On the impact of biomass composition in constraint-based flux analysis

The biomass equation is a critical component in genome-scale metabolic models (GEMs): It is one of most widely used objective functions within constraint-based flux analysis formulation, describing cellular driving force under the growth condition. The equation accounts for the quantities of all known biomass precursors that are required for cell growth. Most often than not, published GEMs have adopted relevant information from other species to derive the biomass equation when any of the macromolecular composition is unavailable. Thus, its validity is still questionable. Here, we investigated the qualitative and quantitative aspects of biomass equations from GEMs of eight different yeast species. Expectedly, most yeast GEMs borrowed macromolecular compositions from the model yeast, Saccharomyces cerevisiae. We further confirmed that the biomass compositions could be markedly different even between phylogenetically closer species and none of the high throughput omics data such as genome, transcriptome and proteome provided a good estimate of relative amino acid abundances. Upon varying the stoichiometric coefficients of biomass components, subsequent flux simulations demonstrated how predicted in silico growth rates change with the carbon substrates used. Furthermore, the internal fluxes through individual reactions are highly sensitive to all components in the biomass equation. Overall, the current analysis clearly highlight that biomass equation need to be carefully drafted from relevant experiments, and the in silico simulation results should be appropriately interpreted to avoid any inaccuracies.