Yeast 5 – an expanded reconstruction of the Saccharomyces cerevisiae metabolic network

Abstract

BackgroundEfforts to improve the computational reconstruction of the Saccharomyces cerevisiae biochemical reaction network and to refine the stoichiometrically constrained metabolic models that can be derived from such a reconstruction have continued since the first stoichiometrically constrained yeast genome scale metabolic model was published in 2003. Continuing this ongoing process, we have constructed an update to the Yeast Consensus Reconstruction, Yeast 5. The Yeast Consensus Reconstruction is a product of efforts to forge a community-based reconstruction emphasizing standards compliance and biochemical accuracy via evidence-based selection of reactions. It draws upon models published by a variety of independent research groups as well as information obtained from biochemical databases and primary literature.ResultsYeast 5 refines the biochemical reactions included in the reconstruction, particularly reactions involved in sphingolipid metabolism; updates gene-reaction annotations; and emphasizes the distinction between reconstruction and stoichiometrically constrained model. Although it was not a primary goal, this update also improves the accuracy of model prediction of viability and auxotrophy phenotypes and increases the number of epistatic interactions. This update maintains an emphasis on standards compliance, unambiguous metabolite naming, and computer-readable annotations available through a structured document format. Additionally, we have developed MATLAB scripts to evaluate the model’s predictive accuracy and to demonstrate basic model applications such as simulating aerobic and anaerobic growth. These scripts, which provide an independent tool for evaluating the performance of various stoichiometrically constrained yeast metabolic models using flux balance analysis, are included as Additional files 1, 2 and 3.ConclusionsYeast 5 expands and refines the computational reconstruction of yeast metabolism and improves the predictive accuracy of a stoichiometrically constrained yeast metabolic model. It differs from previous reconstructions and models by emphasizing the distinction between the yeast metabolic reconstruction and the stoichiometrically constrained model, and makes both available as Additional file 4 and Additional file 5 and at http://yeast.sf.net/ as separate systems biology markup language (SBML) files. Through this separation, we intend to make the modeling process more accessible, explicit, transparent, and reproducible.

Optimizing genome-scale network reconstructions

“…2c). A similar pattern is observed for S. cerevisiae, from the initial GENRE (iFF708) 11 through to the latest version (YEAST5) 24 . Given these two examples, it seems reasonable to expect that coverage of unique metabolic characteristics for other target organisms can be expanded through extensive manual curation of legacy metabolic knowledge, where it exists.…”

Reconstruction of genome-scale metabolic model of Yarrowia lipolytica and its application in overproduction of triacylglycerol

“…Compared with iYL619_PCP, the model size of iYL_2.0 was more extensive, but less than iNL895 and iYali4 for the reason that models iNL895 and iYali4 were constructed automatically and several genes, metabolites, and reactions in iNL895 and iYali4 were quoted directly from the metabolic model of Saccharomyces cerevisiae (Heavner et al 2012). The directionalities or reversibility of all metabolic reactions were defined based on thermodynamics, therefore, there exist 266 reversible and 741 irreversible enzymatic reactions in the model of iYL_2.0.…”

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

“…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?…”