NEW YORK, May 7 – Demonstrating that systems biology may perhaps be more than just a good theory, researchers at the Institute of Systems Biology applied their global, model-based strategy for understanding gene function to a cellular pathway in yeast, according to a paper published in Science last Friday.
While the individual research techniques used by the research team are not necessarily new, taken together, and combined with close integration between computation and experiment, the approach represents a new way of performing biology, said Benno Schwikowski, a researcher at the Institute for Systems Biology, in Seattle, who contributed to the study.
“This is the first time it’s been suggested that you could do these kinds of analyses in a global way,” said David Goodlett, a co-author of the paper also at the institute.
To show how their approach works, the research team, led by institute director Leroy Hood, chose to study the galactose utilization pathway in the yeast Saccharomyces cerevisiae , a well-known pathway that the scientists could model as a genetic regulatory switch. In this type of pathway, the enzymes necessary for transporting and breaking down galactose are only present when galactose itself is present and other sugars are not.
In the first step of their approach, Hood and his colleagues used information found in the scientific literature to simulate the molecular interactions that allow the pathway to function. Then, the researchers varied the parameters of the model, and simulated how the galactose pathway would respond.
Meanwhile, in the lab, the researchers genetically engineered yeast cells to over- or under-express certain proteins, or varied the cells’ environment, and measured the cells’ response using DNA microarrays and tandem mass spectrometry aided by ICAT, a method for detecting proteins found only in low numbers in the cell.
By comparing the simulated and experimental responses to outside stimuli, Hood’s team could observe where the model predicted the correct response, and where the model lacked information to make a correct prediction. In this way, the researchers could gather information about the pathway, while also pinpointing areas for more focused study.
One of the advantages of this approach, said Goodlett, is that it isn’t necessary for a researcher to formulate a hypothesis before conducting the experiment. Instead, learning where the model fails allows the researcher to propose new hypotheses about how the pathway works, and then go back and test them.
The idea, said Schwikowski, is “to open up hypothesis-driven science to discovery-based methods” by combining powerful computational methods with high-throughput experimental techniques.“Ultimately, you want to elucidate larger and larger biological systems with that approach,” Schwikowski added.