Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris

Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. Here, we used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of Chlorella vulgaris UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealed an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted fluxes trends and gene expression trends was found for 65% of multi-subunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acids metabolism. Furthermore, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of C. vulgaris, iCZ946, thus increasing our knowledgebase by 10% for this model green alga.