With implications for disease characterization, biotechnology and drug design, the approach tested by researchers at the Medical University of South Carolina (MUSC) and the Georgia Institute of Technology offers an efficient way of gaining useful knowledge from the massive amounts of complex biological information generated with today's advanced analysis technology.
The work represents another step toward modeling complex biological systems accurately enough to make useful predictions. "Our research went beyond describing a one-way street," said Professor Eberhard Voit of the Georgia Tech/Emory University Wallace H. Coulter Department of Biomedical Engineering. "Experimenters generate data, modelers design a mathematical model that fits the data, and often that's the end of the story. But, in this research, the experimenters actually tested hypotheses generated by the model, thus closing the circle."
Voit -- also a Georgia Research Alliance Eminent Scholar with expertise in mathematical and computational modeling -- reports this research with his MUSC colleagues in the Jan. 27, 2005 issue of the journal Nature. The researchers demonstrated their scientific approach within the context of sphingolipid metabolism in yeast. Sphingolipids are signaling molecules that assist cells in deciding whether to grow or die. Research has shown these molecules have implications in preventing several types of cancer in animal models.
"We amassed an incredible amount of data from the literature and the lab on this particular metabolic pathway and integrated it all into one functioning entity -- the mathematical model," Voit explained. "This model now allows us to test 'what-if' scenarios and make predictions on experiments th