Utilizing genome-scale models to optimize nutrient supply for sustained algal growth and lipid productivity

Autor: Cristal Zuñiga, Kai-Wen Huang, Alison Shapiro, Chien-Ting Li, Yiqun Chen, Michael J. Betenbaugh, Liqun Jiang, Jacob Yelsky, Richard Eng, Karsten Zengler
Rok vydání: 2019
Předmět:
0106 biological sciences
0301 basic medicine
Nitrogen
Microorganism
Heterotroph
Biomass
Cyanobacteria
Photosynthesis
01 natural sciences
Article
General Biochemistry
Genetics and Molecular Biology
03 medical and health sciences
chemistry.chemical_compound
Nutrient
Nitrate
010608 biotechnology
Drug Discovery
Microalgae
Autotroph
lcsh:QH301-705.5
2. Zero hunger
Autotrophic Processes
Systems Biology
Applied Mathematics
Fatty Acids
food and beverages
Nutrients
15. Life on land
Pulp and paper industry
Lipids
Computer Science Applications
Glucose
030104 developmental biology
lcsh:Biology (General)
chemistry
Batch Cell Culture Techniques
13. Climate action
Modeling and Simulation
Nutrient pollution
Computer modelling
8. Economic growth
Environmental science
Chlorella vulgaris
Algorithms
Biotechnology
Zdroj: NPJ Systems Biology and Applications
npj Systems Biology and Applications, Vol 5, Iss 1, Pp 1-11 (2019)
ISSN: 2056-7189
Popis: 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 (iCZ946) 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.
Genome-scale models as a tool to optimize nutrient supply for different conditions Supplying the optimal levels of nutrients such as nitrogen and carbon is an important consideration when cultivating green algae and other biological systems. Overfeeding nutrients can increase production costs while insufficient supplies can lower product yields. Finding the balance between production costs and productivity is a major challenge in biomanufacturing. Our published C. vulgaris genome-scale model was used to control the supplies of nitrate for algal growth under autotrophic or photosynthetic conditions, saving 18% of nitrate fed. Under heterotrophic conditions with organic carbon supplied, we optimized feeding of both glucose and nitrate while maintaining robust growth. The model was also used to enhance fatty acid volumetric product yields by over 60% and lutein levels nearly 3 fold under nitrogen-limited conditions. This study demonstrates that metabolic models can be used to predict the optimal nutrient supply as we strive to make algal bioprocessing sustainable for the future.
Databáze: OpenAIRE
Externí odkaz: