Scientific Circle: Novel Approach Yields Predictions Validated by Experiments on Key Cell Signalling Pathways

Jan 26, 2005

Coming full circle has new meaning for researchers who demonstrated a promising new approach integrating scientific experimentation and mathematical modeling to study a key signaling pathway that helps cells decide whether to grow or die.

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 that have not been performed or that are very difficult, or impossible, to perform.”

The research was funded by the National Institutes of Health and largely completed at MUSC, where Voit was a professor before joining the Georgia Tech faculty this past fall. Voit is continuing this research in his new position. His co-authors on the Nature paper are Yusuf Hannun, professor and chair of the MUSC Department of Biochemistry and Molecular Biology, MUSC postdoctoral researchers Fernando Alvarez-Vasquez and Ashley Cowart, MUSC graduate student Kellie Sims and former MUSC postdoctoral fellow Yasuo Okamoto.

The Nature paper represents a very early stage in the necessary process of developing more sophisticated models, Voit said. Though the paper focused on modeling sphingolipid metabolism in yeast, it represents a good starting point for modeling this pathway in humans because of similarities in the process, he added. He plans to collaborate on developing such a model with Georgia Tech Professor of Biology Alfred Merrill, whose research focuses on human sphingolipids.

In the current study, Voit and his co-authors tested their model to determine the degree to which its predictions were accurate.

“Qualitatively, all of our predictions were correct,” Voit said. “If we predicted an increase in something, the experiments showed a similar increase. Quantitatively, our predictions need to be refined further. If we had a human model of the current quality, we would still not be able, for instance, to predict with sufficient reliability the drug dosage needed for treating a specific disease process.”

The researchers plan to refine their model with additional mathematical methods and then create new hypotheses for experimenters to test.

“We’ll be able to compute mathematically the points in the system that are most crucial to test because they are most sensitive to change,” Voit explained. “Eventually, we’ll have a metabolic model of the yeast cell. Then, for example, we might be able to apply it in biotechnology to yeast strains that are better producers of industrial alcohol or methanol as fuel for cars.”

Voit emphasized that mathematical modeling of whole cells – the Holy Grail in his field -- is a highly complex task because of the huge amounts of data necessary and the multitude of possible biological system responses that must be considered.

He compares the complexity of this task to an aerial view of a busy city with many people, cars, energy, and information moving around. “You want to capture all of that activity, but you have incomplete information,” Voit explained. “You can’t ask who just called whom and why, or where all these people and cars are going.”

Addressing this complexity necessitates the use of advanced mathematical equations based upon biochemical systems theory to describe dynamic biological processes, Voit said. These processes include feedback mechanisms that work to stabilize a system much like a thermostat maintains a constant temperature.

Such mathematical models could help characterize diseases in which a system is unable to return to its normal state, is set to a wrong state (e.g., glucose fluctuations in diabetes) or a control is missing, such as the proliferation of cells in cancer, or the absence of an enzyme that results in an inherited metabolic disease, Voit explained.

Another application for mathematical models is drug design. Researchers could use models to find optimal points in a metabolic pathway for drug intervention that would achieve desired treatment results with minimal side effects, Voit said.

“The real Holy Grail will be a theory of biology that allows us to make solid predictions,” Voit said. “We biologists are always envious of the physicists because they have all sorts of theories. But biology is so much more complicated. We’re on our way there, and we’re looking for biological design principles we can test mathematically…. Then we’ll be a step closer to a theory of biology.”

Source: Georgia Institute of Technology

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