MIT model explains how the brain can learn novel tasks while still remembering what it has already learned.
The National Institutes of Health announced on Aug. 5 that it is spending $15.5 million to create a Center of Excellence in Genomic Science so scientists can better predict yeast's behavior.
The initiative, called the Alpha Project , involving MIT research scientist Drew Endy , is a critical step in advancing human genome research. Endy's lab, which is helping lead the biological and computational aspects of the project, will receive $1.5 million.
Endy, a fellow in the Department of Biology and Division of Bioengineering and co-principal investigator for the center, emphasized that knowledge obtained by sequencing an organism's genome and from other experimental methods "does not instantly translate into an understanding of how an organism will behave and how we might best interact with or redirect its behavior."
The ability to make useful predictions about the future behavior of living systems has profound implications for understanding human diseases and for eventually tailoring precise drug treatments to individuals. Such a predictive ability also would provide a foundation for the design and engineering of new, synthetic biological systems directed toward a whole host of applications.
"Drew Endy's leadership in the national Alpha Project is an important part of MIT's movement to integrating informational science and modern biology," said Institute Professor Phillip A. Sharp of biology, a Nobel laureate and director of the McGovern Institute for Brain Research at MIT. "Yeast are an ideal organism for this integration and a good model for future studies of more complex cells."
In addition to work at MIT, the project involves 40 biologists, chemists, engineers, mathematicians, computer scientists and physicists from the California Institute of Technology, the University of California at Berkeley, and Pacific Northwest National Laboratory and the Molecular Sciences Institute, an independent research laboratory in Berkeley, Calif.
Roger Brent, director of the Molecular Sciences Institute, will lead the center, one of two established this year by the NIH's National Human Genome Research Institute.
"This research agenda is a logical next step. Now that the genome projects have begun to identify the molecular components of living things, we next need to learn how these components work together to create complex outcomes," Brent said.
Baker's yeast was chosen for the study because it is a relatively well-characterized and readily manipulated unicellular organism. The project's name refers to a yeast mating pheromone, alpha factor, which is responsible for triggering a signaling pathway that results in the arrest of yeast cell growth and prepares cells for mating.
"This particular eukaryotic regulatory pathway is a prototype for regulatory networks that process information and govern response to extracellular stimuli in higher eukaryotes," Brent said. "The methods we will apply and develop during the course of this project, and the combined approach by which we will employ them, will be ported to study numerous other processes in other organisms. The types of molecular players involved in these responses have shared characteristics in similar pathways in higher organisms such as humans."
The project's three goals are to measure components of the signal transduction pathway in individual yeast cells; create computer simulations of the behavior of the pathway and the cell; and combine the experimental and computational approaches to build models that would allow a user to predict the behavior of cells in response to defined changes.
"Cells and organisms are oftentimes described as small factories, nanomachines or circuits and information processing devices," Endy said. "While valuable, each of these metaphors implies that living systems are created through a determined process whose operations and elements we fully understand. The work of the center provides an opportunity to better define their ultimate utility and limitations, and to learn from biology what else might be relevant."
A version of this article appeared in MIT Tech Talk on August 14, 2002.