yeast

Genome-scale modeling for metabolic engineering

Metabolic Pathway Databases: A Word of Caution

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