Now housed in a corner of the medical school (a new building for the department funded by Microsoft chairman Bill Gates is going up next door), the department is attacking the problem on many fronts. Computer programmers and geneticists are collaborating to analyze complex networks of genes and proteins. Research on model organisms is closely integrated with investigations of human genetics. Four HHMI investigators in addition to Green—David Baker, Stanley Fields, Richard Palmiter, and newly appointed investigator Evan Eichler—are working on problems ranging from genomic evolution to protein folding to development of the mammalian nervous system.

Green collaborates on several projects in the department and offers advice on many more. He also finds time to pursue topics that strike his interest. Several years ago, for instance, he noticed that data from the sequencing of expressed DNA fragments implied that the number of human genes was much lower than expected. When he and a colleague expressed the idea in print, their paper was met with howls of disbelief, but their number now appears to be more or less right.

Green also takes the time to think about the overall trajectory of biological research: the slow but steady progression from understanding biological systems to prediction to control. Some of his colleagues question the extent to which such an ambitious agenda can be achieved. “I’m a skeptic about systems biology, at least in its most grandiose form—the idea that we will build quantitative models of cells comparable to the models used to build bridges,” says Green’s colleague Olson. The cascading effects of chance and the sheer complexity of biological systems are likely to place limits on the extent to which the outcomes of biological systems can be predicted and controlled, Olson says.

He believes biological research eventually will move toward more practical ends, in which the focus will be on specific interventions. "I think bioengineering is going to look very different than engineering as we've known it in the physical world," he says. For instances, in addition to searching for a drug that might cure diabetes, biologists might build sensors that carefully monitor blood insulin levels, or they may engineer artifical cells that deliver precisely calibrated amounts of insulin.

Green is more optimistic than Olson that a deeper understanding of the cell will lead to direct interventions in biological processes. “The main issue is how quantitative we’re going to be able to get,” he says. “Most people will accept the idea that we will know qualitatively how things are interacting with each other. But what you really want is a quantitative result, so that you can change the levels of one component and predict how it will affect the system.”

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