Further developments towards a genome-scale metabolic model of yeast

Abstract

BackgroundTo date, several genome-scale network reconstructions have been used to describe the metabolism of the yeast Saccharomyces cerevisiae, each differing in scope and content. The recent community-driven reconstruction, while rigorously evidenced and well annotated, under-represented metabolite transport, lipid metabolism and other pathways, and was not amenable to constraint-based analyses because of lack of pathway connectivity.ResultsWe have expanded the yeast network reconstruction to incorporate many new reactions from the literature and represented these in a well-annotated and standards-compliant manner. The new reconstruction comprises 1102 unique metabolic reactions involving 924 unique metabolites - significantly larger in scope than any previous reconstruction. The representation of lipid metabolism in particular has improved, with 234 out of 268 enzymes linked to lipid metabolism now present in at least one reaction. Connectivity is emphatically improved, with more than 90% of metabolites now reachable from the growth medium constituents. The present updates allow constraint-based analyses to be performed; viability predictions of single knockouts are comparable to results from in vivo experiments and to those of previous reconstructions.ConclusionsWe report the development of the most complete reconstruction of yeast metabolism to date that is based upon reliable literature evidence and richly annotated according to MIRIAM standards. The reconstruction is available in the Systems Biology Markup Language (SBML) and via a publicly accessible database http://www.comp-sys-bio.org/yeastnet/.

Building a Kinetic Model of Trehalose Biosynthesis in Saccharomyces cerevisiae

“…First, N is the stoichiometric matrix, which may be derived easily from the topology of the model (Fig. 18.1)-here derived from a recent genomescale reconstruction of yeast metabolism (Dobson et al, 2010). Symbol x denotes metabolite concentrations, which for our pathway are the concentrations of glucose, G6P, glucose 1-phosphate, UDP glucose, T6P, and trehalose.…”

Large-Scale Metabolic Models: From Reconstruction to Differential Equations

“…1,2 The consensus metabolic network of the model organism S. cerevisiae contains thousands of reactions and metabolites. [3][4][5] From the steady state solution space of all possible fluxes, a number of techniques have been proposed to deduce network behavior, including flux balance and extreme pathway or elementary mode analysis. In particular, flux balance analysis (FBA) highlights the most effective and efficient paths through the network in order to achieve a particular objective function.…”

Kinetic modelling of large-scale metabolic networks

“…This program calculates the likelihood score for each model and uses different model selection techniques to choose the "best" one according to the likelihood and number of parameters. The model selection strategies implemented in jModelTest are the Akaike Information Criterion (AIC) [1], Bayesian Information Criterion (BIC) [13] and dynamic Likelihood Ratio Tests (dLRTs) [12]. Table 1 shows the 88 candidate substitution models supported by jModelTest.…”

“…These metabolic "reconstructions" are further interpreted as metabolic networks and several analyses can be derived from them. In the case of the popular model organism Saccharomyces cerevisiae the metabolic reconstruction is fairly advanced in terms of completeness and sophistication [2,1].…”

“…These metabolic "reconstructions" are further interpreted as metabolic networks and several analyses can be derived from them. In the case of the popular model organism Saccharomyces cerevisiae the metabolic reconstruction is fairly advanced in terms of completeness and sophistication [2,1].On its own, a metabolic reconstruction can be analysed through a number of approaches: network analyses (clustering coe cients, betweeness centrality, etc.) provide metrics about the connectedness of the network; elementary flux mode analysis provides a unique decomposition of the network in minimal subsets that are capable of operating independently.…”