BY KRISTA CONGER

Russ Altman began his lecture in the Unsolved Mysteries in Medical Research series with a tough question and a snappy answer. "Why can't computers simulate a living cell? That's easy -- because it's too hard. Thank you."

When the chuckles died down, Altman, MD, PhD, associate professor of medical informatics at Stanford, began the real work of explaining why computers can't yet replace living organisms in medical research.

During his April 17 lecture, Altman broke down the question into steps, each with its own problems and potential solutions. But first he issued a warning.

"Most of us are not trained to do this," Altman said of the challenge of reassembling millions of bits of experimental data into a cohesive model system that could, for instance, predict the effects of untested medication on humans. "We're taught to be reductionists, but usually the more simple a model is, the more likely it is to be wrong."

Altman said the first step in the process is identifying the individual components -- such as proteins and pools of molecules -- that affect cellular functions. Then the interactions between the components and pools must be identified and the results represented in a map format. Finally, it's necessary to translate the relationships represented by the map into equations, which can then be used to analyze input data -- such as the presence of a new drug -- and predict cellular responses.

The Human Genome Project, a national effort to identify and characterize all human genetic material, has helped to identify many of the players. But Altman emphasized that alternative splicing and multifunctional proteins could inflate the effective number of components beyond the 35,000 genes that have been identified. He also pointed out that differences in the three-dimensional distribution of molecules within a cell can affect their function.

Identifying interactions between the components is extremely complicated, Altman said. Current methods of calculating interactions between isolated components, such as the Michaelis-Menton equation used in enzyme kinetics, are not accurate when applied to living systems, he said. And it's difficult to precisely quantify interactions between feedback pathways.

"As soon as you draw both a plus and a minus on the same page of a model, you've bought yourself a quantitative problem," Altman said. These quantitative tussles can hamstring any effort to generate accurate equations.

Finally, it's not clear whether the computational power exists to crunch the numbers of the billions of interactions that occur in a cell, and whether enough experimental data exists to support this goal, Altman said.

"We may have to give up our desire to have a computer system that permits 'one-stop shopping' and -- at least for the short term -- scale back our expectations," Altman said.