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Press Release 041309 | |
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FOR IMMEDIATE RELEASE
ISB Researchers Demonstrate Computational Method for the Maximizing Biological Information from Genetic Interaction Data
SEATTLE, April 13, 2009 – Researchers at the Institute for Systems Biology (ISB) have demonstrated a new computational method for getting the most biological information out of huge and growing sets of genetic interaction data. A paper addressing the development has been published in the April edition of PLoS Computational Biology at http://www.ploscompbiol.org/doi/pcbi.1000347. Researchers need new and better methods for gleaning information about genetic interactions from massive data sets because technology is now allowing for studies of complex genetic interactions rather than single genes or even linear pathway interaction. "Until recently, genetics has been practiced mainly as the study or identification of this gene or that gene which is then linked to a specific disease," said ISB Associate Professor Timothy Galitski, PhD. "Technological and computational advances are allowing us to look at gene networks and how they interact in ways that result in human health or disease states." "Understanding how entire gene networks function will be critical if we are to use personal genome sequences as predictors of health and disease in humans," Galitski said. The new algorithm identified over 30 percent more associations of specific genes with biological functions than a previous analysis using pathway reconstruction techniques. More strikingly, it provided a tenfold increase in the identification of co-functioning allele pairs over pathway analysis. When represented as a network, these allele pairs form functionally interacting multi-gene modules that correspond to distinct cellular functions. "Pathway analysis reveals specific relationships for a few genes at a time, but by looking at interaction patterns across the entire network we are able to find large groups, or modules, of genes working together inside the cell," said Greg Carter, PhD, a senior research scientist at ISB who collaborated to develop and validate the algorithm. The new information extraction and analysis process addresses several research objectives. It removes genetic preconceptions from the process by automatically finding the optimal analysis for each data set, improves the ability of researchers to extract information from externally generated data sets, and quantitatively extracts more information about functional genetic interactions than previous approaches. "This is a terrific example of a driving force here at ISB, which is the idea that answering biological questions drives the need to develop new technologies, and those technologies provide new information which generates new biological questions," Galitski said. About the Institute for Systems BiologyThe Institute for Systems Biology (ISB) is an internationally renowned, non-profit research institute headquartered in Seattle and dedicated to the study and application of systems biology. Founded by Leroy Hood, Alan Aderem and Ruedi Aebersold, ISB seeks to unravel the mysteries of human biology and identify strategies for predicting and preventing diseases such as cancer, diabetes and AIDS. ISB's systems approach integrates biology, computation and technological development, enabling scientists to analyze all elements in a biological system rather than one gene or protein at a time. Founded in 2000, the Institute has grown to 14 faculty and more than 250 staff members; an annual budget of more than $35 million; and an extensive network of academic and industrial partners. For more information about ISB, visit http://www.systemsbiology.org
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CONTACT:
Todd Langton Associate Director of Communications and Public Relations (206) 732-1333 
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