![]() Furthermore, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acid metabolism. ![]() Accordance between predicted flux trends and gene expression trends was found for 65% of multisubunit enzymes and 75% of allosteric reactions. Agreement between flux and gene expression trends was determined by statistical comparison. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates more » revealed an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. 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. Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. Publication Date: Research Org.: National Renewable Energy Laboratory (NREL), Golden, CO (United States) Johns Hopkins Univ., Baltimore, MD (United States) Sponsoring Org.: USDOE Office of Science (SC), Basic Energy Sciences (BES) USDOE Office of Energy Efficiency and Renewable Energy (EERE) OSTI Identifier: 1395087 Alternate Identifier(s): OSTI ID: 1485586 Report Number(s): NREL/JA-5100-66824 Journal ID: ISSN 0032-0889 Grant/Contract Number: AC36-08GO28308 SC0012658 Resource Type: Journal Article: Accepted Manuscript Journal Name: Plant Physiology (Bethesda) Additional Journal Information: Journal Volume: 172 Journal Issue: 1 Journal ID: ISSN 0032-0889 Publisher: American Society of Plant Biologists Country of Publication: United States Language: English Subject: 09 BIOMASS FUELS 59 BASIC BIOLOGICAL SCIENCES Chlorella vulgaris genome-scale reconstruction validation = , ![]() Johns Hopkins Univ., Baltimore, MD (United States).of California, San Diego, La Jolla, CA (United States) Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with more » the addition of tryptophan and methionine. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility.
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