SCIENCE

Gene Network Sciences (GNS) today announced that the Department of Energy (DOE) has awarded the company a $2.5 million, three-year grant as part of the Genomics: GTL Program. The funds will be used to create a computational hypothesis testing framework that will allow GNS to use computer simulations to infer probable network architectures in cells in a massively parallel computing environment. This framework will include techniques to integrate diverse data and to explore millions of "what if" hypotheses about the functions of genes and proteins within pathways. Even when full genomic sequences for an organism are available, the functions and interactions of only a small number of gene components are clear. The GNS grant will further the company's methods to best infer the most probable network architectures in cells, starting with the creation of a dynamic simulation of E. coli, a well-studied bacteria where more than 60 percent of the genes have a known function. GNS will then apply its findings to other systems, including the metabolically versatile bacterium Shewanella oneidensis, which is of particular interest to the DOE given its ability to metabolize and immobilize metals, including radionuclides such as uranium. Fewer than 10 percent of the function of Shewanella genes are currently known, making it a prime candidate for network inference methods. "While there is a wealth of genomics, proteomics and microarray data available today, we still have many more questions than answers when it comes to understanding the functions of these genes and proteins and their downstream effects on the behavior of a cell or an organism," said Colin Hill, CEO of Gene Network Sciences. "The methodologies we are creating with the DOE grant will be applicable to any organism, including humans, where only three to five percent of gene function is known. As we better understand the functions of genes in a network context, we can better predict and control their responses to internal and external perturbations." According to Hill, these methods will be able to rapidly test multiple hypotheses on supercomputers, which would take decades to do in a wet lab. In addition, the cost of running multiple in silico experiments represent a fraction of what it would take to test similar predictions in vitro. The GNS team leading the network inference effort includes Dr. Michael Shuler, a member of the company's scientific advisory board and director of Cornell's Bioengineering Program. Shuler is considered a pioneer in the field of E. coli research and modeling, and bioprocess engineering. "The GNS effort proposes going beyond the cellular mapping of known circuitry to actually generate new insights into genetic interactions and a predictive understanding of the biology of organisms," said Shuler. Additional GNS collaborators on this effort include the Wadsworth Center, Washington University, the Cybercell consortium, and the Shewanella Federation. "I applaud the work being done by Gene Network Sciences in the important field of bioremediation," said House Science Committee Chairman Sherwood Boehlert, who represents New York's 24th District. "Working together, we can continue to provide better technology for a cleaner, safer environment for the nation through biotechnology research." This is the second major grant that GNS has been awarded in 2004. In February, the company announced that along with Cornell University and the University of California, San Diego, it was selected by the National Heart, Lung, and Blood Institute of the National Institutes of Health to receive a $2 million, four-year Bioengineering Research Grant. Those funds are being used to develop a data-driven 3-D computer model of the canine ventricle that will serve as a useful representation for other species, including humans.