A genome-scale metabolic model of a green microalga is providing strategies for improving its growth.
The green microalga, Chlorella vulgaris, has the potential to act as a cell factory in the production of biofuels and bioproducts. To better understand the complex and diverse metabolic capabilities of this green microalga, researchers transformed the organism’s genomic data into a mathematical model. This model enabled the researchers to understand and systematically analyze how the alga is able to grow in a variety of conditions including with just sunlight and carbon dioxide. The model then provided guidance on modifying the conditions to enhance growth performance.
An in-depth understanding of how microorganisms use nutrients and grow is essential to improving the production of desired products, including biofuels and bioproducts. The developed model simulated different growth parameters simultaneously (e.g., nutritional resources, genetic modifications, and light source and availability) so that optimal conditions can be predicted. Optimizing growth conditions maximizes the probability of obtaining the desired experimental result, while also saving valuable time and resources.
The global movement toward more green-energy opportunities is resulting in the development of new approaches for producing renewable fuels in economical ways. The green microalga, C. vulgaris, is recognized as a promising candidate for biofuel production due to its ability to store high amounts of lipids and its natural metabolic versatility. However, many fundamental questions remain on how this alga and other microorganisms can more efficiently use nutritional sources not just for the organism’s growth, but also for sustainable and efficient production of biofuel and bioproducts. Researchers from the University of California, San Diego; Johns Hopkins University; University of Delaware; and National Renewable Energy Laboratory wanted to develop a way to more efficiently modify C. vulgaris to improve growth productivity. To do this, the scientists developed a compartmentalized genome-scale metabolic model that enabled quantitative insight into the organism’s metabolism. The model accurately predicted phenotypes under a variety of growth conditions including photoautotrophic, heterotrophic, and mixotrophic conditions. Model validation was performed using experimental data, laying the foundation for model-driven strain design and growth medium alteration to improve biomass yield. Model prediction of growth rates under various medium compositions and subsequent experimental tests showed an increased growth rate with the addition of the amino acids tryptophan and methionine. The reconstruction represents the most comprehensive model of eukaryotic photosynthetic organisms to date, based on genome size and number of genes in the reconstruction. With this metabolic model, researchers should be able to improve experimental design strategies for strain, production process, and final product yield optimization.
Contact (BER PM)
Dawn Adin, Ph.D.
Program Manager, Office of Biological and Environmental Research
Michael J. Betenbaugh
Department of Chemical and Biomolecular Engineering
Johns Hopkins University
Department of Bioengineering
University of California, San Diego
This work was supported by the National Science Foundation (grant number 1332344); U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (grant number DE-SC0012658); and Mexican National Research Council (fellowship number 237897 to C.Z.).
C. Zuñiga, C.-T. Li, T.Huelsman, J. Levering, D. C. Zielinski, B. O. McConnell, C. P. Long, E. P Knoshaug, T. G. Guarnieri, M. R. Antoniewicz, M. J. Betenbaugh, and K. Zengler, “Genome-scale metabolic model for the green alga Chlorella vulgaris UTEX 395 accurately predicts phenotypes under autotrophic, heterotrophic, and mixotrophic growth conditions.” Plant Physiology 172, 589-602 (2016). [DOI: 10.1104/pp.16.00593] (Reference link)
Zengler Laboratory Website
Betenbaugh Laboratory Website
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