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Yeast Microarrays May Help Make Communication Networks More Secure

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A researcher at the Keck Graduate Institute of Applied Life Sciences is taking a novel approach to microarray analysis, hoping to use the information gleaned from a series of gene expression and protein-protein interaction experiments in budding yeast to learn how to make communications networks better resist attack.

The researcher, Animesh Ray, and his group in early February received a $2.5 million National Science Foundation grant to construct model biological networks that might provide insights into how communications networks like the Internet and power grid systems might be improved. The grant expands research originally funded by a two-year $500,000 NSF grant.

Ray’s collaborators in this grant include Amarnath Gupta, an associate research scientist at the University of California, San Diego, and a member of the data and knowledge systems group of the San Diego Supercomputer Center; and Fan Chung Graham, a professor of mathematics and computer science at the University of California at San Diego.

The group’s work begins to expand the boundaries of microarray research and demonstrates the promise of the technology as a tool outside of the genomics laboratory.

“This is a bottoms-up approach,” Ray, an associate professor in molecular computation, told BioArray News. “A systems approach might be the term that best describes it. A lot of people have been thinking about it for a long time.”

In the two-stage experiment, microarrays will be used to study the gene regulatory network of sporulation in yeast.

In the first part, Ray said, whole-genome yeast microarrays will be used in the ordinary way: to determine transcription of RNA in particular cells under certain conditions. Later in the three-year project, the researchers will use chromatin immunoprecipitation (ChIP) microarrays to test biological networks.

“We want to directly test the hypothesis that some genes’ products directly bind to nearby other genes,” he said.

The approach is a time-series analysis, using single and multiple mutations, and analyzing them in microarrays to see the effects of perturbation, Ray said. “This will allow us to put together connections of how various genes are communicating with each other,” he said. “The question is how do we figure out which genes affect the others.”

Using this data, the researchers will build and test matrices of genes. The models will be tested by choosing specific genes to be disrupted, singly or in multiples, perhaps the laboratory equivalent of testing strings of lights for a Christmas tree.

The models will make assumptions of regulation, either positively or negatively, and create a chain of relationships that, then, will be tested.

“This won’t be done in isolation, but in an integrated fashion with the rest of the experiment and the rest of the knowledge everybody has,” he said. “It’s an integration of knowledge-rich data and models based on microarrays. You are looking at many different genes’ response to small perturbations. You are no longer telescoping into a small part of a tapestry but looking at the whole tapestry.”

The research team comprises as many as 14 people including biologists, mathematicians, and computer scientists.

“Most people are interested in analyzing gene regulatory networks to solve some biological problem or gain insights into how genes regulate on another,” said Ray. “Our approach should lead to all of those insights, but also the abstract mathematical properties of the biological networks and discovering whether some of these properties have been selected by evolution.”

— MOK

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