September 9, 2011 — Researchers worldwide are looking at practical ways to use algae as the basis for biofuels. While the environmental sustainability of these fuels is enticing, creating economical algae-based fuels requires lengthy and expensive bioengineering experiments.
Jason Papin, an associate professor in the University of Virginia School of Engineering and Applied Science's Department of Biomedical Engineering, along with researchers at New York University, Dana Farber Cancer Institute, the University of California, San Diego, and the University of Iceland, have developed a computational model that will help to greatly reduce the time and effort of manipulating algae genes so they can be efficiently grown into a source for biofuels.
Papin is a principal investigator for a new research paper on the subject that was recently published in Molecular Systems Biology. The paper was featured in Science's "Editors Choice" section and was recently one of the top downloaded papers on the Molecular Systems Biology site.
The researchers based their model on a mathematical formula that quantitatively represents light and predicts how algae grow under different light sources, including light-emitting diodes, or LEDs, and sunlight.
"With a computer model you can explore tens of thousands permutations that would take many researchers significant time to identify through experimentation," he said. "With our model, you can predict which light sources will produce the desired results and save all the effort and resources. These predictions can then be experimentally verified."
The model can predict how fast cells will grow under the different light sources, as well as critical cell reactions required for growth into a viable feedstock for biofuel. The research has direct applications for producing algae biofuels and numerous algae-based products, including everything from chemotherapy drugs to lip balms. It also advances fundamental understanding of how photosynthesis generates energy for cell growth and migration.
Genetic engineering involves removing or adding combinations of genes with specific functions to make cells perform a desired function. The genetic experiments are long, tedious and expensive because genomes are complex and interconnected structures. The team's computer model alleviates the burden of carrying out much of the experimental research to show the outcomes of growth under varied conditions.
This research extends findings from the group's 2009 paper in the journal Nature Methods. In that work, they developed a method for integrating Papin's computational modeling with his NYU colleague's large experimental techniques. The method used a computer model to annotate specific genes present in a genome. Experiments were then used to find the specific genes, and in turn, validate the model. This iterative process improved the reliability of the model to make predictions about the presence of specific genes.
The research is funded by the Department of Energy and the National Science Foundation.
For information, visit Papin's website.