Abstract Nutrient availability is critical for growth of algae and other microbes used for generating valuable biochemical products. Determining the optimal levels of nutrient supplies to cultures can eliminate feeding of excess nutrients, lowering production costs and reducing nutrient pollution into the environment. With the advent of omics and bioinformatics methods, it is now possible to construct genome-scale models that accurately describe the metabolism of microorganisms. In this study, a genome-scale model of the green alga Chlorella vulgaris ( i CZ946) was applied to predict feeding of multiple nutrients, including nitrate and glucose, under both autotrophic and heterotrophic conditions. The objective function was changed from optimizing growth to instead minimizing nitrate and glucose uptake rates, enabling predictions of feed rates for these nutrients. The metabolic model control (MMC) algorithm was validated for autotrophic growth, saving 18% nitrate while sustaining algal growth. Additionally, we obtained similar growth profiles by simultaneously controlling glucose and nitrate supplies under heterotrophic conditions for both high and low levels of glucose and nitrate. Finally, the nitrate supply was controlled in order to retain protein and chlorophyll synthesis, albeit at a lower rate, under nitrogen-limiting conditions. This model-driven cultivation strategy doubled the total volumetric yield of biomass, increased fatty acid methyl ester (FAME) yield by 61%, and enhanced lutein yield nearly 3 fold compared to nitrogen starvation. This study introduces a control methodology that integrates omics data and genome-scale models in order to optimize nutrient supplies based on the metabolic state of algal cells in different nutrient environments. This approach could transform bioprocessing control into a systems biology-based paradigm suitable for a wide range of species in order to limit nutrient inputs, reduce processing costs, and optimize biomanufacturing for the next generation of desirable biotechnology products.
The eukaryotic green alga Chlorella vulgaris UTEX 395 was cultured under carbon dioxide (CO 2 ) concentrations ranging from 0.04% to 15% in order to examine the effect of CO 2 on algal growth, biomass composition and reactive oxygen species (ROS) accumulation in the culture medium. Supplying 5% CO 2 yielded the highest biomass growth rate (μ = 0.35 day −1 ) compared with 0.04% (μ = 0.15 day −1 ) and 15% (μ = 0.19 day −1 ) CO 2 conditions. Experimental evidence showed that increasing CO 2 levels from 0.04% to 2% and above did not alter overall protein content significantly but did enhance C16:1 and C18:1 monounsaturated fatty acid (MUFA) composition by 3.5 and 2 fold, respectively, reducing C18:3 polyunsaturated fatty acid (PUFA) levels. Interestingly, bubbling 5% and 15% CO 2 increased one type of ROS, H 2 O 2 levels, in sterile medium by 1.8 to 2 μM while growing C. vulgaris substantially lowered these H 2 O 2 levels. The ability to lower H 2 O 2 levels, which was reduced for non-viable algal cells, was also observed with C. protothecoides UTEX 29 and C. sorokiniana UTEX 1230. In order to understand the impact of H 2 O 2 directly, 10 μM and 25 μM H 2 O 2 were added daily to 0.04% CO 2 -bubbled C. vulgaris cultures. Periodic H 2 O 2 addition did not affect the growth of C. vulgaris or change its biomass composition. These findings demonstrate C. vulgaris can thrive at elevated concentrations of CO 2 and also showed the capacity of microalgae to reduce the ROS level, specifically H 2 O 2 , present in a CO 2 bubbling environment. • Increasing CO 2 levels from 0.04% to over 2% increases MUFA and decreases PUFA. • Elevating CO 2 levels from 0.04% to 15% increases H 2 O 2 level from 0.1 μM to 1.8 μM. • C. vulgaris , C. protothecoides and C. sorokiniana suppress H 2 O 2 levels. • Live C. vulgaris reduced H 2 O 2 level by nearly 100% under 5% CO 2 conditions.
Constraint-based modeling has been applied to analyze metabolism of numerous organisms via flux balance analysis and genome-scale metabolic models, including mammalian cells such as the Chinese hamster ovary (CHO) cells-the principal cell factory platform for therapeutic protein production. Unfortunately, the application of genome-scale model methodologies using the conventional biomass objective function is challenged by the presence of overly-restrictive constraints, including essential amino acid exchange fluxes that can lead to improper predictions of growth rates and intracellular flux distributions. In this study, these constraints are found to be reliably predicted by an "essential nutrient minimization" approach. After modifying these constraints with the predicted minimal uptake values, a series of unconventional objective functions are applied to minimize each individual non-essential nutrient uptake rate, revealing useful insights about metabolic exchange rates and flows across different cell lines and culture conditions. This unconventional uptake-rate objective functions (UOFs) approach is able to distinguish metabolic differences between three distinct CHO cell lines (CHO-K1, -DG44, and -S) not directly observed using the conventional biomass growth maximization solutions. Further, a comparison of model predictions with experimental data from literature correctly correlates with the specific CHO-DG44-derived cell line used experimentally, and the corresponding dual prices provide fruitful information concerning coupling relationships between nutrients. The UOFs approach is likely to be particularly suited for mammalian cells and other complex organisms which contain multiple distinct essential nutrient inputs, and may offer enhanced applicability for characterizing cell metabolism and physiology as well as media optimization and biomanufacturing control.
Today's Biochemical Engineer may contribute to advances in a wide range of technical areas. The recent Biochemical and Molecular Engineering XXI conference focused on "The Next Generation of Biochemical and Molecular Engineering: The role of emerging technologies in tomorrow's products and processes". On the basis of topical discussions at this conference, this perspective synthesizes one vision on where investment in research areas is needed for biotechnology to continue contributing to some of the world's grand challenges.
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