Cyanobacteria, sometimes called blue-green algae, can harness solar energy to convert carbon dioxide into biofuels and bioproducts. Thus they provide a potential for sustainable production of fuels, materials, and other chemicals. Realizing this potential in a cost-effective manner will require a deep understanding of the metabolism (chemical reactions) of cyanobacteria and this project will apply the latest computational and experimental techniques to study cyanobacterial metabolism. The goal of this research is to produce a comprehensive computational model that describes how cyanobacteria utilize carbon and light or chemical energy in light or dark environments. This approach will lead to predictive models for the control of carbon flow in the synthesis of metabolites, a key tool for improving the ability of cyanobacteria to produce valuable bioproducts. The project includes initiatives to advance undergraduate and graduate science education, including a summer undergraduate research program focusing on underrepresented groups in science; new undergraduate and graduate teaching materials based on the advanced computational and experimental techniques used in the project; and a workshop for high-school teachers and students.
Systems biology approaches will be combined to advance understanding of metabolism and growth in aquatic phototrophs. Experimental approaches (comprehensive metabolite measurements made over many growth conditions and including 13C kinetic flux profiling) and computational approaches (mathematical kinetic flux model of metabolism at the whole-cell level) will be applied to quantify and predict three basic growth physiologies in the cyanobacterium Synechococcus sp. PCC 7002: photoautotrophic growth, oxidative respiration, and auto-fermentation. This integrated approach will lead to predictive control of metabolic fluxes, including carbon branching and intracellular energy balance. The project will develop and apply a whole-cell kinetic model fitted to the experimental measurements and the computational model will be validated by prediction and experimental testing of the effects of genetic knockouts and up or down regulation of identified targets. The project will search specifically for genetic manipulations that increase glycogen accumulation during photoautotrophic growth and hydrogen production during auto-fermentation. This knowledge will be widely applicable to understanding cyanobacterial metabolism and to improving yields of many bioproducts, not just biofuels.
This award is funded jointly by the Systems and Synthetic Biology Program in the Division of Molecular and Cellular Biosciences and the Biomedical Engineering Program in the Division of Chemical, Bioengineering, Environmental and Transport Systems.
