Intellectual Merit: The cyanobacteria are unique organisms that can grow using CO2 as a carbon source in the presence of sunlight. These organisms have the potential to combat global warming by utilizing CO2 while providing the economy with useful fuels and commodity chemicals. In order to achieve this goal, more must be learned about the basic metabolic activity of the cyanobacteria. These organisms can grow both in the presence of light (day-time "autotrophic" growth) where they synthesize excess sugars for continued growth in the dark (night-time "heterotrophic" growth). Understanding both metabolic programs is essential for ultimately engineering the cyanobacteria to convert CO2 into fuels and chemicals such as: ethanol, butanol, biodiesel, alkane biofuels, and eventually any of the commodity chemicals currently obtained from a barrel of oil. The cyanobacterium considered in this research is Synechocystis sp. PCC 6803, a common strain for research that is also industrially relevant. This organism will be fed radiolabeled glucose in this research so the molecule can be tracked easily through the metabolic network using a methodology called 13C-labeled metabolic flux analysis (13CMFA). This method is very accurate but also very difficult, expensive, and time-consuming to perform. Metabolism will also be studied separately by considering a purely mathematical model of metabolism using an established technique called flux balance analysis (FBA). FBA is very fast and inexpensive, but results do not usually agree with the 13C-MFA method. This research will develop a method to reconcile 13CMFA and FBA. The hypothesis to be explored is that an accurate portrayal of cell composition (e.g., correct amounts of protein, DNA, RNA, cell wall, lipids, etc.) is needed to obtain FBA predictions that are consistent with 13C-MFA measurements. To achieve this, a "phenotype predictor" algorithm will be developed and verified with laboratory measurements of cyanobacteria cell composition. The phenotype predictor will require very simple to obtain measurements of the organism and will predict the resulting cell composition along with an accurate portrayal of how metabolites are distributed throughout the metabolic network of the organism.
Broader Impacts: The "phenotype predictor" will allow scientists to rapidly and inexpensively determine very complex characteristics of a growing culture. This method will be used to determine how metabolic engineering the cyanobacteria for fuels and chemicals production ultimately impacts the health of the culture so it can be optimized. The methodology will be extremely useful to researchers working with other organisms used for industrial scale metabolite production. The project will offer excellent cross-disciplinary research training and educational opportunities for students and postdoctoral fellows.
