Dataset Information

Optimization of nutrient utilization efficiency and productivity for algal cultures under light and dark cycles using genome-scale model process control.

ABSTRACT: Algal cultivations are strongly influenced by light and dark cycles. In this study, genome-scale metabolic models were applied to optimize nutrient supply during alternating light and dark cycles of Chlorella vulgaris. This approach lowered the glucose requirement by 75% and nitrate requirement by 23%, respectively, while maintaining high final biomass densities that were more than 80% of glucose-fed heterotrophic culture. Furthermore, by strictly controlling glucose feeding during the alternating cycles based on model-input, yields of biomass, lutein, and fatty acids per gram of glucose were more than threefold higher with cycling compared to heterotrophic cultivation. Next, the model was incorporated into open-loop and closed-loop control systems and compared with traditional fed-batch systems. Closed-loop systems which incorporated a feed-optimizing algorithm increased biomass yield on glucose more than twofold compared to standard fed-batch cultures for cycling cultures. Finally, the performance was compared to conventional proportional-integral-derivative (PID) controllers. Both simulation and experimental results exhibited superior performance for genome-scale model process control (GMPC) compared to traditional PID systems, reducing the overall measured value and setpoint error by 80% over 8 h. Overall, this approach provides researchers with the capability to enhance nutrient utilization and productivity of cell factories systematically by combining genome-scale models and controllers into an integrated platform with superior performance to conventional fed-batch and PID methodologies.

SUBMITTER: Li CT

PROVIDER: S-EPMC10017758 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Optimization of nutrient utilization efficiency and productivity for algal cultures under light and dark cycles using genome-scale model process control.

Li Chien-Ting CT Eng Richard R Zuniga Cristal C Huang Kai-Wen KW Chen Yiqun Y Zengler Karsten K Betenbaugh Michael J MJ

NPJ systems biology and applications 20230315 1

Algal cultivations are strongly influenced by light and dark cycles. In this study, genome-scale metabolic models were applied to optimize nutrient supply during alternating light and dark cycles of Chlorella vulgaris. This approach lowered the glucose requirement by 75% and nitrate requirement by 23%, respectively, while maintaining high final biomass densities that were more than 80% of glucose-fed heterotrophic culture. Furthermore, by strictly controlling glucose feeding during the alternati ...[more]

PMID: 36922521

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Project description:The winter maize area is rapidly spreading in south India in response to rising demand from the poultry and fish feed industries due to the absence of major environmental constraints. Further farmers' are using the winter environment to expand maize area and production. Hence there is immense potential to increase the area under winter maize cultivation. There were no planned field experiments to explore and optimize the right time of sowing and quantity of fertilizer to be added previously due to the presence of negligible winter maize area. Farmers used to cultivate maize as per their choice of sowing time with the application of a quantity of fertilizer recommended for rainy season maize. There were no efforts made towards working on economic analysis including energy budgeting. And hence the investigation was conducted with the objective to explore the optimal planting period and fertilizer levels for winter maize through economic and energy budgeting. Planting windows (1st week of October, 2nd week of October, 3rd week of October, 4th week of October, and 5th week of October) and fertility levels (100 percent recommended dose of fertilizer (RDF), 150 percent RDF, and 200 percent RDF) were used as factors in Factorial Randomized Complete Block Design (RCBD) with three replications. The present investigation revealed that significantly higher winter maize productivity was achieved from the first and second week of October planting along with the application of 200% RDF (recommended dose of fertilizer) followed by 150% RDF. Planting of winter maize during the first week of October recorded significantly higher grain yield (8786kg ha-1) and stover yield (1220 kg ha-1) and was found on par with sowing during the second week of October. Among fertility levels, significantly higher grain yield (8320 kg ha -1) and stover yield (1195 kg ha-1) was recorded with the application of 200% RDF and were found on par with the application of 150% RDF. Further interaction effect showed that higher dry matter production, more days for physiological maturity, higher accumulation of growing degree days, photothermal units, and heliothermal units were recorded from crops planted during the first and second week of October along with the application of either 200% or 150% RDF. However, higher nutrient use efficiency was recorded from the first and second week of October planted crop supplied with lower fertility level (100% RDF). Similarly, significantly higher net returns and gross returns, output energy, net energy, and specific energy were higher from crops planted during the first week of planting along with the application of 200% RDF. Whereas, energy use efficiency and energy productivity were higher with the first week of October planted crop applied with 100% RDF. From the overall interaction, it is recommended to plant winter maize during the first fortnight of October with the application of 150 percent RDF for sustaining higher maize productivity, energy output, and economics in the maize growing area of south India.

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Dataset Information

Optimization of nutrient utilization efficiency and productivity for algal cultures under light and dark cycles using genome-scale model process control.

Publications

Optimization of nutrient utilization efficiency and productivity for algal cultures under light and dark cycles using genome-scale model process control.

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