Flux balance analysis of plant metabolism: the effect of biomass composition and model structure on model predictions

• Main Authors: Huili eYuan, Maurice eCheung, Peter eHilbers, Natal eVan Riel
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2016-04-01
• Series: Frontiers in Plant Science
• Subjects: Arabidopsis; Flux balance analysis; Sensitivity; Biomass composition; Central carbon metabolism; Model structure
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fpls.2016.00537/full

The biomass composition represented in constraint-based metabolic models is a key component for predicting cellular metabolism using flux balance analysis (FBA). Despite major advances in analytical technologies, it is often challenging to obtain a detailed composition of all major biomass components experimentally. Studies examining the influence of the biomass composition on the predictions of metabolic models have so far mostly been done on models of microorganisms. Little is known about the impact of varying biomass composition on flux prediction in FBA models of plants, whose metabolism is very versatile and complex because of the presence of multiple subcellular compartments. Also, the published metabolic models of plants differ in size and complexity. In this study, we examined the sensitivity of the predicted fluxes of plant metabolic models to biomass composition and model structure. These questions were addressed by evaluating the sensitivity of predictions of growth rates and central carbon metabolic fluxes to varying biomass compositions in three different genome-/large-scale metabolic models of Arabidopsis thaliana. Our results showed that fluxes through the central carbon metabolism were robust to changes in biomass composition. Nevertheless, comparisons between the predictions from three models using identical modelling constraints and objective function showed that model predictions were sensitive to the structure of the models, highlighting large discrepancies between the published models.