Biomass composition: the “elephant in the room” of metabolic modelling

Abstract

Genome-scale stoichiometric models, constrained to optimise biomass production are often used to predict mutant phenotypes. However, for Saccharomyces cerevisiae, the representation of biomass in its metabolic model has hardly changed in over a decade, despite major advances in analytical technologies. Here, we use the stoichiometric model of the yeast metabolic network to show that its ability to predict mutant phenotypes is particularly poor for genes encoding enzymes involved in energy generation. We then identify apparently inefficient energy-generating pathways in the model and demonstrate that the network suffers from the high energy burden associated with the generation of biomass. This is tightly connected to the availability of phosphate since this macronutrient links energy generation and structural biomass components. Variations in yeast’s biomass composition, within experimentally-determined bounds, demonstrated that flux distributions are very sensitive to such changes and to the identity of the growth-limiting nutrient. The predictive accuracy of the yeast metabolic model is, therefore, compromised by its failure to represent biomass composition in an accurate and context-dependent manner.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-015-0819-2) contains supplementary material, which is available to authorized users.

Extension of the yeast metabolic model to include iron metabolism and its use to estimate global levels of iron‐recruiting enzyme abundance from cofactor requirements

“…For simplifications of the function and activity of the iron regulon, see text [Color figure can be viewed at wileyonlinelibrary.com] omission or incomplete representation of some pathways in the model. Our previous work on the Yeast 7 metabolic network model highlighted the fact that the high metabolic burden (characterised by high reaction fluxes, and often indicative of the low efficiency of their cognate enzymes (Bonarius, Hatzimanikatis, Meesters, Schmid, & Tramper, 1996) carried by pathways involved in energy generation and processes imposed limitations on the model's predictive ability (Dikicioglu, Kırdar, & Oliver, 2015). A recent analysis that we carried out by constraining this model by fluxes calculated using the intracellular concentrations of intermediates in the purine nucleotide biosynthetic pathway, as determined by HPLC analysis (Hesketh, Vergnano, Wan, & Oliver, 2017), demonstrated that the prediction of growth rate was at least half or twice the experimentally determined value.…”

Advances in metabolic modeling of oleaginous microalgae

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Integration of Biomass Formulations of Genome-Scale Metabolic Models with Experimental Data Reveals Universally Essential Cofactors in Prokaryotes

“…Yet, most GEMs adapt the biomass composition from few well-studied organisms due to the lack of standardized protocols, both experimental and computational. For quantitative analyses of the impact of variations in the stoichiometric coefficients we refer the reader to previous studies (Dikicioglu et al, 2015, Feist et al, 2007, Pramanik and Keasling, 1998, Yuan et al, 2016). Here, we address the qualitative aspect of the problem, specifically pertaining to organic cofactors, by bringing together evidences for essentiality hidden in disparate data sources – biochemical and bioinformatics databases, literature and genetic screens.…”